Modern vision-language models have transformed how we process visual data, yet they often fall short when it comes to fine-grained localization and dense feature extraction. Many traditional models focus on high-level semantic understanding and zero-shot classification but struggle with detailed spatial reasoning. These limitations can impact applications that require precise localization, such as document analysis or object segmentation.Moreover, models that primarily rely on contrastive loss sometimes do not perform well in tasks needing refined spatial cues. There is also a challenge in supporting multiple languages and ensuring fair representation across diverse cultural contexts. Addressing these issues is essential to create models that are both technically robust and socially responsible.Google DeepMind Research Releases SigLIP2: a family of new multilingual vision-language encoders with Improved Semantic Understanding, Localization, and Dense Features. SigLIP 2 extends the original imagetext training objective by blending captioning-based pretraining with self-supervised approaches like self-distillation and masked prediction. This combination is designed to enhance both the overall semantic representation and the models ability to capture local, detailed features. The training process also includes a mix of multilingual dataprimarily English with a smaller proportion of non-English contentand employs de-biasing methods to ensure fairer outcomes.Technical Details and BenefitsAt its core, SigLIP 2 is built on the foundation of Vision Transformers, ensuring backward compatibility with earlier versions. This means that users can replace the model weights without the need to overhaul their entire system. The model uses a sigmoid loss instead of the traditional contrastive loss, which allows for a more balanced learning of both global and local features.In addition to the sigmoid loss, SigLIP 2 incorporates a decoder-based loss. This helps in learning tasks like image captioning and region-specific localization, ultimately leading to better performance in dense prediction tasks. The models design also includes a MAP head for pooling features from both the image and text components, ensuring that the learned representations are both robust and detailed. Another notable technical aspect is the introduction of the NaFlex variant. NaFlex supports native aspect ratios by processing images at various resolutions using a single checkpoint. This method helps maintain the integrity of the images spatial information, which is particularly important in tasks where the aspect ratio can influence the outcome, such as in document understanding or OCR.Furthermore, the use of self-distillation and masked prediction improves the quality of the local features. By training the model to predict masked patches, it learns to focus on subtle details that are crucial for tasks like segmentation and depth estimation. This careful design allows even smaller models to achieve improved performance through enhanced distillation techniques.Results, Data Insights, and EvaluationThe experimental results in the paper support the technical choices made in SigLIP 2. Across several benchmarksincluding zero-shot classification tests on ImageNet, ObjectNet, and ImageNet ReaLthe model shows consistent improvements over earlier models. The benefits are particularly clear in tasks that demand detailed spatial understanding.For multilingual imagetext retrieval tasks, such as those evaluated on Crossmodal-3600, SigLIP 2 performs competitively with models designed exclusively for multilingual data. At the same time, it maintains strong performance on English-centered tasks. This balance is achieved through careful data curation and training methods that emphasize both semantic richness and localization precision. In dense prediction tasks, such as semantic segmentation, depth estimation, and surface normal prediction, the models advantages are again evident. When tested on open-vocabulary segmentation frameworks like Cat-Seg, SigLIP 2 consistently reports higher mean Intersection-over-Union (mIoU) scores compared to its predecessors and other open-weight models. These results are a testament to the models ability to capture intricate details in images.Localisation tasks also benefit from the models refined training. For instance, in referring expression comprehension and open-vocabulary detection, the performance improvements are clear. The model not only aligns text and image features more effectively but also demonstrates a reduced tendency toward biased associations. In evaluations of representation bias, SigLIP 2 shows a marked decrease in unfair object-to-gender associations, underscoring the importance of the de-biasing techniques used during training. The research presents a range of comparative tables and figures that detail these improvements. The data suggest that as the model size increases, the benefits of these training enhancements become even more pronounced. Across various configurations and resolutions, the models performance remains robust, making it a strong candidate for both research and practical applications.ConclusionIn conclusion, SigLIP 2 represents a measured and well-engineered step forward in the development of vision-language models. It integrates established techniques with thoughtful innovations to address known challenges such as fine-grained localization, dense prediction, and multilingual support. By moving away from solely contrastive losses and incorporating additional self-supervised objectives, SigLIP 2 achieves a more balanced representation of visual data. Its careful handling of native aspect ratios through the NaFlex variant further improves its applicability in real-world scenarios where image integrity matters.The inclusion of multilingual data and de-biasing measures reflects an awareness of the diverse contexts in which these models operate. This approach not only improves performance across various benchmarks but also ensures that the model is better aligned with broader ethical considerations in AI. Overall, the release of SigLIP 2 is a promising development for the vision-language research community. It offers a versatile, backward-compatible framework that can be readily integrated into existing systems. The models ability to deliver reliable performance across a range of taskswhile maintaining fairness and inclusivitysets a thoughtful benchmark for future research in this field.Check outthePaper, GitHub Page and Models on Hugging Face.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Aswin AkAswin AK is a consulting intern at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, bringing a strong academic background and hands-on experience in solving real-life cross-domain challenges.Aswin Akhttps://www.marktechpost.com/author/aswinak/Boosting AI Math Skills: How Counterexample-Driven Reasoning is Transforming Large Language ModelsAswin Akhttps://www.marktechpost.com/author/aswinak/Google DeepMind Releases PaliGemma 2 Mix: New Instruction Vision Language Models Fine-Tuned on a Mix of Vision Language TasksAswin Akhttps://www.marktechpost.com/author/aswinak/KGGen: Advancing Knowledge Graph Extraction with Language Models and Clustering TechniquesAswin Akhttps://www.marktechpost.com/author/aswinak/ViLa-MIL: Enhancing Whole Slide Image Classification with Dual-Scale Vision-Language Multiple Instance Learning

Organizations face significant challenges when deploying LLMs in todays technology landscape. The primary issues include managing the enormous computational demands required to process high volumes of data, achieving low latency, and ensuring optimal balance between CPU-intensive tasks, such as scheduling and memory allocation, and GPU-intensive computations. Repeatedly processing similar inputs further compounds the inefficiencies in many systems, leading to redundant computations that slow down overall performance. Also, generating structured outputs like JSON or XML in real-time introduces further delays, making it difficult for applications to deliver fast, reliable, cost-effective performance at scale.SGLang is an open-source inference engine designed by the SGLang team to address these challenges. It optimizes CPU and GPU resources during inference, achieving significantly higher throughput than many competitive solutions. Its design utilizes an innovative approach that reduces redundant computations and enhances overall efficiency, thereby enabling organizations to manage better the complexities associated with LLM deployment.RadixAttention is central to SGLang, which reuses shared prompt prefixes across multiple requests. This approach effectively minimizes the repeated processing of similar input sequences, improving throughput. The technique is advantageous in conversational interfaces or retrieval-augmented generation applications, where similar prompts are frequently processed. By eliminating redundant computations, the system ensures that resources are used more efficiently, contributing to faster processing times and more responsive applications.Another critical feature of SGLang is its zero-overhead batch scheduler. Earlier inference systems often suffer from significant CPU overhead due to tasks like batch scheduling, memory allocation, and prompt preprocessing. In many cases, these operations result in idle periods for the GPU, which in turn hampers overall performance. SGLang addresses this bottleneck by overlapping CPU scheduling with ongoing GPU computations. The scheduler keeps the GPUs continuously engaged by running one batch ahead and preparing all necessary metadata for the next batch. Profiling has shown that this design reduces idle time and achieves measurable speed improvements, especially in configurations that involve smaller models and extensive tensor parallelism.SGLang also incorporates a cache-aware load balancer that departs from conventional load balancing methods such as round-robin scheduling. Traditional techniques often ignore the state of the key-value (KV) cache, leading to inefficient resource use. In contrast, SGLangs load balancer predicts the cache hit rates of different workers and directs incoming requests to those with the highest likelihood of a cache hit. This targeted routing increases throughput and enhances cache utilization. The mechanism relies on an approximate radix tree that reflects the current cache state on each worker, and it lazily updates this tree to impose minimal overhead. The load balancer, implemented in Rust for high concurrency, is especially well suited for distributed, multi-node environments.In addition to these features, SGLang supports data parallelism attention, a strategy particularly tailored for DeepSeek models. While many modern models use tensor parallelism, which can lead to duplicated KV cache storage when scaling across multiple GPUs, SGLang employs a different method for models utilizing multi-head latent attention. In this approach, individual data parallel workers independently handle various batches, such as prefill, decode, or idle. The attention-processed data is then aggregated across workers before passing through subsequent layers, such as a mixture-of-experts layer, and later redistributed.SGLang also excels in the efficient generation of structured outputs. Many inference systems struggle with the real-time decoding of formats like JSON, which can be a critical requirement in many applications. SGLang addresses this by integrating a specialized grammar backend known as xgrammar. This integration streamlines the decoding process, allowing the system to generate structured outputs up to ten times faster than other open-source alternatives. This capability is especially valuable when rapidly producing machine-readable data, essential for downstream processing or interactive applications.Several high-profile companies have recognized SGLangs practical benefits. For example, ByteDance channels a large portion of its internal NLP pipelines through this engine, processing petabytes of data daily. Similarly, xai has reported substantial cost savings by leveraging optimized scheduling and effective cache management, resulting in a notable reduction in serving expenses. These real-world applications highlight SGLangs ability to operate efficiently at scale, delivering performance improvements and cost benefits.SGLang is released under the Apache 2.0 open-source license and is accessible for academic research and commercial applications. Its compatibility with OpenAI standards and the provision of a Python API allows developers to integrate it seamlessly into existing workflows. The engine supports many models, including popular ones such as Llama, Mistral, Gemma, Qwen, DeepSeek, Phi, and Granite. It is designed to work across various hardware platforms, including NVIDIA and AMD GPUs, and integrates advanced quantization techniques like FP8 and INT4. Future enhancements will include FP6 weight and FP8 activation quantization, faster startup times, and cross-cloud load balancing.Several Key Takeaways from the research on SGLang include:SGLang addresses critical challenges in deploying large language models by optimizing the balance between CPU and GPU tasks.RadixAttention minimizes redundant computations, improving throughput in conversational and retrieval scenarios.A zero-overhead batch scheduler overlaps CPU scheduling with GPU operations to ensure continuous processing and reduce idle time.A cache-aware load balancer efficiently predicts cache hit rates and routes requests, boosting overall performance and cache utilization.Data parallelism attention reduces memory overhead and enhances decoding throughput for multi-head latent attention models.The integration of xgrammar allows for the rapid generation of structured outputs, significantly improving processing speed for formats like JSON.SGLangs practical benefits are demonstrated by its adoption in large-scale production environments, which contribute to substantial cost savings and performance improvements.Check outtheGitHub Repo, Documentation and Technical Details.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/Stanford Researchers Developed POPPER: An Agentic AI Framework that Automates Hypothesis Validation with Rigorous Statistical Control, Reducing Errors and Accelerating Scientific Discovery by 10xAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Steps to Build an Interactive Text-to-Image Generation Application using Gradio and Hugging Faces DiffusersAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Microsoft Researchers Present Magma: A Multimodal AI Model Integrating Vision, Language, and Action for Advanced Robotics, UI Navigation, and Intelligent Decision-MakingAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Moonshot AI Research Introduce Mixture of Block Attention (MoBA): A New AI Approach that Applies the Principles of Mixture of Experts (MoE) to the Attention Mechanism

While LLMs have shown remarkable advancements in general-purpose applications, their development for specialized fields like medicine remains limited. The complexity of medical knowledge and the scarcity of high-quality, domain-specific data make creating highly efficient medical LLMs challenging. Although models like GPT-4 and DeepseekR1 have demonstrated impressive capabilities across industries, their adaptation to the medical domain is hindered by the intricate nature of medical terminology, diverse disciplines, and constantly evolving literature. Unlike general applications, medical AI must interpret highly technical language and provide precise, contextually relevant responses, which traditional LLMs struggle to achieve.One major obstacle in building effective medical LLMs is the limited accessibility of high-quality training data, which is restricted due to privacy concerns and regulatory barriers. Medical datasets consist of structured and unstructured information, including clinical notes, textbooks, and research articles, making comprehensive model training difficult. While approaches like fine-tuning general LLMs on medical datasets and applying transfer learning have been explored, these methods often fail to grasp the depth of medical knowledge fully. As a result, such models may perform well on specific tasks but lack the nuanced understanding necessary for complex medical inquiries, highlighting the need for more refined training strategies.Researchers at Baichuan Inc. introduced Baichuan-M1, a specialized large language model series designed specifically for medical applications. Unlike traditional models that refine existing architectures through additional pretraining or post-training, Baichuan-M1 is built from scratch with a strong focus on medical expertise. Trained on 20 trillion tokens, including both general and medical-specific data, the model balances broad language understanding with domain-specific precision. It excels in general tasks like coding and mathematics and in medical applications such as diagnostics and treatment recommendations. With an optimized Transformer architecture, Baichuan-M1 sets a new benchmark for AI-driven advancements in healthcare.The model architecture follows Llama and similar frameworks, incorporating pre-norm RMSNorm, SwishGlu in the FFN layer, and rotary position embeddings. The study integrates global and sliding window attention to optimize inference efficiency, increasing the head dimension to 256 for global layers. Additionally, temporal short convolutions on key-value attention enhance in-context learning. The model employs a hybrid tokenizer for medical and general text, a curriculum-based training strategy with progressive data complexity, and adaptive gradient clipping for stability. Supervised fine-tuning refines general reasoning and medical-specific tasks, ensuring robust language understanding, medical reasoning, and long-document handling capabilities while maintaining inference efficiency.Using various benchmarks, baichuan-M1-14B-Bases code and mathematical abilities were evaluated against the Qwen2.5 series models. Code generation performance was tested with the EvalPlus framework and Bigcodebench, while mathematical proficiency was assessed using MATH and CMATH datasets. Although the 14B-Instruct variant still lags behind proprietary models like Claude-3.5-Sonnet and GPT-4o, the gap has narrowed significantly. The results demonstrate that Baichuan-M1-14B-Base performs competitively in certain tasks, showcasing its code generation and mathematical reasoning strengths compared to other advanced models.In conclusion, Traditional methods for adapting LLMs to specialized fields often involve fine-tuning existing models. However, experiments suggest that further training on pre-existing models can hinder domain-specific improvements without sacrificing general performance. In the medical domain, fine-tuning general models with domain-specific data may be less effective than training from scratch. Baichuan-M1 was developed with this approach, using 20 trillion tokens to enhance medical expertise while maintaining general capabilities. Open-sourcing Baichuan-M1-14B allows further research, though challenges remain in rare disease diagnosis and real-world applications. Its continued evolution could significantly advance AI-driven medical decision-making.Check outBaichuan-M1-14B-Instruct.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/xAI Releases Grok 3 Beta: A Super Advanced AI Model Blending Strong Reasoning with Extensive Pretraining KnowledgeSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Learning Intuitive Physics: Advancing AI Through Predictive Representation ModelsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Microsoft AI Releases OmniParser V2: An AI Tool that Turns Any LLM into a Computer Use AgentSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Enhancing Diffusion Models: The Role of Sparsity and Regularization in Efficient Generative AI

Large Language models (LLMs) operate by predicting the next token based on input data, yet their performance suggests they process information beyond mere token-level predictions. This raises questions about whether LLMs engage in implicit planning before generating complete responses. Understanding this phenomenon can lead to more transparent AI systems, improving efficiency and making output generation more predictable.One challenge in working with LLMs is predicting how they will structure responses. These models generate text sequentially, making controlling the overall response length, reasoning depth, and factual accuracy challenging. The lack of explicit planning mechanisms means that although LLMs generate human-like responses, their internal decision-making remains opaque. As a result, users often rely on prompt engineering to guide outputs, but this method lacks precision and does not provide insight into the models inherent response formulation.Existing techniques to refine LLM outputs include reinforcement learning, fine-tuning, and structured prompting. Researchers have also experimented with decision trees and external logic-based frameworks to impose structure. However, these methods do not fully capture how LLMs internally process information.The Shanghai Artificial Intelligence Laboratory research team has introduced a novel approach by analyzing hidden representations to uncover latent response-planning behaviors. Their findings suggest that LLMs encode key attributes of their responses even before the first token is generated. The research team examined their hidden representations and investigated whether LLMs engage in emergent response planning. They introduced simple probing models trained on prompt embeddings to predict upcoming response attributes. The study categorized response planning into three main areas: structural attributes, such as response length and reasoning steps, content attributes including character choices in story-writing tasks, and behavioral attributes, such as confidence in multiple-choice answers. By analyzing patterns in hidden layers, the researchers found that these planning abilities scale with model size and evolve throughout the generation process.To quantify response planning, the researchers conducted a series of probing experiments. They trained models to predict response attributes using hidden state representations extracted before output generation. The experiments showed that probes could accurately predict upcoming text characteristics. The findings indicated that LLMs encode response attributes in their prompt representations, with planning abilities peaking at the beginning and end of responses. The study further demonstrated that models of different sizes share similar planning behaviors, with larger models exhibiting more pronounced predictive capabilities.The experiments revealed substantial differences in planning capabilities between base and fine-tuned models. Fine-tuned models exhibited better prediction accuracy in structural and behavioral attributes, confirming that planning behaviors are reinforced through optimization. For instance, response length prediction showed high correlation coefficients across models, with Spearmans correlation reaching 0.84 in some cases. Similarly, reasoning step predictions exhibited strong alignment with ground-truth values. Classification tasks such as character choice in story writing and multiple-choice answer selection performed significantly above random baselines, further supporting the notion that LLMs internally encode elements of response planning.Larger models demonstrated superior planning abilities across all attributes. Within the LLaMA and Qwen model families, planning accuracy improved consistently with increased parameter count. The study found that LLaMA-3-70B and Qwen2.5-72B-Instruct exhibited the highest prediction performance, while smaller models like Qwen2.5-1.5B struggled to encode long-term response structures effectively. Further, layer-wise probing experiments indicated that structural attributes emerged prominently in mid-layers, while content attributes became more pronounced in later layers. Behavioral attributes, such as answer confidence and factual consistency, remained relatively stable across different model depths.These findings highlight a fundamental aspect of LLM behavior: they do not merely predict the next token but plan broader attributes of their responses before generating text. This emergent response planning ability has implications for improving model transparency and control. Understanding these internal processes can help refine AI models, leading to better predictability and reduced reliance on post-generation corrections. Future research may explore integrating explicit planning modules within LLM architectures to enhance response coherence and user-directed customization.Check outthePaper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Shortest Majority Vote: An Improved Parallel Scaling Method for Enhancing Test-Time Performance in Large Language ModelsNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Diverse Inference and Verification: Enhancing AI Reasoning for Advanced Mathematical and Logical Problem-SolvingNikhilhttps://www.marktechpost.com/author/nikhil0980/Stanford Researchers Introduced a Multi-Agent Reinforcement Learning Framework for Effective Social Deduction in AI CommunicationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper from IBM and MIT Introduces SOLOMON: A Neuro-Inspired Reasoning Network for Enhancing LLM Adaptability in Semiconductor Layout Design

Large language models (LLMs) use extensive computational resources to process and generate human-like text. One emerging technique to enhance reasoning capabilities in LLMs is test-time scaling, which dynamically allocates computational resources during inference. This approach aims to improve the accuracy of responses by refining the models reasoning process. As models like OpenAIs o1 series introduced test-time scaling, researchers sought to understand whether longer reasoning chains led to improved performance or if alternative strategies could yield better results.Scaling reasoning in AI models poses a significant challenge, especially in cases where extended chains of thought do not necessarily translate to better outcomes. The assumption that increasing the length of responses enhances accuracy is being questioned by researchers, who have found that longer explanations can introduce inconsistencies. Errors accumulate over extended reasoning chains, and models often make unnecessary self-revisions, leading to performance degradation rather than improvement. If test-time scaling is to be an effective solution, it must balance reasoning depth with accuracy, ensuring that computational resources are used efficiently without diminishing the models effectiveness.Current approaches to test-time scaling primarily fall into sequential and parallel categories. Sequential scaling extends the chain-of-thought (CoT) during inference, expecting that more extended reasoning will lead to improved accuracy. However, studies on models like QwQ, Deepseek-R1 (R1), and LIMO indicate that extending CoTs does not consistently yield better results. These models frequently use self-revision, introducing redundant computations that degrade performance. In contrast, parallel scaling generates multiple solutions simultaneously and selects the best one based on a predetermined criterion. Comparative analyses suggest that parallel scaling is more effective in maintaining accuracy and efficiency.Researchers from Fudan University and the Shanghai AI Laboratory introduced an innovative method called Shortest Majority Vote to address the limitations of sequential scaling. This method optimizes test-time scaling by leveraging parallel computation while factoring in solution length. The primary insight behind this approach is that shorter solutions tend to be more accurate than longer ones, as they contain fewer unnecessary self-revisions. By incorporating solution length into the majority voting process, this method enhances models performance by prioritizing frequent and concise answers.The proposed method modifies traditional majority voting by considering the number and length of solutions. Conventional majority voting selects the most frequently occurring answer among generated solutions, whereas Shortest Majority Vote assigns higher priority to answers that appear often but are also shorter. The reasoning behind this approach is that longer solutions tend to introduce more errors due to excessive self-revisions. Researchers found that QwQ, R1, and LIMO generate increasingly longer responses when prompted to refine their solutions, often leading to lower accuracy. The proposed method aims to filter out unnecessary extensions and prioritize more precise answers by integrating length as a criterion.Experimental evaluations demonstrated that Shortest Majority Vote method significantly outperformed traditional majority voting across multiple benchmarks. On the AIME dataset, models incorporating this technique showed an increase in accuracy compared to existing test-time scaling approaches. For instance, accuracy improvements were observed in R1-Distill-32b, which reached 72.88% compared to conventional methods. Similarly, QwQ and LIMO also exhibited enhanced performance, particularly in cases where extended reasoning chains previously led to inconsistencies. These findings suggest that the assumption that longer solutions always yield better results is flawed. Instead, a structured and efficient approach that prioritizes conciseness can lead to superior performance.The results also revealed that sequential scaling suffers from diminishing returns. While initial revisions may contribute to improved responses, excessive revisions often introduce errors rather than correcting them. In particular, models like QwQ and R1-Distill-1.5b tended to change correct answers into incorrect ones rather than improving accuracy. This phenomenon further highlights the limitations of sequential scaling, reinforcing the argument that a more structured approach, such as Shortest Majority Vote, is necessary for optimizing test-time scaling.The research underscores the need to rethink how test-time scaling is applied in large language models. Rather than assuming that extending reasoning chains leads to better accuracy, the findings demonstrate that prioritizing concise, high-quality solutions through parallel scaling is a more effective strategy. The introduction of Shortest Majority Vote provides a practical and empirically validated improvement over existing methods, offering a refined approach to optimizing computational efficiency in LLMs. By focusing on structured reasoning rather than excessive self-revision, this method paves the way for more reliable and accurate AI-driven decision-making.Check outthePaper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Diverse Inference and Verification: Enhancing AI Reasoning for Advanced Mathematical and Logical Problem-SolvingNikhilhttps://www.marktechpost.com/author/nikhil0980/Stanford Researchers Introduced a Multi-Agent Reinforcement Learning Framework for Effective Social Deduction in AI CommunicationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper from IBM and MIT Introduces SOLOMON: A Neuro-Inspired Reasoning Network for Enhancing LLM Adaptability in Semiconductor Layout DesignNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper from Apple Introduces a Distillation Scaling Law: A Compute-Optimal Approach for Training Efficient Language Models

Mathematical Large Language Models (LLMs) have demonstrated strong problem-solving capabilities, but their reasoning ability is often constrained by pattern recognition rather than true conceptual understanding. Current models are heavily based on exposure to similar proofs as part of their training, confining their extrapolation to new mathematical problems. This constraint restricts LLMs from engaging in advanced mathematical reasoning, especially in problems requiring the differentiation between closely related mathematical concepts. An advanced reasoning strategy commonly lacking in LLMs is the proof by counterexample, a central method of disproving false mathematical assertions. The absence of sufficient generation and employment of counterexamples hinders LLMs in conceptual reasoning of advanced mathematics, hence diminishing their reliability in formal theorem verification and mathematical exploration.Previous attempts to improve mathematical reasoning in LLMs have been categorized into two general approaches. The first approach, synthetic problem generation, trains LLMs on vast datasets generated from seed math problems. For example, WizardMath uses GPT-3.5 to generate problems of varying levels of difficulty. The second approach, formal theorem proving, trains models to work with proof systems such as Lean 4, as in Draft-Sketch-Prove and Lean-STaR, that assist LLMs in structured theorem proving. Although these approaches have enhanced problem-solving ability, they have severe limitations. Synthetic question generation generates memorization and not genuine understanding, leaving models vulnerable to failure in the face of novel problems. Formal theorem-proving techniques, on the other hand, are limited by being grounded in structured mathematical languages that limit their application to various mathematical contexts. These limitations underscore the need for an alternative paradigma paradigm that is concerned with conceptual understanding as opposed to pattern recognition.To address these limitations, a counterexample-driven mathematical reasoning benchmark is introduced, known as COUNTERMATH. The benchmark is specifically constructed to assess and enhance LLMs use of counterexamples in proof. The innovations encompass a high-quality benchmark, data engineering process, and thorough model assessments. COUNTERMATH is comprised of 1,216 mathematical assertions, each of which needs a counterexample to disprove. The problems are hand-curated from university textbooks and extensively validated by experts. To enhance LLMs counterexample-based reasoning, an automated data-gathering process is implemented, filtering and refining mathematical proof data to obtain counterexample-based reasoning examples. The efficacy of state-of-the-art mathematical LLMs, such as OpenAIs o1 model and fine-tuned open-source variants, is rigorously examined on COUNTERMATH. By diverting the focus toward example-based reasoning from exclusive theorem-proving, this method initiates a novel and under-explored route to training mathematical LLMs.COUNTERMATH is constructed based on four core mathematical disciplines: Algebra, Topology, Real Analysis, and Functional Analysis. The data is built in a multi-step process. First, mathematical statements are gathered from textbooks and converted to structured data via OCR. Mathematicians then review and annotate each problem for logical consistency and accuracy. Professional translations are performed as the original data is in Chinese, followed by additional checks. An in-task data engineering framework is also presented to automatically retrieve training data for counterexample-based reasoning. GPT-4o filtering and refinement techniques are applied in this framework to extract relevant proofs from outside sources such as ProofNet and NaturalProof. Refinement is done to ensure each proof explicitly illustrates counterexamples so that LLMs can learn counterexample-based reasoning more effectively.The evaluation of state-of-the-art mathematical LLMs on COUNTERMATH reveals significant gaps in counterexample-driven reasoning. The majority of the models do not pass judgment on whether a statement is true or false using counterexamples, reflecting a profound conceptual weakness. Performance is also mixed across mathematical areas, with algebra and functional analysis performing better, and topology and real analysis still being very challenging due to their abstract nature. Open-source models perform worse than proprietary models, with only a few having moderate conceptual reasoning. Fine-tuning with counterexample-based data, however, significantly enhances performance, with better judgment accuracy and example-based reasoning. A fine-tuned model, with only 1,025 counterexample-based training samples, performs significantly better than its baseline versions and has strong generalization to out-of-distribution mathematical tests. A detailed evaluation reported in Table 1 below shows performance comparisons based on F1 scores and reasoning consistency metrics. Qwen2.5-Math-72B-Instruct performs best (41.8 F1) among open-source models but falls behind proprietary models like GPT-4o (59.0 F1) and OpenAI o1 (60.1 F1). Fine-tuning leads to significant gains, with Qwen2.5-Math-7B-Instruct-SFT + Hint prompt achieving 41.1 F1, affirming the effectiveness of counterexample-based training. This proposed method presents COUNTERMATH, a counterexample-based reasoning benchmark designed to improve LLMs conceptual mathematical abilities. The utilization of well-curated problem sets and an automated data refinement process demonstrates that existing LLMs are not proficient in deep mathematical reasoning but can be greatly enhanced with counterexample-based training. These results imply that future AI research needs to be focused on enhancing conceptual understanding and not exposure-based learning. Counterexample reasoning is not only essential in mathematics but also in logic, scientific investigation, and formal verification, and this method can thus be extended to a broad variety of AI-driven analytical tasks.Check outthePaper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Aswin AkAswin AK is a consulting intern at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, bringing a strong academic background and hands-on experience in solving real-life cross-domain challenges.Aswin Akhttps://www.marktechpost.com/author/aswinak/Google DeepMind Releases PaliGemma 2 Mix: New Instruction Vision Language Models Fine-Tuned on a Mix of Vision Language TasksAswin Akhttps://www.marktechpost.com/author/aswinak/KGGen: Advancing Knowledge Graph Extraction with Language Models and Clustering TechniquesAswin Akhttps://www.marktechpost.com/author/aswinak/ViLa-MIL: Enhancing Whole Slide Image Classification with Dual-Scale Vision-Language Multiple Instance LearningAswin Akhttps://www.marktechpost.com/author/aswinak/Mistral AI Introduces Mistral Saba: A New Regional Language Model Designed to Excel in Arabic and South Indian-Origin Languages such as Tamil

Hypothesis validation is fundamental in scientific discovery, decision-making, and information acquisition. Whether in biology, economics, or policymaking, researchers rely on testing hypotheses to guide their conclusions. Traditionally, this process involves designing experiments, collecting data, and analyzing results to determine the validity of a hypothesis. However, the volume of generated hypotheses has increased dramatically with the advent of LLMs. While these AI-driven hypotheses offer novel insights, their plausibility varies widely, making manual validation impractical. Thus, automation in hypothesis validation has become an essential challenge in ensuring that only scientifically rigorous hypotheses guide future research.The main challenge in hypothesis validation is that many real-world hypotheses are abstract and not directly measurable. For instance, stating that a specific gene causes a disease is too broad and needs to be translated into testable implications. The rise of LLMs has exacerbated this issue, as these models generate hypotheses at an unprecedented scale, many of which may be inaccurate or misleading. Existing validation methods struggle to keep pace, making it difficult to determine which hypotheses are worth further investigation. Also, statistical rigor is often compromised, leading to false verifications that can misdirect research and policy efforts.Traditional methods of hypothesis validation include statistical testing frameworks such as p-value-based hypothesis testing and Fishers combined test. However, these approaches rely on human intervention to design falsification experiments and interpret results. Some automated approaches exist, but they often lack mechanisms for controlling Type-I errors (false positives) and ensuring that conclusions are statistically reliable. Many AI-driven validation tools do not systematically challenge hypotheses through rigorous falsification, increasing the risk of misleading findings. As a result, a scalable and statistically sound solution is needed to automate the hypothesis validation process effectively.Researchers from Stanford University and Harvard University introduced POPPER, an agentic framework that automates the process of hypothesis validation by integrating rigorous statistical principles with LLM-based agents. The framework systematically applies Karl Poppers principle of falsification, which emphasizes disproving rather than proving hypotheses. POPPER employs two specialized AI-driven agents:The Experiment Design Agent which formulates falsification experimentsThe Experiment Execution Agent which implements themEach hypothesis is divided into specific, testable sub-hypotheses and subjected to falsification experiments. POPPER ensures that only well-supported hypotheses are advanced by continuously refining the validation process and aggregating evidence. Unlike traditional methods, POPPER dynamically adapts its approach based on prior results, significantly improving efficiency while maintaining statistical integrity.POPPER functions through an iterative process in which falsification experiments sequentially test hypotheses. The Experiment Design Agent generates these experiments by identifying the measurable implications of a given hypothesis. The Experiment Execution Agent then carries out the proposed experiments using statistical methods, simulations, and real-world data collection. Key to POPPERs methodology is its ability to strictly control Type-I error rates, ensuring that false positives are minimized. Unlike conventional approaches that treat p-values in isolation, POPPER introduces a sequential testing framework in which individual p-values are converted into e-values, a statistical measure allowing continuous evidence accumulation while maintaining error control. This adaptive approach enables the system to refine its hypotheses dynamically, reducing the chances of reaching incorrect conclusions. The frameworks flexibility allows it to work with existing datasets, conduct new simulations, or interact with live data sources, making it highly versatile across disciplines.POPPER was evaluated across six domains: biology, sociology, and economics. The system was tested against 86 validated hypotheses, with results showing Type-I error rates below 0.10 across all datasets. POPPER demonstrated significant improvements in statistical power compared to existing validation methods, outperforming standard techniques such as Fishers combined test and likelihood ratio models. In one study focusing on biological hypotheses related to Interleukin-2 (IL-2), POPPERs iterative testing mechanism improved validation power by 3.17 times compared to alternative methods. Also, an expert evaluation involving nine PhD-level computational biologists and biostatisticians found that POPPERs hypothesis validation accuracy was comparable to that of human researchers but was completed in one-tenth the time. By leveraging its adaptive testing framework, POPPER reduced the time required for complex hypothesis validation by 10, making it significantly more scalable and efficient.Several Key Takeaways from the Research include:POPPER provides a scalable, AI-driven solution that automates the falsification of hypotheses, reducing manual workload and improving efficiency.The framework maintains strict Type-I error control, ensuring that false positives remain below 0.10, critical for scientific integrity.Compared to human researchers, POPPER completes hypothesis validation 10 times faster, significantly improving the speed of scientific discovery.Unlike traditional p-value testing, using e-values allows accumulating experimental evidence while dynamically refining hypothesis validation.Tested across six scientific fields, including biology, sociology, and economics, demonstrating broad applicability.Evaluated by nine PhD-level scientists, POPPERs accuracy matched human performance while dramatically reducing time spent on validation.Improved statistical power by 3.17 times over traditional hypothesis validation methods, ensuring more reliable conclusions.POPPER integrates Large Language Models to dynamically generate and refine falsification experiments, making it adaptable to evolving research needs.Check outthePaper and GitHub Page.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/Steps to Build an Interactive Text-to-Image Generation Application using Gradio and Hugging Faces DiffusersAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Microsoft Researchers Present Magma: A Multimodal AI Model Integrating Vision, Language, and Action for Advanced Robotics, UI Navigation, and Intelligent Decision-MakingAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Moonshot AI Research Introduce Mixture of Block Attention (MoBA): A New AI Approach that Applies the Principles of Mixture of Experts (MoE) to the Attention MechanismAsif Razzaqhttps://www.marktechpost.com/author/6flvq/DeepSeek AI Introduces NSA: A Hardware-Aligned and Natively Trainable Sparse Attention Mechanism for Ultra-Fast Long-Context Training and Inference

Modern AI systems have made significant strides, yet many still struggle with complex reasoning tasks. Issues such as inconsistent problem-solving, limited chain-of-thought capabilities, and occasional factual inaccuracies remain. These challenges hinder practical applications in research and software development, where nuanced understanding and precision are crucial. The drive to overcome these limitations has prompted a reexamination of how AI models are built and trained, with a focus on improving transparency and reliabilitxAIs recent release of the Grok 3 Beta marks a thoughtful step forward in AI development. In their announcement, the company outlines how this new model builds on its predecessors with a refined approach to reasoning and problem-solving. Grok 3 is trained on the companys Colossus supercluster using substantially more compute than previous iterations. This enhanced training has yielded improvements in areas such as mathematics, coding, and instruction-following, while also enabling the model to consider multiple solution paths before arriving at a final answer.Rather than relying on oversold promises, the release emphasizes that Grok 3and its streamlined variant, Grok 3 miniare still evolving. Early access is designed to encourage user feedback, which will help guide further improvements. The models ability to reveal its reasoning process through a Think button invites users to engage directly with its problem-solving steps, promoting a level of transparency that is often absent in traditional AI outputs.Technical Details and Practical BenefitsAt its core, Grok 3 leverages a reinforcement learning framework to enhance its chain-of-thought process. This approach allows the model to simulate a form of internal reasoning, iterating over possible solutions and correcting errors along the way. Users can observe this process, which is particularly valuable in tasks where a clear rationale is as important as the final answer. The integration of this reasoning mode sets Grok 3 apart from many earlier models that simply generate responses without an explainable thought process.Technically, Grok 3s architecture benefits from an expanded context window, now capable of handling up to one million tokens. This makes it better suited for processing lengthy documents and managing intricate instructions. Benchmark tests indicate notable improvements in various areas, including competition math challenges, advanced reasoning tasks, and code generation. For example, the model achieved a 93.3% accuracy rate on a recent mathematics competition when utilizing its highest level of test-time compute. These technical enhancements translate into practical benefits: clearer, more reliable responses that can support both academic and professional applications without unnecessary embellishment.Data Insights and Comparative AnalysisThe models performance in various benchmarks, such as those assessing reasoning and code generation, demonstrates that it can effectively handle complex tasks. Although some skepticism remains within the community, the empirical results suggest that Grok 3 is a robust addition to the AI landscape.Comparative analysis with other leading models highlights that while many systems continue to be popular choices, Grok 3s combination of enhanced reasoning and a larger context window provides a distinct advantage in addressing more involved queries. Furthermore, the introduction of the Grok 3 mini variant broadens the range of applications by offering a more cost-efficient option for tasks that do not require as extensive world knowledge. This data underscores the importance of continued innovation in AI, driven by rigorous testing and real-world performance rather than speculative promises.ConclusionGrok 3 represents a thoughtful evolution in the quest for more reliable and transparent AI reasoning. By focusing on improved problem-solving through reinforcement learning and offering users a window into its internal thought processes, the model addresses several longstanding challenges. Its performance across a range of benchmarksspanning from competition math to advanced code generationdemonstrates that a balanced, methodical approach to AI development can yield meaningful improvements.For researchers and developers, Grok 3 offers not only enhanced technical capabilities but also a practical tool for exploring complex ideas with greater clarity. The models design reflects a measured progression in AI, one that values incremental improvements and user engagement over hyperbolic claims. As xAI continues to refine Grok 3 based on real-world feedback, the technology stands to play a significant role in both academic research and practical applications in software development.Check outtheTechnical details.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/Learning Intuitive Physics: Advancing AI Through Predictive Representation ModelsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Microsoft AI Releases OmniParser V2: An AI Tool that Turns Any LLM into a Computer Use AgentSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Enhancing Diffusion Models: The Role of Sparsity and Regularization in Efficient Generative AISana Hassanhttps://www.marktechpost.com/author/sana-hassan/Rethinking AI Safety: Balancing Existential Risks and Practical Challenges

In this tutorial, we will build an interactive text-to-image generator application accessed through Google Colab and a public link using Hugging Faces Diffusers library and Gradio. Youll learn how to transform simple text prompts into detailed images by leveraging the state-of-the-art Stable Diffusion model and GPU acceleration. Well walk through setting up the environment, installing dependencies, caching the model, and creating an intuitive application interface that allows real-time parameter adjustments.!pip install diffusers transformers accelerate gradioFirst, we install four essential Python packages using pip. Diffusers provides tools for working with diffusion models, Transformers offers pretrained models for various tasks, Accelerate optimizes performance on different hardware setups, and Gradio enables the creation of interactive machine learning interfaces. These libraries form the backbone of our text-to-image generation demo in Google Colab. Set the runtime to GPU.import torchfrom diffusers import StableDiffusionPipelineimport gradio as gr# Global variable to cache the pipelinepipe = NoneNo, we import necessary libraries: torch for tensor computations and GPU acceleration, StableDiffusionPipeline from the Diffusers library for loading and running the Stable Diffusion model, and gradio for building interactive demos. Also, a global variable pipe is initialized to None to cache the loaded model pipeline later, which helps avoid reloading the model on every inference call.print("CUDA available:", torch.cuda.is_available())The above code line indicates whether a CUDA-enabled GPU is available. It uses PyTorchs torch.cuda.is_available() function returns True if a GPU is detected and ready for computations and False otherwise, helping ensure that your code can leverage GPU acceleration.pipe = StableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)pipe = pipe.to("cuda")The above code snippet loads the Stable Diffusion pipeline using a pretrained model from runwayml/stable-diffusion-v1-5. It sets its data type to a 16-bit floating point (torch.float16) to optimize memory usage and performance. It then moves the entire pipeline to the GPU (cuda) to leverage hardware acceleration for faster image generation.def generate_sd_image(prompt, num_inference_steps=50, guidance_scale=7.5): """ Generate an image from a text prompt using Stable Diffusion. Args: prompt (str): Text prompt to guide image generation. num_inference_steps (int): Number of denoising steps (more steps can improve quality). guidance_scale (float): Controls how strongly the prompt is followed. Returns: PIL.Image: The generated image. """ global pipe if pipe is None: print("Loading Stable Diffusion model... (this may take a while)") pipe = StableDiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, revision="fp16" ) pipe = pipe.to("cuda") # Use autocast for faster inference on GPU with torch.autocast("cuda"): image = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale).images[0] return imageAbove function, generate_sd_image, takes a text prompt along with parameters for inference steps and guidance scale to generate an image using Stable Diffusion. It checks if the model pipeline is already loaded in the global pipe variable; if not, it loads and caches the model with half-precision (FP16) and moves it to the GPU. It then utilizes torch.autocast for efficient mixed-precision inference and returns the generated image.# Define the Gradio interfacedemo = gr.Interface( fn=generate_sd_image, inputs=[ gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Text Prompt"), gr.Slider(minimum=10, maximum=100, step=5, value=50, label="Inference Steps"), gr.Slider(minimum=1, maximum=20, step=0.5, value=7.5, label="Guidance Scale") ], outputs=gr.Image(type="pil", label="Generated Image"), title="Stable Diffusion Text-to-Image Demo", description="Enter a text prompt to generate an image using Stable Diffusion. Adjust the parameters to fine-tune the result.")# Launch the interactive demodemo.launch()Here, we define a Gradio interface that connects the generate_sd_image function to an interactive web UI. It provides three input widgets, a textbox for entering the text prompt, and sliders for adjusting the number of inference steps and guidance scale. In contrast, the output widget displays the generated image. The interface also includes a title and descriptive text to guide users, and the interactive demo is finally launched. App Interface Generated by Code on Public URLYou can also access the web app through a public URL: https://7dc6833297cf83b160.gradio.live/ (Active for 72 hrs). A similar link will be generated for your code as well.In conclusion, this tutorial demonstrated how to integrate Hugging Faces Diffusers with Gradio to create a powerful, interactive text-to-image application in Google Colab and a web application. From setting up the GPU-accelerated environment and caching the Stable Diffusion model to building an interface for dynamic user interaction, you have a solid foundation to experiment with and further develop advanced generative models.Here is the Colab Notebook for the above project. Also,dont forget to follow us onTwitterand join ourTelegram ChannelandLinkedIn Group. Dont Forget to join our75k+ ML SubReddit. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/Microsoft Researchers Present Magma: A Multimodal AI Model Integrating Vision, Language, and Action for Advanced Robotics, UI Navigation, and Intelligent Decision-MakingAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Moonshot AI Research Introduce Mixture of Block Attention (MoBA): A New AI Approach that Applies the Principles of Mixture of Experts (MoE) to the Attention MechanismAsif Razzaqhttps://www.marktechpost.com/author/6flvq/DeepSeek AI Introduces NSA: A Hardware-Aligned and Natively Trainable Sparse Attention Mechanism for Ultra-Fast Long-Context Training and InferenceAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Stepwise Python Code Implementation to Create Interactive Photorealistic Faces with NVIDIA StyleGAN2ADA

Knowledge graphs (KGs) are the foundation of artificial intelligence applications but are incomplete and sparse, affecting their effectiveness. Well-established KGs such as DBpedia and Wikidata lack essential entity relationships, diminishing their utility in retrieval-augmented generation (RAG) and other machine-learning tasks. Traditional extraction methods are likely to provide sparse graphs with absent important connections or noisy, redundant representations. Therefore it is difficult to obtain high-quality structured knowledge from unstructured text. Overcoming these challenges is critical to enable improved knowledge retrieval, reasoning, and insights with the help of artificial intelligence.State-of-the-art methods for extracting KGs from raw text are Open Information Extraction (OpenIE) and GraphRAG. OpenIE, a dependency parsing technique, produces structured (subject, relation, object) triples but produces extremely complex and redundant nodes, reducing coherence. GraphRAG, which combines graph-based retrieval and language models, enhances entity linking but does not produce densely connected graphs, restricting downstream reasoning processes. Both techniques are plagued by low entity resolution consistency, sparsity in connectivity, and poor generalizability, rendering them ineffective for high-quality KG extraction.Researchers from Stanford University, the University of Toronto, and FAR AI introduce KGGen, a novel text-to-KG generator that leverages language models and clustering algorithms to extract structured knowledge from plain text. Unlike earlier methods, KGGen introduces an iterative LM-based clustering method that enhances the extracted graph by merging synonymous entities and grouping relations. This enhances sparsity and redundancy, offering a more coherent and well-connected KG. KGGen also introduces MINE (Measure of Information in Nodes and Edges), the first benchmark for text-to-KG extraction performance, enabling standardized measurement of extraction methods.KGGen operates through a modular Python package with modules for entity and relation extraction, aggregation, and entity and edge clustering. The module for entity and relation extraction employs GPT-4o to obtain structured triples (subject, predicate, object) from unstructured text. The aggregation module combines extracted triples from different sources into a unified knowledge graph (KG), hence ensuring a homogeneous representation of entities. The module for entity and edge clustering uses an iterative clustering algorithm to disambiguate synonymous entities, cluster similar edges, and enhance graph connectivity. Through the enforcement of strict constraints on the language model using DSPy, KGGen enables the attainment of structured and high-fidelity extractions. The output knowledge graph is distinguished by its dense connectivity, semantic relevance, and optimization for artificial intelligence purposes.The benchmarking outcomes indicate the success of the method in extracting structured knowledge from text sources. KGGen gets an accuracy rate of 66.07%, which is significantly greater than GraphRAG at 47.80% and OpenIE at 29.84%. The system facilitates the capability to extract and structure knowledge without redundancy and enhancing connectivity and coherence. Comparative analysis confirms an 18% improvement in extraction fidelity over existing methods, highlighting its capability to generate well-structured knowledge graphs. Tests also demonstrate that produced graphs are denser and more informative, making them particularly suitable in the context of knowledge retrieval tasks and AI-based reasoning.KGGen is a breakthrough in the field of knowledge graph extraction because it pairs language model-based entity recognition with iterative clustering techniques to generate higher-quality structured data. By achieving radically improved accuracy on the MINE benchmark, it raises the bar for transforming unstructured text into impactful representations. This breakthrough has far-reaching implications for artificial intelligence-driven knowledge retrieval, reasoning operations, and embedding-based learning, thus paving the way for further development of larger and more comprehensive knowledge graphs. Future development will focus on refining clustering techniques and expanding benchmark tests to cover larger datasets.Check outthePaper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Aswin AkAswin AK is a consulting intern at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, bringing a strong academic background and hands-on experience in solving real-life cross-domain challenges.Aswin Akhttps://www.marktechpost.com/author/aswinak/ViLa-MIL: Enhancing Whole Slide Image Classification with Dual-Scale Vision-Language Multiple Instance LearningAswin Akhttps://www.marktechpost.com/author/aswinak/Mistral AI Introduces Mistral Saba: A New Regional Language Model Designed to Excel in Arabic and South Indian-Origin Languages such as TamilAswin Akhttps://www.marktechpost.com/author/aswinak/Higher-Order Guided Diffusion for Graph Generation: A Coarse-to-Fine Approach to Preserving Topological StructuresAswin Akhttps://www.marktechpost.com/author/aswinak/Can Users Fix AI Bias? Exploring User-Driven Value Alignment in AI Companions

Multimodal AI agents are designed to process and integrate various data types, such as images, text, and videos, to perform tasks in digital and physical environments. They are used in robotics, virtual assistants, and user interface automation, where they need to understand and act based on complex multimodal inputs. These systems aim to bridge verbal and spatial intelligence by leveraging deep learning techniques, enabling interactions across multiple domains.AI systems often specialize in vision-language understanding or robotic manipulation but struggle to combine these capabilities into a single model. Many AI models are designed for domain-specific tasks, such as UI navigation in digital environments or physical manipulation in robotics, limiting their generalization across different applications. The challenge lies in developing a unified model to understand and act across multiple modalities, ensuring effective decision-making in structured and unstructured environments.Existing Vision-Language-Action (VLA) models attempt to address multimodal tasks by pretraining on large datasets of vision-language pairs followed by action trajectory data. However, these models typically lack adaptability across different environments. Examples include Pix2Act and WebGUM, which excel in UI navigation, and OpenVLA and RT-2, which are optimized for robotic manipulation. These models often require separate training processes and fail to generalize across both digital and physical environments. Also, conventional multimodal models struggle with integrating spatial and temporal intelligence, limiting their ability to perform complex tasks autonomously.Researchers from Microsoft Research, the University of Maryland, the University of Wisconsin-Madison KAIST, and the University of Washington introduced Magma, a foundation model designed to unify multimodal understanding with action execution, enabling AI agents to function seamlessly in digital and physical environments. Magma is designed to overcome the shortcomings of existing VLA models by incorporating a robust training methodology that integrates multimodal understanding, action grounding, and planning. Magma is trained using a diverse dataset comprising 39 million samples, including images, videos, and robotic action trajectories. It incorporates two novel techniques,Set-of-Mark (SoM): SoM enables the model to label actionable visual objects, such as buttons in UI environmentsTrace-of-Mark (ToM): ToM allows it to track object movements over time and plan future actions accordinglyMagma employs a combination of deep learning architectures and large-scale pretraining to optimize its performance across multiple domains. The model uses a ConvNeXt-XXL vision backbone to process images and videos, while an LLaMA-3-8B language model handles textual inputs. This architecture enables Magma to integrate vision-language understanding with action execution seamlessly. It is trained on a curated dataset that includes UI navigation tasks from SeeClick and Vision2UI, robotic manipulation datasets from Open-X-Embodiment, and instructional videos from sources like Ego4D, Something-Something V2, and Epic-Kitchen. By leveraging SoM and ToM, Magma can effectively learn action grounding from UI screenshots and robotics data while enhancing its ability to predict future actions based on observed visual sequences. During training, the model processes up to 2.7 million UI screenshots, 970,000 robotic trajectories, and over 25 million video samples to ensure robust multimodal learning.In zero-shot UI navigation tasks, Magma achieved an element selection accuracy of 57.2%, outperforming models like GPT-4V-OmniParser and SeeClick. In robotic manipulation tasks, Magma attained a success rate of 52.3% in Google Robot tasks and 35.4% in Bridge simulations, significantly surpassing OpenVLA, which only achieved 31.7% and 15.9% in the same benchmarks. The model also performed exceptionally well in multimodal understanding tasks, reaching 80.0% accuracy in VQA v2, 66.5% in TextVQA, and 87.4% in POPE evaluations. Magma also demonstrated strong spatial reasoning capabilities, scoring 74.8% on the BLINK dataset and 80.1% on the Visual Spatial Reasoning (VSR) benchmark. In video question-answering tasks, Magma achieved an accuracy of 88.6% on IntentQA and 72.9% on NextQA, further highlighting its ability to process temporal information effectively.Several Key Takeaways emerge from the Research on Magma:Magma was trained on 39 million multimodal samples, including 2.7 million UI screenshots, 970,000 robotic trajectories, and 25 million video samples.The model combines vision, language, and action in a unified framework, overcoming the limitations of domain-specific AI models.SoM enables accurate labeling of clickable objects, while ToM allows tracking object movement over time, improving long-term planning capabilities.Magma achieved a 57.2% accuracy rate in element selection in UI tasks, a 52.3% success rate in robotic manipulation, and an 80.0% accuracy rate in VQA tasks.Magma outperformed existing AI models by over 19.6% in spatial reasoning benchmarks and improved by 28% over previous models in video-based reasoning.Magma demonstrated superior generalization across multiple tasks without requiring additional fine-tuning, making it a highly adaptable AI agent.Magmas capabilities can enhance decision-making and execution in robotics, autonomous systems, UI automation, digital assistants, and industrial AI.Check outthePaper and Project Page.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Recommended Read- LG AI Research Releases NEXUS: An Advanced System Integrating Agent AI System and Data Compliance Standards to Address Legal Concerns in AI DatasetsThe post Microsoft Researchers Present Magma: A Multimodal AI Model Integrating Vision, Language, and Action for Advanced Robotics, UI Navigation, and Intelligent Decision-Making appeared first on MarkTechPost.

Humans possess an innate understanding of physics, expecting objects to behave predictably without abrupt changes in position, shape, or color. This fundamental cognition is observed in infants, primates, birds, and marine mammals, supporting the core knowledge hypothesis, which suggests humans have evolutionarily developed systems for reasoning about objects, space, and agents. While AI surpasses humans in complex tasks like coding and mathematics, it struggles with intuitive physics, highlighting Moravecs paradox. AI approaches to physical reasoning fall into two categories: structured models, which simulate object interactions using predefined rules, and pixel-based generative models, which predict future sensory inputs without explicit abstractions.Researchers from FAIR at Meta, Univ Gustave Eiffel, and EHESS explore how general-purpose deep neural networks develop an understanding of intuitive physics by predicting masked regions in natural videos. Using the violation-of-expectation framework, they demonstrate that models trained to predict outcomes in an abstract representation spacesuch as Joint Embedding Predictive Architectures (JEPAs)can accurately recognize physical properties like object permanence and shape consistency. In contrast, video prediction models operating in pixel space and multimodal large language models perform closer to random guessing. This suggests that learning in an abstract space, rather than relying on predefined rules, is sufficient to acquire an intuitive understanding of physics.The study focuses on a video-based JEPA model, V-JEPA, which predicts future video frames in a learned representation space, aligning with the predictive coding theory in neuroscience. V-JEPA achieved 98% zero-shot accuracy on the IntPhys benchmark and 62% on the InfLevel benchmark, outperforming other models. Ablation experiments revealed that intuitive physics understanding emerges robustly across different model sizes and training durations. Even a small 115 million parameter V-JEPA model or one trained on just one week of video showed above-chance performance. These findings challenge the notion that intuitive physics requires innate core knowledge and highlight the potential of abstract prediction models in developing physical reasoning.The violation-of-expectation paradigm in developmental psychology assesses intuitive physics understanding by observing reactions to physically impossible scenarios. Traditionally applied to infants, this method measures surprise responses through physiological indicators like gaze time. More recently, it has been extended to AI systems by presenting them with paired visual scenes, where one includes a physical impossibility, such as a ball disappearing behind an occluder. The V-JEPA architecture, designed for video prediction tasks, learns high-level representations by predicting masked portions of videos. This approach enables the model to develop an implicit understanding of object dynamics without relying on predefined abstractions, as shown through its ability to anticipate and react to unexpected physical events in video sequences.V-JEPA was tested on datasets such as IntPhys, GRASP, and InfLevel-lab to benchmark intuitive physics comprehension, assessing properties like object permanence, continuity, and gravity. Compared to other models, including VideoMAEv2 and multimodal language models like Qwen2-VL-7B and Gemini 1.5 pro, V-JEPA achieved significantly higher accuracy, demonstrating that learning in a structured representation space enhances physical reasoning. Statistical analyses confirmed its superiority over untrained networks across multiple properties, reinforcing that self-supervised video prediction fosters a deeper understanding of real-world physics. These findings highlight the challenge of intuitive physics for existing AI models and suggest that predictive learning in a learned representation space is key to improving AIs physical reasoning abilities.In conclusion, the study explores how state-of-the-art deep learning models develop an understanding of intuitive physics. The model demonstrates intuitive physics comprehension without task-specific adaptation by pretraining V-JEPA on natural videos using a prediction task in a learned representation space. Results suggest this ability arises from general learning principles rather than hardwired knowledge. However, V-JEPA struggles with object interactions, likely due to training limitations and short video processing. Enhancing model memory and incorporating action-based learning could improve performance. Future research may examine models trained on infant-like visual data, reinforcing the potential of predictive learning for physical reasoning in AI.Check outthePaper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/Microsoft AI Releases OmniParser V2: An AI Tool that Turns Any LLM into a Computer Use AgentSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Enhancing Diffusion Models: The Role of Sparsity and Regularization in Efficient Generative AISana Hassanhttps://www.marktechpost.com/author/sana-hassan/Rethinking AI Safety: Balancing Existential Risks and Practical ChallengesSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Nous Research Released DeepHermes 3 Preview: A Llama-3-8B Based Model Combining Deep Reasoning, Advanced Function Calling, and Seamless Conversational Intelligence

Multimodal Large Language Models (MLLMs) have gained significant attention for their ability to handle complex tasks involving vision, language, and audio integration. However, they lack the comprehensive alignment beyond basic Supervised Fine-tuning (SFT). Current state-of-the-art models often bypass rigorous alignment stages, leaving crucial aspects like truthfulness, safety, and human preference alignment inadequately addressed. Existing approaches target only specific domains such as hallucination reduction or conversational improvements, falling short of enhancing the models overall performance and reliability. This narrow focus raises questions about whether human preference alignment can improve MLLMs across a broader spectrum of tasks.Recent years have witnessed substantial progress in MLLMs, built upon advanced LLM architectures like GPTs, LLaMA, Alpaca, Vicuna, and Mistral. These models have evolved through end-to-end training approaches, tackling complex multimodal tasks involving image-text alignment, reasoning, and instruction following. Several open-source MLLMs, including Otter, mPLUG-Owl, LLaVA, Qwen-VL, and VITA, have emerged to address fundamental multimodal challenges. However, alignment efforts have remained limited. While algorithms like Fact-RLHF and LLAVACRITIC have shown promise in reducing hallucinations and improving conversational abilities, they havent enhanced general capabilities. Evaluation frameworks such as MME, MMBench, and Seed-Bench have been developed to assess these models.Researchers from KuaiShou, CASIA, NJU, USTC, PKU, Alibaba, and Meta AI have proposed MM-RLHF, an innovative approach featuring a comprehensive dataset of 120k fine-grained, human-annotated preference comparison pairs. This dataset represents a significant advancement in terms of size, diversity, and annotation quality compared to existing resources. The method introduces two key innovations: a Critique-Based Reward Model that generates detailed critiques before scoring outputs, and Dynamic Reward Scaling that optimizes sample weights based on reward signals. It enhances both the interpretability of model decisions and the efficiency of the alignment process, addressing the limitations of traditional scalar reward mechanisms in multimodal contexts.The MM-RLHF implementation involves a complex data preparation and filtering process across three main domains: image understanding, video understanding, and multimodal safety. The image understanding component integrates data from multiple sources including LLaVA-OV, VLfeedback, and LLaVA-RLHF, with multi-turn dialogues converted to single-turn format. This compilation results in over 10 million dialogue samples covering diverse tasks from basic conversation to complex reasoning. The data filtering process uses predefined sampling weights categorized into three types: multiple-choice questions for testing reasoning and perception, long-text questions for evaluating conversational abilities, and short-text questions for basic image analysis.The evaluation of MM-RLHF and MM-DPO shows significant improvements across multiple dimensions when applied to models like LLaVA-OV-7B, LLaVA-OV-0.5B, and InternVL-1B. Conversational abilities improved by over 10%, while unsafe behaviors decreased by at least 50%. The aligned models show better results in hallucination reduction, mathematical reasoning, and multi-image understanding, even without specific training data for some tasks. However, model-specific variations are observed, with different models requiring distinct hyperparameter settings for optimal performance. Also, high-resolution tasks show limited gains due to dataset constraints and filtering strategies that dont target resolution optimization.In this paper, researchers introduced MM-RLHF, a dataset and alignment approach that shows significant advancement in MLLM development. Unlike previous task-specific approaches, this method takes a holistic approach to improve model performance across multiple dimensions. The datasets rich annotation granularity, including per-dimension scores and ranking rationales, offers untapped potential for future development. Future research directions will focus on utilizing this granularity through advanced optimization techniques, addressing high-resolution data limitations, and expanding the dataset through semi-automated methods, potentially establishing a foundation for more robust multimodal learning frameworks.Check outthePaper and Project Page.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Sajjad AnsariSajjad Ansari is a final year undergraduate from IIT Kharagpur. As a Tech enthusiast, he delves into the practical applications of AI with a focus on understanding the impact of AI technologies and their real-world implications. He aims to articulate complex AI concepts in a clear and accessible manner.Sajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Enhancing Reasoning Capabilities in Low-Resource Language Models through Efficient Model MergingSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/TransMLA: Transforming GQA-based Models Into MLA-based ModelsSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Microsoft Research Introduces Data Formulator: An AI Application that Leverages LLMs to Transform Data and Create Rich VisualizationsSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/ByteDance Introduces UltraMem: A Novel AI Architecture for High-Performance, Resource-Efficient Language Models

Efficiently handling long contexts has been a longstanding challenge in natural language processing. As large language models expand their capacity to read, comprehend, and generate text, the attention mechanismcentral to how they process inputcan become a bottleneck. In a typical Transformer architecture, this mechanism compares every token to every other token, resulting in computational costs that scale quadratically with sequence length. This problem grows more pressing as we apply language models to tasks that require them to consult vast amounts of textual information: long-form documents, multi-chapter books, legal briefs, or large code repositories. When a model must navigate tens or even hundreds of thousands of tokens, the cost of naively computing full attention becomes prohibitive.Previous efforts to address this issue often rely on imposing fixed structures or approximations that may compromise quality in certain scenarios. For example, sliding-window mechanisms confine tokens to a local neighborhood, which can obscure important global relationships. Meanwhile, approaches that radically alter the fundamental architecturesuch as replacing softmax attention with entirely new constructscan demand extensive retraining from scratch, making it difficult to benefit from existing pre-trained models. Researchers have sought a method that maintains the key benefits of the original Transformer designits adaptability and ability to capture wide-ranging dependencieswithout incurring the immense computational overhead associated with traditional full attention on extremely long sequences.Researchers from Moonshot AI, Tsinghua University, and Zhejiang University introduce Mixture of Block Attention (MoBA), an innovative approach that applies the principles of Mixture of Experts (MoE) to the attention mechanism. By partitioning the input into manageable blocks and using a trainable gating system to decide which blocks are relevant for each query token, MoBA addresses the inefficiency that arises when a model has to compare every token to every other token. Unlike approaches that rigidly enforce local or windowed attention, MoBA allows the model to learn where to focus. This design is guided by the principle of less structure, meaning the architecture does not predefine exactly which tokens should interact. Instead, it delegates those decisions to a learned gating network.A key feature of MoBA is its capacity to function seamlessly with existing Transformer-based models. Rather than discarding the standard self-attention interface, MoBA operates as a form of plug-in or substitute. It maintains the same number of parameters, so it does not bloat the architecture, and it preserves causal masking to ensure correctness in autoregressive generation. In practical deployments, MoBA can be toggled between sparse and full attention, enabling the model to benefit from speedups when tackling extremely long inputs while preserving the fallback to standard full attention in layers or phases of training where it might be desirable.Technical Details and BenefitsMoBA centers on dividing the context into blocks, each of which spans a consecutive range of tokens. The gating mechanism computes an affinity score between a query token and each block, typically by comparing the query with a pooled representation of the blocks keys. It then chooses the top-scoring blocks. As a result, only those tokens in the most relevant blocks contribute to the final attention distribution. The block that contains the query itself is always included, ensuring local context remains accessible. At the same time, a causal mask is enforced so that tokens do not attend to positions in the future, preserving the left-to-right autoregressive property.Because of this procedure, MoBAs attention matrix is significantly sparser than in the original Transformer. Yet, it remains flexible enough to allow queries to attend to faraway information when needed. For instance, if a question posed near the end of a text can only be answered by referencing details near the beginning, the gating mechanism can learn to assign a high score to the relevant earlier block. Technically, this block-based method reduces the number of token comparisons to sub-quadratic scales, bringing efficiency gains that become especially evident as context lengths climb into the hundreds of thousands or even millions of tokens.Another appealing aspect of MoBA is its compatibility with modern accelerators and specialized kernels. In particular, the authors combine MoBA with FlashAttention, a high-performance library for fast, memory-efficient exact attention. By carefully grouping the querykeyvalue operations according to which blocks have been selected, they can streamline computations. The authors report that at one million tokens, MoBA can yield roughly a sixfold speedup compared to conventional full attention, underscoring its practicality in real-world use cases.Results and InsightsAccording to the technical report, MoBA demonstrates performance on par with full attention across a variety of tasks, while offering significant computational savings when dealing with long sequences. Tests on language modeling data show that MoBAs perplexities remain close to those of a full-attention Transformer at sequence lengths of 8,192 or 32,768 tokens. Critically, as the researchers gradually extend context lengths to 128,000 and beyond, MoBA retains robust long-context comprehension. The authors present trailing token evaluations, which concentrate on the models ability to predict tokens near the end of a long promptan area that typically highlights weaknesses of methods relying on heavy approximations. MoBA effectively manages these trailing positions without any drastic loss in predictive quality.They also explore the sensitivity of the approach to block size and gating strategies. In some experiments, refining the granularity (i.e., using smaller blocks but selecting more of them) helps the model approximate full attention more closely. Even in settings where MoBA leaves out large portions of the context, adaptive gating can identify the blocks that truly matter for the query. Meanwhile, a hybrid regime demonstrates a balanced approach: some layers continue to use MoBA for speed, while a smaller number of layers revert to full attention. This hybrid approach can be particularly beneficial when performing supervised fine-tuning, where certain positions in the input might be masked out from the training objective. By preserving full attention in a few upper layers, the model can retain broad context coverage, benefiting tasks that require more global perspective.Overall, these findings suggest that MoBA is well-suited for tasks that involve extensive context, such as reading comprehension of long documents, large-scale code completion, or multi-turn dialogue systems where the entire conversation history becomes essential. Its practical efficiency gains and minimal performance trade-offs position MoBA as an appealing method for making large language models more efficient at scale.ConclusionIn conclusion, Mixture of Block Attention (MoBA) provides a pathway toward more efficient long-context processing in large language models, without an extensive overhaul of the Transformer architecture or a drop in performance. By adopting Mixture of Experts ideas within the attention module, MoBA offers a learnable yet sparse way to focus on relevant portions of very long inputs. The adaptability inherent in its designparticularly its seamless switching between sparse and full attentionmakes it especially attractive for ongoing or future training pipelines. Researchers can fine-tune how aggressively to trim the attention pattern, or selectively use full attention for tasks that demand exhaustive coverage.Though much of the attention to MoBA focuses on textual contexts, the underlying mechanism may also hold promise for other data modalities. Wherever sequence lengths are large enough to raise computational or memory concerns, the notion of assigning queries to block experts could alleviate bottlenecks while preserving the capacity to handle essential global dependencies. As sequence lengths in language applications continue to grow, approaches like MoBA may play a critical role in advancing the scalability and cost-effectiveness of neural language modeling.Check outthePaper and GitHub Page.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/DeepSeek AI Introduces NSA: A Hardware-Aligned and Natively Trainable Sparse Attention Mechanism for Ultra-Fast Long-Context Training and InferenceAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Stepwise Python Code Implementation to Create Interactive Photorealistic Faces with NVIDIA StyleGAN2ADAAsif Razzaqhttps://www.marktechpost.com/author/6flvq/OpenAI introduces SWE-Lancer: A Benchmark for Evaluating Model Performance on Real-World Freelance Software Engineering WorkAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Scale AI Research Introduces J2 Attackers: Leveraging Human Expertise to Transform Advanced LLMs into Effective Red Teamers

In the realm of artificial intelligence, enabling Large Language Models (LLMs) to navigate and interact with graphical user interfaces (GUIs) has been a notable challenge. While LLMs are adept at processing textual data, they often encounter difficulties when interpreting visual elements like icons, buttons, and menus. This limitation restricts their effectiveness in tasks that require seamless interaction with software interfaces, which are predominantly visual.To address this issue, Microsoft has introduced OmniParser V2, a tool designed to enhance the GUI comprehension capabilities of LLMs. OmniParser V2 converts UI screenshots into structured, machine-readable data, enabling LLMs to understand and interact with various software interfaces more effectively. This development aims to bridge the gap between textual and visual data processing, facilitating more comprehensive AI applications.OmniParser V2 operates through two main components: detection and captioning. The detection module employs a fine-tuned version of the YOLOv8 model to identify interactive elements within a screenshot, such as buttons and icons. Simultaneously, the captioning module uses a fine-tuned Florence-2 base model to generate descriptive labels for these elements, providing context about their functions within the interface. This combined approach allows LLMs to construct a detailed understanding of the GUI, which is essential for accurate interaction and task execution.A significant improvement in OmniParser V2 is the enhancement of its training datasets. The tool has been trained on a more extensive and refined set of icon captioning and grounding data, sourced from widely used web pages and applications. This enriched dataset enhances the models accuracy in detecting and describing smaller interactive elements, which are crucial for effective GUI interaction. Additionally, by optimizing the image size processed by the icon caption model, OmniParser V2 achieves a 60% reduction in latency compared to its previous version, with an average processing time of 0.6 seconds per frame on an A100 GPU and 0.8 seconds on a single RTX 4090 GPU.The effectiveness of OmniParser V2 is demonstrated through its performance on the ScreenSpot Pro benchmark, an evaluation framework for GUI grounding capabilities. When combined with GPT-4o, OmniParser V2 achieved an average accuracy of 39.6%, a notable increase from GPT-4os baseline score of 0.8%. This improvement highlights the tools ability to enable LLMs to accurately interpret and interact with complex GUIs, even those with high-resolution displays and small target icons.To support integration and experimentation, Microsoft has developed OmniTool, a dockerized Windows system that incorporates OmniParser V2 along with essential tools for agent development. OmniTool is compatible with various state-of-the-art LLMs, including OpenAIs 4o/o1/o3-mini, DeepSeeks R1, Qwens 2.5VL, and Anthropics Sonnet. This flexibility allows developers to utilize OmniParser V2 across different models and applications, simplifying the creation of vision-based GUI agents.In summary, OmniParser V2 represents a meaningful advancement in integrating LLMs with graphical user interfaces. By converting UI screenshots into structured data, it enables LLMs to comprehend and interact with software interfaces more effectively. The technical enhancements in detection accuracy, latency reduction, and benchmark performance make OmniParser V2 a valuable tool for developers aiming to create intelligent agents capable of navigating and manipulating GUIs autonomously. As AI continues to evolve, tools like OmniParser V2 are essential in bridging the gap between textual and visual data processing, leading to more intuitive and capable AI systems.Check outtheTechnical Details, Model on HF and GitHub Page.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/Enhancing Diffusion Models: The Role of Sparsity and Regularization in Efficient Generative AISana Hassanhttps://www.marktechpost.com/author/sana-hassan/Rethinking AI Safety: Balancing Existential Risks and Practical ChallengesSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Nous Research Released DeepHermes 3 Preview: A Llama-3-8B Based Model Combining Deep Reasoning, Advanced Function Calling, and Seamless Conversational IntelligenceSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Layer Parallelism: Enhancing LLM Inference Efficiency Through Parallel Execution of Transformer Layers

Whole Slide Image (WSI) classification in digital pathology presents several critical challenges due to the immense size and hierarchical nature of WSIs. WSIs contain billions of pixels and hence direct observation is computationally infeasible. Current strategies based on multiple instance learning (MIL) are effective in performance but considerably dependent on large amounts of bag-level annotated data, whose acquisition is troublesome, particularly in the case of rare diseases. Moreover, current strategies are strongly based on image insights and encounter generalization issues due to differences in the data distribution across hospitals. Recent improvements in Vision-Language Models (VLMs) introduce linguistic prior through large-scale pretraining from image-text pairs; however, current strategies fall short in addressing domain-specific insights related to pathology. Moreover, the computationally expensive nature of pretraining models and their insufficient adaptability with the hierarchical characteristic specific to pathology are additional setbacks. It is essential to transcend these challenges to promote AI-based cancer diagnosis and proper WSI classification.MIL-based methods generally adopt a three-stage pipeline: patch cropping from WSIs, feature extraction with a pre-trained encoder, and patch-level to slide-level feature aggregation to make predictions. Although these methods are effective for pathology-related tasks like cancer subtyping and staging, their dependency on large annotated datasets and data distribution shift sensitivity renders them less practical to use. VLM-based models like CLIP and BiomedCLIP try to tap into language priors by utilizing large-scale image-text pairs gathered from online databases. These models, however, depend on general, handcrafted text prompts that lack the subtlety of pathological diagnosis. In addition, knowledge transfer from vision-language models to WSIs is inefficient owing to the hierarchical and large-scale nature of WSIs, which demands astronomical computational costs and dataset-specific fine-tuning.Researchers from Xian Jiaotong University, Tencent YouTu Lab, and Institute of High-Performance Computing Singapore introduce a dual-scale vision-language multiple instance learning model capable of efficiently transferring vision-language model knowledge to digital pathology through descriptive text prompts designed specifically for pathology and trainable decoders for image and text branches. In contrast to generic class-name-based prompts for traditional vision-language methods, the model utilizes a frozen large language model to generate domain-specific descriptions at two resolutions. The low-scale prompt highlights global tumor structures, and the high-scale prompt highlights finer cellular details, with improved feature discrimination. A prototype-guided patch decoder progressively accumulates patch features by clustering similar patches into learnable prototype vectors, minimizing computational complexity and improving feature representation. A context-guided text decoder further improves text descriptions by using multi-granular image context, facilitating a more effective fusion of visual and textual modalities.The model proposed relies on CLIP as its underlying model and utilizes several additions to adapt it for pathology tasks. Whole-slide images are patchily segmented at the 5 and 10 magnification levels, while feature extraction uses a frozen ResNet-50 image encoder. A frozen large GPT-3.5 language model is also used to generate class-specific descriptive prompts for two scales with learnable vectors to facilitate effective feature representation. Progressive feature agglomeration is supported using a set of 16 learnable prototype vectors. The patch and prototype multi-granular features also help support the text embeddings, hence improved cross-modal alignment. Optimizing training makes use of the cross-entropy loss with equally weighted low- and high-scale similarity scores for robust classification support.This method demonstrates better performance on various subtyping datasets of cancer significantly outperforming current MIL-based and VLM-based methods in few-shot learning scenarios. The model records impressive gains in AUC, F1 score, and accuracy over three diverse datasetsTIHD-RCC, TCGA-RCC, and TCGA-Lungdemonstrating the models solidity in tests executed in both single-center and multi-center setups. In comparison to state-of-the-art approaches, significant gains in classification accuracy are observed with rises of 1.7% to 7.2% in AUC and 2.1% to 7.3% in F1 score. The employment of dual-scale text prompts with a prototype-guided patch decoder and context-guided text decoder aids the framework in its ability to learn effective discriminative morphological patterns despite the presence of few training instances. Moreover, excellent generalization abilities across multiple datasets suggest enhanced adaptability toward domain shift during cross-center testing. These observations demonstrate the merits of fusing vision-language models with pathology-specialized advances toward whole slide image classification.Through the development of a new dual-scale vision-language learning framework, this research makes a substantial contribution to WSI classification with the utilization of large language models to prompt text and prototype-based feature aggregation. The method enhances few-shot generalization, decreases computational cost, and promotes interpretability, solving core pathology AI challenges. By building on the successful vision-language model transfer to digital pathology, this research is a valuable contribution to cancer diagnosis with AI, with the potential to generalize to other medical image tasks.Check outthePaper and GitHub Page.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/DeepSeek AI Introduces NSA: A Hardware-Aligned and Natively Trainable Sparse Attention Mechanism for Ultra-Fast Long-Context Training and InferenceAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Stepwise Python Code Implementation to Create Interactive Photorealistic Faces with NVIDIA StyleGAN2ADAAsif Razzaqhttps://www.marktechpost.com/author/6flvq/OpenAI introduces SWE-Lancer: A Benchmark for Evaluating Model Performance on Real-World Freelance Software Engineering WorkAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Scale AI Research Introduces J2 Attackers: Leveraging Human Expertise to Transform Advanced LLMs into Effective Red Teamers

Vision Language Models have been a revolutionizing milestone in the development of language models, which overcomes the shortcomings of predecessor pre-trained LLMs like LLama, GPT, etc. Vision Language Models explore a new territory beyond single modularity to combine inputs from text and image videos. VLMs thus bestow a better understanding of visual-spatial relationships by expanding the representational boundaries of input, supporting a richer worldview. With new opportunities come new challenges, which is the case with VLMs. Currently, researchers across the globe are encountering and solving new challenges to make VLMs better, one at a time. Based on a survey by researchers from the University of Maryland and the University of Southern California, this article gives an intricate glimpse of what is going on in this field and what we can expect in the future of vision language models.This article discusses a structured examination of VLMs developed over the past five years, encompassing architectures, training methodologies, benchmarks, applications, and the challenges inherent in the field. To begin with, lets familiarize ourselves with some of the SOTA models in VLM and where they come from -CLIP by OpenAI, BLIP by Salesforce, Flamingo by DeepMind, and Gemini. These are the big fish in this domain, which is expanding rapidly to support multimodality user interaction.When we dissect a VLM to understand its structure, we find that certain blocks are fundamental to models, irrespective of their features or capabilities. These are Vision Encoder, Text Encoder, and Text Decoder. Further, the mechanism of cross-attention integrates information across modalities, but it is present in fewer. The architecture of VLMs is also evolving as developers now use pre-trained Large Language models as the backbone instead of training from scratch. Self-supervised methodologies such as masked image modeling and contrastive learning have been prevalent in the latter option. On the other hand, while using a pre-trained model backbone, the most common ways to align visual and pre-trained LLM text features are using a projector, joint training, and freezing training stages.Another interesting development is how the latest models treat visual features as tokens. Besides, the transfusion treats discrete text tokens and continuous image vectors in parallel by introducing strategic breakpoints.Now, we discuss the major categories of benchmarks in the domain that evaluate the various capabilities of a VLM. Most of the datasets are created via synthetic generation or human annotations. These benchmarks test various models capabilities, including visual text understanding, text-to-image generation, and multimodal general intelligence. There are also benchmarks that test challenges against hallucinations, etc. Answer matching, Multiple Choice Questions, and Image/Text Similarity scores have emerged as common evaluation techniques.VLMs are adapted to a variety of tasks, from virtual-world applications such as virtual embodied agents to real-world applications such as robotics and autonomous driving. Embodied agents are an interesting use case that relies heavily on developing VLMs.Embodied agents are AI models with virtual or physical bodies that can interact with their environment.VLMs increase their user interaction and support system by enabling features like Visual Question Answering. Besides, Generative VLM models like GAN generate visual content like memes, etc. In robotics, VLMs find their use cases in ability manipulation, Navigation, Human-robot Interaction, and Autonomous Driving.While VLMs have shown tremendous potential over their textual counterparts, researchers must overcome multiple limitations and challenges. There are considerable tradeoffs between flexibility and generalizability of models. Further Issues, such as visual hallucination, raise concerns about the models reliability. There are additional constraints on fairness and safety due to biases in training data. Furthermore, in technical challenges, we are yet to see an efficient training and fine-tuning paradigm when high-quality datasets are scarce. Also, the contextual deviations between modalities or misalignments lower the output quality.Conclusion: The paper provides an overview of the ins and outs of Vision Language Models- a new field of research that integrates content from multiple modalities. We see the models architecture, innovations, and challenges in the present times.Check outthePaper and GitHub Page.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Adeeba Alam AnsariAdeeba Alam Ansari is currently pursuing her Dual Degree at the Indian Institute of Technology (IIT) Kharagpur, earning a B.Tech in Industrial Engineering and an M.Tech in Financial Engineering. With a keen interest in machine learning and artificial intelligence, she is an avid reader and an inquisitive individual. Adeeba firmly believes in the power of technology to empower society and promote welfare through innovative solutions driven by empathy and a deep understanding of real-world challenges.Adeeba Alam Ansarihttps://www.marktechpost.com/author/adeeba-alam-ansari/LIMO: The AI Model that Proves Quality Training Beats QuantityAdeeba Alam Ansarihttps://www.marktechpost.com/author/adeeba-alam-ansari/Researchers from ETH Zurich and TUM Share Everything You Need to Know About Multimodal AI Adaptation and GeneralizationAdeeba Alam Ansarihttps://www.marktechpost.com/author/adeeba-alam-ansari/University of Bath Researchers Developed an Efficient and StableMachine Learning Training Method for Neural ODEs with O(1) Memory FootprintAdeeba Alam Ansarihttps://www.marktechpost.com/author/adeeba-alam-ansari/Baidu Research Introduces EICopilot: An Intelligent Agent-based Chatbot to Retrieve and Interpret Enterprise Information from Massive Graph Databases

In this tutorial, we will do an in-depth, interactive exploration of NVIDIAs StyleGAN2ADA PyTorch model, showcasing its powerful capabilities for generating photorealistic images. Leveraging a pretrained FFHQ model, users can generate high-quality synthetic face images from a single latent seed or visualize smooth transitions through latent space interpolation between different seeds. With an intuitive interface powered by interactive widgets, this tutorial is a valuable resource for researchers, artists, and enthusiasts looking to understand and experiment with advanced generative adversarial networks.!git clone https://github.com/NVlabs/stylegan2-ada-pytorch.gitFirst, we clone the NVIDIA StyleGAN2ADA PyTorch repository from GitHub into your current Colab workspace.!mkdir -p stylegan2-ada-pytorch/pretrained!wget https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl -O stylegan2-ada-pytorch/pretrained/ffhq.pklIn this code part, the first command creates the necessary directory (if it doesnt already exist) for storing pretrained models. The second command downloads the FFHQ pretrained model and saves it in that directory for use with the StyleGAN2ADA model.import syssys.path.append('stylegan2-ada-pytorch')In this code, we add the stylegan2-ada-pytorch directory to Pythons module search path, ensuring that modules from the repository can be easily imported and used.import torchimport numpy as npimport PIL.Imageimport matplotlib.pyplot as pltimport ipywidgets as widgetsfrom IPython.display import displayHere, we import statements and load essential libraries for deep learning, numerical operations, image processing, visualization, and interactive controls into your code. These libraries ensure you have the tools to build, manipulate, and display generated images interactively.import legacyimport dnnlibdef generate_image(seed=42, truncation=1.0, network_pkl='stylegan2-ada-pytorch/pretrained/ffhq.pkl'): print(f'Generating image with seed {seed} and truncation {truncation}') device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') with dnnlib.util.open_url(network_pkl) as f: # Load the pretrained generator network G = legacy.load_network_pkl(f)['G_ema'].to(device) z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device) # Create a latent vector using the provided seed label = None # FFHQ is unconditional with torch.no_grad(): # Generate image img = G(z, label, truncation_psi=truncation, noise_mode='const') # Convert image tensor to uint8 and format for display img = (img + 1) * (255/2) img = img.clamp(0,255).to(torch.uint8) img = img[0].permute(1,2,0).cpu().numpy() plt.figure(figsize=(4,4)) plt.imshow(img) plt.axis('off') plt.show()In this part, we define a function called generate_image that Loads the pretrained StyleGAN2ADA generator network from a given URL. Creates a latent vector based on a seed, generates an image with a specified truncation parameter, and then processes and displays the resulting image using matplotlib.def interpolate_images(seed1=42, seed2=123, steps=10, truncation=1.0, network_pkl='stylegan2-ada-pytorch/pretrained/ffhq.pkl'): print(f'Interpolating between seeds {seed1} and {seed2} with {steps} steps and truncation {truncation}') device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') with dnnlib.util.open_url(network_pkl) as f: # Load the pretrained generator network G = legacy.load_network_pkl(f)['G_ema'].to(device) # Generate latent vectors for the two seeds z1 = torch.from_numpy(np.random.RandomState(seed1).randn(1, G.z_dim)).to(device) z2 = torch.from_numpy(np.random.RandomState(seed2).randn(1, G.z_dim)).to(device) # Create interpolation latent vectors alphas = np.linspace(0, 1, steps) z_interp = [] for a in alphas: z_interp.append((1 - a) * z1 + a * z2) z_interp = torch.cat(z_interp, dim=0) label = None # Generate images for each interpolated latent vector with torch.no_grad(): imgs = G(z_interp, label, truncation_psi=truncation, noise_mode='const') imgs = (imgs + 1) * (255/2) imgs = imgs.clamp(0,255).to(torch.uint8).cpu().numpy() plt.figure(figsize=(steps * 2, 2)) # Plot images in a row to visualize the interpolation for i in range(steps): plt.subplot(1, steps, i+1) img = np.transpose(imgs[i], (1,2,0)) plt.imshow(img) plt.axis('off') plt.show()Here, we define interpolate_images, which generates images by interpolating between latent vectors derived from two seeds. It loads the pretrained StyleGAN2ADA generator, computes a smooth transition between the latent codes of the two seeds over a specified number of steps, and then displays the resulting images in a row to visualize the interpolation.Sample Generated ImageIn conclusion, we demonstrated a versatile and hands-on approach using NVIDIAs StyleGAN2ADA model for static image generation and dynamic latent space interpolation. By allowing users to adjust parameters such as seed values and truncation levels interactively, this notebook provides insight into the intricacies of GAN-based image synthesis and fosters creativity and innovation.Here is the Colab Notebook for the above project. Also,dont forget to follow us onTwitterand join ourTelegram ChannelandLinkedIn Group. Dont Forget to join our75k+ ML SubReddit. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/OpenAI introduces SWE-Lancer: A Benchmark for Evaluating Model Performance on Real-World Freelance Software Engineering WorkAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Scale AI Research Introduces J2 Attackers: Leveraging Human Expertise to Transform Advanced LLMs into Effective Red TeamersAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Guide to Setting Up a Custom BPE Tokenizer with Tiktoken for Advanced NLP Applications in PythonAsif Razzaqhttps://www.marktechpost.com/author/6flvq/LG AI Research Releases NEXUS: An Advanced System Integrating Agent AI System and Data Compliance Standards to Address Legal Concerns in AI Datasets

Understanding financial information means analyzing numbers, financial terms, and organized data like tables for useful insights. It requires math calculations and knowledge of economic concepts, rules, and relationships between financial terms. Although sophisticated AI models have shown excellent general reasoning ability, their suitability for financial tasks is questionable. Such tasks require more than simple mathematical calculations since they involve interpreting domain-specific vocabulary, recognizing relationships between financial points, and analyzing structured financial data.Generally, reasoning approaches like chain-of-thought fine-tuning and reinforcement learning boost performance on multiple tasks but collapse with financial rationale. They improve logical reasoning but cannot replicate the complexity of economic information, which requires numerical comprehension, knowledge of the field, and data interpretation in an organized way. While large language models are widely used in finance for tasks like sentiment analysis, market prediction, and automated trading, general models are not optimized for financial reasoning. Finance-specific models, such as BloombergGPT and FinGPT, help understand financial terms but still face challenges in reasoning over financial documents and structured data.To solve this, researchers from TheFinAI proposed Fino1, a financial reasoning model based on Llama-3.1-8B-Instruct. Existing models struggled with financial text, tabular data, and equations, showing poor performance in long-context tasks and multi-table reasoning. Simple dataset improvements and general techniques like CoT fine-tuning failed to bring consistent results. This framework employed reinforcement learning and iterative CoT fine-tuning to enhance financial reasoning, logical step refinement, and decision-making accuracy. Logical sequences were built systematically so the model could analyze financial issues step by step, and verification mechanisms tested reliability to determine correct financial conclusions. Two-stage LoRA fine-tuning resolved contradictions in numerical reasoning and equation solving, with the first stage fine-tuning the model to financial principles and the second stage fine-tuning intricate calculations. Organized training on various finance datasets, such as reports and tabular data, enhanced interpretation to provide more accurate financial statements and transaction records analysis.Researchers evaluated language models on financial reasoning tasks and found DeepSeek-R1 performed best (68.93) due to strong XBRLMath results, followed by DeepSeek-R1-Distill-Llama-70B and DeepSeek-R1-Distill-Qwen-32B. GPT-4o performed well but lagged due to lower XBRL-Math scores. General-purpose models like Llama3.3-70B outperformed some reasoning-focused models, showing that general reasoning did not always enhance financial tasks. Researchers found that logical-task fine-tuning struggled with economic data, while mathematical enhancements improved XBRL-Math but hurt FinQA and DM-Simplong accuracy. Scaling model size did not always help, as smaller models sometimes performed better. Expanding pre-training data and refining post-training techniques improved financial reasoning. Fino1-8B, trained with reasoning paths from GPT-4o, outperformed others, proving financial-specific training was effective. These results highlighted the importance of domain-specific training to improve financial understanding and multi-step numerical reasoning.In summary, the new approach improved financial thinking in LLMs. By taking advantage of reasonability paths from GPT-4o on FinQA, Fino1 was 10% better across three financial tests. Although formal mathematical models performed best on numerical tasks such as XBRL-Math, they fell short of expectations in processing financial text and long contexts, with domain adaptation necessary. Despite the model scale and dataset diversity limitations, this framework can act as a baseline for future research. Advancements in dataset expansion, retrieval-augmented methods, and multi-step reasoning can further enhance financial LLMs for real-world applications.Check outTwitterand dont forget to join our75k+ ML SubReddit. Divyesh Vitthal JawkhedeDivyesh is a consulting intern at Marktechpost. He is pursuing a BTech in Agricultural and Food Engineering from the Indian Institute of Technology, Kharagpur. He is a Data Science and Machine learning enthusiast who wants to integrate these leading technologies into the agricultural domain and solve challenges.Divyesh Vitthal Jawkhedehttps://www.marktechpost.com/author/divyesh-jawkhede/Ola: A State-of-the-Art Omni-Modal Understanding Model with Advanced Progressive Modality Alignment StrategyDivyesh Vitthal Jawkhedehttps://www.marktechpost.com/author/divyesh-jawkhede/How AI Chatbots Mimic Human Behavior: Insights from Multi-Turn Evaluations of LLMsDivyesh Vitthal Jawkhedehttps://www.marktechpost.com/author/divyesh-jawkhede/Google DeepMind Research Introduces WebLI-100B: Scaling Vision-Language Pretraining to 100 Billion Examples for Cultural Diversity and MultilingualitDivyesh Vitthal Jawkhedehttps://www.marktechpost.com/author/divyesh-jawkhede/Vintix: Scaling In-Context Reinforcement Learning for Generalist AI Agents

Addressing the evolving challenges in software engineering starts with recognizing that traditional benchmarks often fall short. Real-world freelance software engineering is complex, involving much more than isolated coding tasks. Freelance engineers work on entire codebases, integrate diverse systems, and manage intricate client requirements. Conventional evaluation methods, which typically emphasize unit tests, miss critical aspects such as full-stack performance and the real monetary impact of solutions. This gap between synthetic testing and practical application has driven the need for more realistic evaluation methods.OpenAI introduces SWE-Lancer, a benchmark for evaluating model performance on real-world freelance software engineering work. The benchmark is based on over 1,400 freelance tasks sourced from Upwork and the Expensify repository, with a total payout of $1 million USD. Tasks range from minor bug fixes to major feature implementations. SWE-Lancer is designed to evaluate both individual code patches and managerial decisions, where models are required to select the best proposal from multiple options. This approach better reflects the dual roles found in real engineering teams.One of SWE-Lancers key strengths is its use of end-to-end tests rather than isolated unit tests. These tests are carefully crafted and verified by professional software engineers. They simulate the entire user workflowfrom issue identification and debugging to patch verification. By using a unified Docker image for evaluation, the benchmark ensures that every model is tested under the same controlled conditions. This rigorous testing framework helps reveal whether a models solution would be robust enough for practical deployment.The technical details of SWE-Lancer are thoughtfully designed to mirror the realities of freelance work. Tasks require modifications across multiple files and integrations with APIs, and they span both mobile and web platforms. In addition to producing code patches, models are challenged to review and select among competing proposals. This dual focus on technical and managerial skills reflects the true responsibilities of software engineers. The inclusion of a user tool that simulates real user interactions further enhances the evaluation by encouraging iterative debugging and adjustment.Results from SWE-Lancer offer valuable insights into the current capabilities of language models in software engineering. In individual contributor tasks, models such as GPT-4o and Claude 3.5 Sonnet achieved pass rates of 8.0% and 26.2%, respectively. In managerial tasks, the best model reached a pass rate of 44.9%. These numbers suggest that while state-of-the-art models can offer promising solutions, there is still considerable room for improvement. Additional experiments indicate that allowing more attempts or increasing test-time compute can meaningfully enhance performance, particularly on more challenging tasks.In conclusion, SWE-Lancer presents a thoughtful and realistic approach to evaluating AI in software engineering. By directly linking model performance to real monetary value and emphasizing full-stack challenges, the benchmark provides a more accurate picture of a models practical capabilities. This work encourages a move away from synthetic evaluation metrics toward assessments that reflect the economic and technical realities of freelance work. As the field continues to evolve, SWE-Lancer serves as a valuable tool for researchers and practitioners alike, offering clear insights into both current limitations and potential avenues for improvement. Ultimately, this benchmark helps pave the way for safer and more effective integration of AI into the software engineering process.Check outthePaper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/Scale AI Research Introduces J2 Attackers: Leveraging Human Expertise to Transform Advanced LLMs into Effective Red TeamersAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Guide to Setting Up a Custom BPE Tokenizer with Tiktoken for Advanced NLP Applications in PythonAsif Razzaqhttps://www.marktechpost.com/author/6flvq/LG AI Research Releases NEXUS: An Advanced System Integrating Agent AI System and Data Compliance Standards to Address Legal Concerns in AI DatasetsAsif Razzaqhttps://www.marktechpost.com/author/6flvq/KAIST and DeepAuto AI Researchers Propose InfiniteHiP: A Game-Changing Long-Context LLM Framework for 3M-Token Inference on a Single GPU Recommended Open-Source AI Platform: IntellAgent is a An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System' (Promoted)

Large language models have demonstrated remarkable problem-solving capabilities and mathematical and logical reasoning. These models have been applied to complex reasoning tasks, including International Mathematical Olympiad (IMO) combinatorics problems, Abstraction and Reasoning Corpus (ARC) puzzles, and Humanitys Last Exam (HLE) questions. Despite improvements, existing AI models often struggle with high-level problem-solving that requires abstract reasoning, formal verification, and adaptability. The growing demand for AI-driven problem-solving has led researchers to develop novel inference techniques that combine multiple methods and models to enhance accuracy and reliability.The challenge with AI reasoning lies in verifying the correctness of solutions, particularly for mathematical problems requiring multiple steps and logical deductions. Traditional models perform well in straightforward arithmetic but struggle when faced with abstract concepts, formal proofs, and high-dimensional reasoning. An effective AI system must generate valid solutions while adhering to established mathematical principles. Current limitations have prompted researchers to explore advanced inference techniques that improve verification and enhance problem-solving reliability.Several techniques have been implemented to address mathematical reasoning challenges. Zero-shot learning enables models to solve problems without prior exposure, while best-of-N sampling selects the most accurate solution from multiple generated responses. Monte Carlo Tree Search (MCTS) explores possible solutions through simulation, and theorem-proving software like Z3 assists in verifying logical statements. Despite their utility, these methods often lack robustness when faced with intricate problems requiring structured verification. This gap has led to the developing of a more comprehensive framework that integrates multiple inference strategies.A team of researchers from Boston University, Google, Columbia University, MIT, Intuit, and Stanford introduced an innovative approach that combines diverse inference techniques with automatic verification. The research integrates test-time simulations, reinforcement learning, and meta-learning to enhance reasoning performance. By leveraging multiple models and problem-solving methodologies, the approach ensures that AI systems are not reliant on a single technique, thus increasing accuracy and adaptability. The system employs structured agent graphs to refine problem-solving pathways and adjust inference strategies based on task complexity.The methodology revolves around verifying solutions for mathematical and logical problems through automated checks. For IMO problems, researchers implemented eight distinct methods, including LEAP, Z3, Monte Carlo Tree Search, and Plan Search, to translate English-based solutions into formal proofs within the Lean theorem-proving environment. This allows for absolute verification of correctness. ARC puzzles are addressed using synthesized code solutions, validated through unit testing against training examples. HLE questions involving broader reasoning categories leverage best-of-N sampling as an imperfect verifier to improve solution selection. Reinforcement learning and test-time meta-learning refine the inference process by adjusting agent graph representations based on prior problem-solving performance.The performance of this approach demonstrated substantial improvements across multiple reasoning tasks. For IMO combinatorics problems, accuracy increased from 33.3% to 77.8%, showcasing a significant leap in AI capabilities for mathematical proof generation. Regarding HLE questions, accuracy rose from 8% to 37%, indicating enhanced problem-solving adaptability across multiple disciplines. The ARC puzzles, known for their complexity, saw an 80% success rate for previously unsolved problems attempted by 948 human participants. Further, the model successfully solved 26.5% of ARC puzzles that OpenAIs o3 high-compute model failed to address. The research highlights the effectiveness of combining multiple inference models, demonstrating that aggregated methodologies outperform single-method approaches in complex reasoning tasks.This study presents a transformative advancement in AI-driven reasoning by merging diverse inference strategies with automated verification systems. By leveraging multiple AI techniques and optimizing reasoning pathways through reinforcement learning, the research offers a scalable solution to complex problem-solving challenges. The results demonstrate that an AI systems performance can be significantly enhanced through structured inference aggregation, paving the way for more sophisticated reasoning models in the future. This work contributes to AIs broader application in mathematical problem-solving and logical verification, addressing fundamental challenges that have limited AIs effectiveness in advanced reasoning tasks.Check outthePaper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/Stanford Researchers Introduced a Multi-Agent Reinforcement Learning Framework for Effective Social Deduction in AI CommunicationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper from IBM and MIT Introduces SOLOMON: A Neuro-Inspired Reasoning Network for Enhancing LLM Adaptability in Semiconductor Layout DesignNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper from Apple Introduces a Distillation Scaling Law: A Compute-Optimal Approach for Training Efficient Language ModelsNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper from UC Berkeley Introduces a Data-Efficient Approach to Long Chain-of-Thought Reasoning for Large Language Models Recommended Open-Source AI Platform: IntellAgent is a An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System' (Promoted)

Artificial intelligence in multi-agent environments has made significant strides, particularly in reinforcement learning. One of the core challenges in this domain is developing AI agents capable of communicating effectively through natural language. This is particularly critical in settings where each agent has only partial visibility of the environment, making knowledge-sharing essential for achieving collective goals. Social deduction games provide an ideal framework for testing AIs ability to deduce information through conversations, as these games require reasoning, deception detection, and strategic collaboration.A key issue in AI-driven social deduction is ensuring that agents can conduct meaningful discussions without relying on human demonstrations. Many language models falter in multi-agent settings due to their dependence on vast datasets of human conversations. The challenge intensifies as AI agents struggle to assess whether their contributions meaningfully impact decision-making. Without a clear mechanism to evaluate the usefulness of their messages, they often generate unstructured and ineffective communication, leading to suboptimal performance in strategic games that require deduction and persuasion.Existing reinforcement learning approaches attempt to address this problem but frequently fall short. Some techniques depend on pre-existing datasets of human interactions, which are not always available or adaptable to new scenarios. Others incorporate language models with reinforcement learning but fail due to sparse feedback, which makes it difficult for AI to refine its dialogue strategies. Traditional methods cannot thus systematically improve communication skills over time, making AI discussions in multi-agent environments less effective.A research team from Stanford University introduced an innovative method for training AI agents in social deduction settings without human demonstrationstheir approach leverages multi-agent reinforcement learning to develop AI capable of understanding and articulating meaningful arguments. The research focuses on the game *Among Us*, where crewmates must identify an imposter through verbal discussions. The researchers designed a training mechanism that divides communication into listening and speaking, allowing the AI to optimize both skills independently. The method integrates a structured reward system that progressively enables agents to refine their discussion techniques.The methodology introduces a dense reward signal that provides precise feedback to improve communication. AI agents enhance their listening abilities by predicting environmental details based on prior discussions. At the same time, their speaking proficiency improves through reinforcement learning, where messages are assessed based on their impact on other agents beliefs. This structured approach ensures that AI-generated messages are logical, persuasive, and relevant to the conversation. The research team employed RWKV, a recurrent neural network model, as the foundation for their training, optimizing it for long-form discussions and dynamic gameplay environments.Experimental results demonstrated that this training approach significantly improved AI performance compared to traditional reinforcement learning techniques. The trained AI exhibited behaviors akin to human players, including suspect accusation, evidence presentation, and reasoning based on observed actions. The study showed that AI models utilizing this structured discussion learning framework achieved a win rate of approximately 56%, compared to the 28% win rate of reinforcement learning models without the structured dialogue framework. Furthermore, the AI trained using this method outperformed models four times larger in size, underscoring the efficiency of the proposed training strategy. When analyzing discussion behaviors, the research team observed that the AI could accurately identify imposters at a success rate twice as high as baseline reinforcement learning approaches.Further analysis revealed that AI models trained under this framework adapted effectively to adversarial strategies. Imposters attempted to manipulate discussions by shifting blame, initially confusing AI crewmates. However, the AI agents learned to differentiate between genuine accusations and misleading statements through iterative training. Researchers found that AI-generated messages that explicitly named a suspect were more likely to influence group decisions. This emergent behavior closely resembled human intuition, indicating that the AI could adapt discussion strategies dynamically.This research marks a significant advancement in AI-driven social deduction. By addressing the communication challenges in multi-agent settings, the study provides a structured and effective framework for training AI agents to engage in meaningful discussions without relying on extensive human demonstrations. The proposed method enhances AI decision-making, allowing for more persuasive and logical reasoning in environments that require collaboration and the detection of deception. The research opens possibilities for broader applications, including AI assistants capable of analyzing complex discussions, negotiating, and strategizing in real-world scenarios.Check outthePaper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper from IBM and MIT Introduces SOLOMON: A Neuro-Inspired Reasoning Network for Enhancing LLM Adaptability in Semiconductor Layout DesignNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper from Apple Introduces a Distillation Scaling Law: A Compute-Optimal Approach for Training Efficient Language ModelsNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper from UC Berkeley Introduces a Data-Efficient Approach to Long Chain-of-Thought Reasoning for Large Language ModelsNikhilhttps://www.marktechpost.com/author/nikhil0980/Meta AI Introduces PARTNR: A Research Framework Supporting Seamless Human-Robot Collaboration in Multi-Agent Tasks Recommended Open-Source AI Platform: IntellAgent is a An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System' (Promoted)

Transforming language models into effective red teamers is not without its challenges. Modern large language models have transformed the way we interact with technology, yet they still struggle with preventing the generation of harmful content. Efforts such as refusal training help these models deny risky requests, but even these safeguards can be bypassed with carefully designed attacks. This ongoing tension between innovation and security remains a critical issue in deploying these systems responsibly.In practice, ensuring safety means contending with both automated attacks and human-crafted jailbreaks. Human red teamers often devise sophisticated multi-turn strategies that expose vulnerabilities in ways that automated techniques sometimes miss. However, relying solely on human expertise is resource intensive and lacks the scalability required for widespread application. As a result, researchers are exploring more systematic and scalable methods to assess and strengthen model safety.Scale AI Research introduces J2 attackers to address these challenges. In this approach, a human red teamer first jailbreaks a refusal-trained language model, encouraging it to bypass its own safeguards. This transformed model, now referred to as a J2 attacker, is then used to systematically test vulnerabilities in other language models. The process unfolds in a carefully structured manner that balances human guidance with automated, iterative refinement.The J2 method begins with a manual phase where a human operator provides strategic prompts and specific instructions. Once the initial jailbreak is successful, the model enters a multi-turn conversation phase where it refines its tactics using feedback from previous attempts. This blend of human expertise and the models own in-context learning abilities creates a feedback loop that continuously improves the red teaming process. The result is a measured and methodical system that challenges existing safeguards without resorting to sensationalism.The technical framework behind J2 attackers is thoughtfully designed. It divides the red teaming process into three distinct phases: planning, attack, and debrief. During the planning phase, detailed prompts break down conventional refusal barriers, allowing the model to prepare its approach. The subsequent attack phase consists of a series of controlled, multi-turn dialogues with the target model, each cycle refining the strategy based on prior outcomes.In the debrief phase, an independent evaluation is conducted to assess the success of the attack. This feedback is then used to further adjust the models tactics, fostering a cycle of continuous improvement. By modularly incorporating diverse red teaming strategiesfrom narrative-based fictionalization to technical prompt engineeringthe approach maintains a disciplined focus on security without overhyping its capabilities.Empirical evaluations of the J2 attackers reveal encouraging, yet measured, progress. In controlled experiments, models like Sonnet-3.5 and Gemini-1.5-pro achieved attack success rates of around 93% and 91% against GPT-4o on the Harmbench dataset. These figures are comparable to the performance of experienced human red teamers, who averaged success rates close to 98%. Such results underscore the potential of an automated system to assist in vulnerability assessments while still relying on human oversight.Further insights show that the iterative planning-attack-debrief cycles play a crucial role in refining the process. Studies indicate that approximately six cycles tend to offer a balance between thoroughness and efficiency. An ensemble of multiple J2 attackers, each applying different strategies, further enhances overall performance by covering a broader spectrum of vulnerabilities. These findings provide a solid foundation for future work aimed at further stabilizing and improving the security of language models.In conclusion, the introduction of J2 attackers by Scale AI represents a thoughtful step forward in the evolution of language model safety research. By enabling a refusal-trained language model to facilitate red teaming, this approach opens new avenues for systematically uncovering vulnerabilities. The work is grounded in a careful balance between human guidance and automated refinement, ensuring that the method remains both rigorous and accessible.Check outthePaper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Guide to Setting Up a Custom BPE Tokenizer with Tiktoken for Advanced NLP Applications in PythonAsif Razzaqhttps://www.marktechpost.com/author/6flvq/LG AI Research Releases NEXUS: An Advanced System Integrating Agent AI System and Data Compliance Standards to Address Legal Concerns in AI DatasetsAsif Razzaqhttps://www.marktechpost.com/author/6flvq/KAIST and DeepAuto AI Researchers Propose InfiniteHiP: A Game-Changing Long-Context LLM Framework for 3M-Token Inference on a Single GPUAsif Razzaqhttps://www.marktechpost.com/author/6flvq/DeepSeek AI Introduces CODEI/O: A Novel Approach that Transforms Code-based Reasoning Patterns into Natural Language Formats to Enhance LLMs Reasoning Capabilities Recommended Open-Source AI Platform: IntellAgent is a An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System' (Promoted)

Recent discussions on AI safety increasingly link it to existential risks posed by advanced AI, suggesting that addressing safety inherently involves considering catastrophic scenarios. However, this perspective has drawbacks: it may exclude researchers with different approaches, mislead the public into thinking AI safety is solely about existential threats, and create resistance among skeptics. As AI rapidly advances, policymakers must establish regulatory frameworks and safety standards. While existential risks dominate the current discourse, past technological safety fieldssuch as aviation, pharmaceuticals, and cybersecurityhave developed robust engineering and governance practices. These frameworks could inform AI safety, ensuring reliable and responsible system deployment.Researchers from the University of Edinburgh and Carnegie Mellon University highlight that AI safety discussions often focus on existential risks, which may exclude diverse perspectives and mislead public perception. Their systematic review of peer-reviewed research reveals a broad spectrum of safety concerns, including adversarial robustness and interpretability, aligning with traditional system safety practices. The study suggests integrating near-term and long-term risks rather than prioritizing existential threats. While AI safety research evolves rapidly, capturing relevant studies remains challenging. Expanding discourse to incorporate established engineering safety principles can help address immediate and future AI risks effectively.The researchers systematically reviewed AI safety literature using a structured methodology based on Kitchenham and Charters guidelines, complemented by snowball sampling to capture emerging research. They focused on two key research questions: identifying risks across the AI system lifecycle and evaluating proposed mitigation strategies. Their search process involved querying the Web of Science (WoS) and Scopus databases, refining results through hierarchical filters, and supplementing findings with influential seed papers. The review process included screening 2,666 database papers and 117 from snowball sampling, ultimately selecting 383 for analysis. Papers were annotated with metadata such as author affiliations, publication year, and citation count and were categorized based on methodological approaches, specific safety concerns addressed, and risk mitigation strategies.The studys bibliometric analysis revealed a steady increase in AI safety research since 2016, driven by advancements in deep learning. A word cloud analysis highlighted key themes such as safe reinforcement learning, adversarial robustness, and domain adaptation. A co-occurrence graph of abstract terms identified four major research clusters: (1) human and societal implications of AI, focusing on trust, accountability, and safety assurance; (2) safe reinforcement learning, emphasizing robust agent control in uncertain environments; (3) supervised learning, particularly in classification tasks, with a focus on robustness, generalization, and accuracy; and (4) adversarial attacks and defense strategies in deep learning models. The findings suggest that AI safety research aligns with traditional safety engineering principles, integrating aspects of reliability engineering, control theory, and cybersecurity to ensure AI systems are both effective and secure.AI safety research categorizes risks into eight types: noise, lack of monitoring, system misspecification, and adversarial attacks. Most studies address issues related to noise and outliers, affecting model robustness and generalization. A significant focus is also on monitoring failures, system misspecifications, and control enforcement gaps. Research methods include applied algorithms, simulated agents, analysis frameworks, and mechanistic interpretability. While theoretical works propose conceptual models, applied studies develop practical algorithms. Recent efforts emphasize reinforcement learning safety, adversarial robustness, and explainability. The field parallels traditional engineering safety, integrating verification techniques to enhance AI reliability and mitigate potential risks.In conclusion, the study systematically reviewed peer-reviewed literature to explore AI safety challenges. The findings highlight diverse motivations and research outcomes aimed at ensuring AI systems are reliable and beneficial. AI safety research addresses various risks, including design flaws, robustness issues, inadequate monitoring, and embedded biases. The study advocates for framing AI safety within broader technological safety, expanding stakeholder engagement, and promoting inclusive research. While existential risks remain relevant, a wider perspective fosters productive discourse. Future research should explore sociotechnical AI safety and incorporate non-peer-reviewed sources for a comprehensive understanding, ensuring AI safety remains an evolving, inclusive, and multidisciplinary field.Check outthePaper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/Nous Research Released DeepHermes 3 Preview: A Llama-3-8B Based Model Combining Deep Reasoning, Advanced Function Calling, and Seamless Conversational IntelligenceSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Layer Parallelism: Enhancing LLM Inference Efficiency Through Parallel Execution of Transformer LayersSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Can 1B LLM Surpass 405B LLM? Optimizing Computation for Small LLMs to Outperform Larger ModelsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Meet OpenThinker-32B: A State-of-the-Art Open-Data Reasoning Model Recommended Open-Source AI Platform: IntellAgent is a An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System' (Promoted)

Graph generation is a complex problem that involves constructing structured, non-Euclidean representations while maintaining meaningful relationships between entities. Most current methods fail to capture higher-order interactions, like motifs and simplicial complexes, required for molecular modeling, social network analysis, and protein design applications. Diffusion-based methods, first developed for image synthesis, have been popularized widely in the application domain but tend to lose important topological information. The rapid decay of structural dependencies throughout diffusion leads to unrealistic graph outputs. Second, traditional methods add isotropic Gaussian noise to adjacency matrices, which destroys key properties like sparsity and connectivity. To overcome these issues, an approach with higher-order structural guidance throughout graph generation is needed to preserve higher topological integrity.Current models for graph generation are based on methods like recurrent neural networks, variational autoencoders, and generative adversarial networks. While such methods can learn structural properties, they are computationally expensive and lack scalability. Recently, diffusion-based frameworks have been proposed that improve graphs step by step progressively. While such models provide some improvement, they are inherently designed for continuous image data and thus fail to capture graphs discrete and hierarchical nature. One major weakness of current methods is the destruction of meaningful structure in adjacency matrices after a few steps of diffusion, resulting in random, unrealistic representations of graphs. Further, the models are often not equivariant because they fail to preserve consistency when permuting nodes, leading to an inaccurate estimation of graph distributions.To address these challenges, HOG-Diff introduces a systematic approach based on a coarse-to-fine learning paradigm that progressively refines graphs in a way that maintains critical topological features. By decoupling the generation process into successive steps, the method first builds higher-order graph skeletons followed by the refinement of pairwise relations and intricate details. An intermediate-stage diffusion bridge mechanism maintains properly organized intermediate steps and realistic intermediate representations without any loss of detailed topological features. In contrast to traditional approaches, which act by manipulating adjacency matrices, this paradigm leverages spectral diffusion with noise injection in the eigenvalue space of the Laplacian matrix. This process damps excessive modification of connectivity patterns, leading to more structurally coherent outputs. Additionally, the model architecture uses graph convolutional networks integrated with graph transformer networks to learn localized relationships and global dependencies to enable improved model performance in general.The generative process uses a structured multi-stage architecture where each stage refines the graph without eliminating its higher-order features. Elimination of unhelpful nodes and edges through a filtering process using cell complexes is utilized to enable this controlled graph construction. The diffusion process is controlled by a Generalized Ornstein-Uhlenbeck bridge, ensuring through mathematical means a smooth transition from one structural arrangement to another. Spectral diffusion replaces the traditional method of noise injection in the adjacency matrix by injecting perturbations into the eigenvalue space of the Laplacian matrix, preserving important connectivity and sparsity patterns. The model architecture provides a balance between the preservation of local and global structure by using the integration of graph convolutional and transformer networks for the capture of informative features across scales.Large-scale experimentation verifies that HOG-Diff attains better performance than state-of-the-art approaches on both molecular and generic graph generation tasks. In the context of applications in molecular space, this model performs to a remarkable degree in major similarity measures, such as lower Neighborhood Subgraph Pairwise Distance Kernel and Frchet ChemNet Distance scores, thus reflecting higher consistency with realistic molecular distributions. Higher validity, uniqueness, and novelty scores further demonstrate its capability to generate chemically meaningful structures. Apart from the specific context of molecular graphs, the model also demonstrates unparalleled capability in abstracting complex topological dependencies in generic datasets, attaining lower error rates in degree distribution, clustering coefficient, and orbit structure accuracy. Maintenance of higher-order features during generative transformation leads to generating graphs that not only attain realism but are also structurally stable, providing a more reliable solution than existing practices.With the integration of higher-order structural information directly into the generative model, HOG-Diff offers an improved solution for graph synthesis overcoming the limitations of traditional diffusion models. The integration of a coarse-to-fine generation strategy, diffusion bridge operations, and spectral diffusion ensures the generated graphs maintain topological fidelity and semantic correctness. Large-scale evaluation on diverse datasets confirms its capability to generate high-quality graphs with improved structural correctness. Systematic exploration of diverse topological guides improves explainability, making this framework a valuable tool in applications from drug discovery and urban modeling to network science. Maintenance of advanced graph structures, this method demonstrates an important advance in deep generative modeling over structured data.Check outthePaper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Aswin AkAswin AK is a consulting intern at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, bringing a strong academic background and hands-on experience in solving real-life cross-domain challenges.Aswin Akhttps://www.marktechpost.com/author/aswinak/Can Users Fix AI Bias? Exploring User-Driven Value Alignment in AI CompanionsAswin Akhttps://www.marktechpost.com/author/aswinak/Anthropic AI Launches the Anthropic Economic Index: A Data-Driven Look at AIs Economic RoleAswin Akhttps://www.marktechpost.com/author/aswinak/Meet Huginn-3.5B: A New AI Reasoning Model with Scalable Latent ComputationAswin Akhttps://www.marktechpost.com/author/aswinak/LLMDet: How Large Language Models Enhance Open-Vocabulary Object Detection Recommended Open-Source AI Platform: IntellAgent is a An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System' (Promoted)

Large Language Models (LLMs) have shown exceptional capabilities in complex reasoning tasks through recent advancements in scaling and specialized training approaches. While models like OpenAI o1 and DeepSeek R1 have set new benchmarks in addressing reasoning problems, a significant disparity exists in their performance across different languages. The dominance of English and Chinese in training data for foundation models like Llama and Qwen has created a substantial capability gap for low-resource languages. However, these models face challenges such as incorrect character usage and code-switching. These issues become pronounced during reasoning-focused fine-tuning and reinforcement learning processes.Regional LLM initiatives have emerged to address low-resource language limitations through specialized pretraining and post-training approaches. Projects like Typhoon, Sailor, EuroLLM, Aya, Sea-lion, and SeaLLM have focused on adapting models for specific target languages. However, the data-centric approach to adapting reasoning capabilities lacks transparency in reasoning model data recipes. Moreover, scaling requires substantial computational resources, as evidenced by DeepSeek R1 70Bs requirement of 800K examples for distillation and general SFT, far exceeding academic efforts like Sky-T1 and Bespoke-Stratos. Model merging has emerged as an alternative approach, showing promise in combining multiple specialized models weights to improve performance across tasks without additional training.Researchers from SCB 10X R&D and SCBX Group Bangkok, Thailand have proposed an innovative approach to enhance reasoning capabilities in language-specific LLMs, particularly focusing on Thai language models. The research combines data selection and model merging methods to incorporate advanced reasoning capabilities similar to DeepSeek R1 while maintaining target language proficiency. The study addresses the critical challenge of improving reasoning abilities in low-resource language models, using only publicly available datasets and a modest computational budget of $1,201, matching DeepSeek R1s reasoning capabilities without compromising performance on target language tasks.The implemented methodology utilizes Typhoon2 70B Instruct and DeepSeek R1 70B Distill as base models. The approach involves applying Supervised Fine-Tuning (SFT) to Typhoon2 70B and merging it with DeepSeek R1 70B. The training configuration employs LoRA with specific parameters: rank 32 and of 16. The system uses sequence packing with 16,384 maximum lengths, alongside Liger kernels, FlashAttention-2, and DeepSpeed ZeRO-3 to optimize computational efficiency. Training runs on 4H100 GPUs for up to 15 hours using axolotl4, with model merging performed via Mergekit. The evaluation focuses on two key aspects: reasoning capability and language task performance, utilizing benchmarks like AIME 2024, MATH-500, and LiveCodeBench, with Thai translations for assessment.Experimental results reveal that DeepSeek R1 70B Distill excels in reasoning tasks like AIME and MATH500 but shows reduced effectiveness in Thai-specific tasks such as MTBench-TH and language accuracy evaluations. Typhoon2 70B Instruct shows strong performance in language-specific tasks but struggles with reasoning challenges, achieving only 10% accuracy in AIME and trailing DeepSeek R1 by over 20% in MATH500. The final model, Typhoon2-R1-70B combines DeepSeek R1s reasoning capabilities with Typhoon2s Thai language proficiency, achieving performance within 4% of Typhoon2 on language tasks while maintaining comparable reasoning abilities. This results in performance improvements of 41.6% over Typhoon2 and 12.8% over DeepSeek R1.In conclusion, researchers present an approach to enhance reasoning capabilities in language-specific models, through the combination of specialized models. While the study proves that SFT and model merging can effectively transfer reasoning capabilities with limited resources, several limitations exist in the current methodology. The research scope was confined to merging DARE in a two-model setup within a single model family, without optimizing instruction tuning despite available high-quality datasets like Tulu3. Significant challenges persist in multilingual reasoning and model merging including the lack of culturally aware reasoning traces. Despite these challenges, the research marks a step toward advancing LLM capabilities in underrepresented languages.Check outthePaper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Sajjad AnsariSajjad Ansari is a final year undergraduate from IIT Kharagpur. As a Tech enthusiast, he delves into the practical applications of AI with a focus on understanding the impact of AI technologies and their real-world implications. He aims to articulate complex AI concepts in a clear and accessible manner.Sajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/TransMLA: Transforming GQA-based Models Into MLA-based ModelsSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Microsoft Research Introduces Data Formulator: An AI Application that Leverages LLMs to Transform Data and Create Rich VisualizationsSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/ByteDance Introduces UltraMem: A Novel AI Architecture for High-Performance, Resource-Efficient Language ModelsSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Adaptive Inference Budget Management in Large Language Models through Constrained Policy Optimization Recommended Open-Source AI Platform: IntellAgent is a An Open-Source Multi-Agent Framework to Evaluate Complex Conversational AI System' (Promoted)

In this tutorial, well learn how to create a custom tokenizer using the tiktoken library. The process involves loading a pre-trained tokenizer model, defining both base and special tokens, initializing the tokenizer with a specific regular expression for token splitting, and testing its functionality by encoding and decoding some sample text. This setup is essential for NLP tasks requiring precise control over text tokenization.from pathlib import Pathimport tiktokenfrom tiktoken.load import load_tiktoken_bpeimport jsonHere, we import several libraries essential for text processing and machine learning. It uses Path from pathlib for easy file path management, while tiktoken and load_tiktoken_bpe facilitate loading and working with a Byte Pair Encoding tokenizer.tokenizer_path = "./content/tokenizer.model"num_reserved_special_tokens = 256mergeable_ranks = load_tiktoken_bpe(tokenizer_path)num_base_tokens = len(mergeable_ranks)special_tokens = [ "<|begin_of_text|>", "<|end_of_text|>", "<|reserved_special_token_0|>", "<|reserved_special_token_1|>", "<|finetune_right_pad_id|>", "<|step_id|>", "<|start_header_id|>", "<|end_header_id|>", "<|eom_id|>", "<|eot_id|>", "<|python_tag|>",]Here, we set the path to the tokenizer model, specifying 256 reserved special tokens. It then loads the mergeable ranks, which form the base vocabulary, calculates the number of base tokens, and defines a list of special tokens for marking text boundaries and other reserved purposes.reserved_tokens = [ f"<|reserved_special_token_{2 + i}|>" for i in range(num_reserved_special_tokens - len(special_tokens))]special_tokens = special_tokens + reserved_tokenstokenizer = tiktoken.Encoding( name=Path(tokenizer_path).name, pat_str=r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^rnp{L}p{N}]?p{L}+|p{N}{1,3}| ?[^sp{L}p{N}]+[rn]*|s*[rn]+|s+(?!S)|s+", mergeable_ranks=mergeable_ranks, special_tokens={token: len(mergeable_ranks) + i for i, token in enumerate(special_tokens)},)Now, we dynamically create additional reserved tokens to reach 256, then append them to the predefined special tokens list. It initializes the tokenizer using tiktoken. Encoding with a specified regular expression for splitting text, the loaded mergeable ranks as the base vocabulary, and mapping special tokens to unique token IDs.#-------------------------------------------------------------------------# Test the tokenizer with a sample text#-------------------------------------------------------------------------sample_text = "Hello, this is a test of the updated tokenizer!"encoded = tokenizer.encode(sample_text)decoded = tokenizer.decode(encoded)print("Sample Text:", sample_text)print("Encoded Tokens:", encoded)print("Decoded Text:", decoded)We test the tokenizer by encoding a sample text into token IDs and then decoding those IDs back into text. It prints the original text, the encoded tokens, and the decoded text to confirm that the tokenizer works correctly.tokenizer.encode("Hey")Here, we encode the string Hey into its corresponding token IDs using the tokenizers encoding method.In conclusion, following this tutorial will teach you how to set up a custom BPE tokenizer using the TikToken library. You saw how to load a pre-trained tokenizer model, define both base and special tokens, and initialize the tokenizer with a specific regular expression for token splitting. Finally, you verified the tokenizers functionality by encoding and decoding sample text. This setup is a fundamental step for any NLP project that requires customized text processing and tokenization.Here is the Colab Notebook for the above project. Also,dont forget to follow us onTwitterand join ourTelegram ChannelandLinkedIn Group. Dont Forget to join our75k+ ML SubReddit. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/LG AI Research Releases NEXUS: An Advanced System Integrating Agent AI System and Data Compliance Standards to Address Legal Concerns in AI DatasetsAsif Razzaqhttps://www.marktechpost.com/author/6flvq/KAIST and DeepAuto AI Researchers Propose InfiniteHiP: A Game-Changing Long-Context LLM Framework for 3M-Token Inference on a Single GPUAsif Razzaqhttps://www.marktechpost.com/author/6flvq/DeepSeek AI Introduces CODEI/O: A Novel Approach that Transforms Code-based Reasoning Patterns into Natural Language Formats to Enhance LLMs Reasoning CapabilitiesAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Google DeepMind Researchers Propose Matryoshka Quantization: A Technique to Enhance Deep Learning Efficiency by Optimizing Multi-Precision Models without Sacrificing Accuracy [Recommended] Join Our Telegram Channel

After the advent of LLMs, AI Research has focused solely on the development of powerful models day by day. These cutting-edge new models improve users experience across various reasoning, content generation tasks, etc. However, trust in the results and the underlying reasoning used by these models have recently been in the spotlight. In developing these models, the quality of the data, its compliance, and associated legal risks have become key concerns, as the models output depends on the underlying dataset.LG AI Research, a pioneer in the AI field with previous successful launches of the EXAONE Models, has developed an Agent AI to address the above concerns. The Agent AI tracks the life cycle of training datasets to be used in AI models, comprehensively analyzing legal risks and assessing potential threats related to a dataset. LG AI Research has also introduced NEXUS, where users can directly explore results generated by this Agent AI system.LG AI Research focuses on the training data underlying AI models. This is concerning because AI has been rapidly expanding into various sectors, and the biggest concern is its legal, safe, and ethical advancement. Through this research, LG AI Research found that AI training datasets are redistributed many times, and a dataset is sometimes linked to hundreds of datasets, making it impossible for a human being to track its sources. This lack of transparency can give rise to some serious legal and compliance risks.Through its offering of an Agent AI embedded in NEXUS, LG AI Research is tracking complex datasets lifecycle to ensure data compliance. The team has achieved this through its robust Agent AI, which can automatically find and analyze complex layers and dataset relationships. They developed this Agent AI system using a comprehensive data compliance framework and their EXAONE 3.5 model. The Agent AI system comprises three core modules, and each has been fine-tuned differently:The Navigation Module: This module is extensively trained to navigate web documents and analyze AI-generated text data. It performs navigation based on the name and type of the entity to find links to web pages or license documents related to the entity.The QA Module: In this module, the model was trained to take collected documents as input and extract dependency and license information from the documents.The Scoring Module: Finally, it was trained using a refined dataset labeled by lawyers, which analyzes license details alongside an entitys metadata to evaluate and quantify potential legal risks.Through this robust development, Agent AI has achieved 45 times faster speed than a human expert at a cost cheaper than 700 times.Other notable results include: when evaluating 216 randomly chosen datasets from Hugging Faces top 1,000+ downloads, Agent AI accurately detected dependencies by around 81.04% and identified license documents by about 95.83%.In this Agent AI, the legal risk assessment for datasets is based on the data compliance framework developed by LG AI Research. This data compliance framework uses 18 key factors: license grants, data modification rights, derivative works permissions, potential copyright infringement in outputs, and privacy considerations. Each factor is weighted according to real-world disputes and case law, ensuring practical, reliable risk assessments. After this, data compliance results are classified into a seven-level risk rating system, where A-1 is the highest, requiring explicit commercial use permission or public domain status, plus consistent rights for all sub-datasets. A-2 to B-2 allows limited use, often free for research but restricted commercially. C-1 to C-2 carry higher risk due to unclear licenses, rights issues, or privacy concerns.The research on NEXUS has set a new standard for the legal stability of AI training datasets. LG AI Research envisions a long way forward; they have conducted an in-depth analysis of 3,612 major datasets through NEXUS and found that the inconsistency of rights relationships between datasets and dependencies is far higher than expected. Many of these datasets with inconsistencies are used for major AI models in widespread use. For example, of the 2,852 AI training datasets determined to be commercially available, only 605 (21.21%) remained commercially available after accounting for dependency risks.Recognizing these real-world issues, LG AI Research has several future goals for evolving AI technology and the legal environment. The first immediate goal is to expand the scope and depth of the datasets that Agent AI technology analyzes, aiming to understand the life cycle of all the data worldwide and maintain the quality of assessment and results throughout this expansion. Another vision is to evolve the data compliance framework into a global standard. LG AI Research plans to collaborate with the worldwide AI community and legal experts to develop these criteria into an international standard. Finally, in the long term, LG AI Research plans to evolve NEXUS into a comprehensive legal risk management system for AI developers, contributing to creating a safe, legal, data-compliant, and responsible AI ecosystem.Sources:Thanks tothe LG AI Research teamfor the thought leadership/ Resources for this article.LG AI Research team has supported us in this content/article. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/KAIST and DeepAuto AI Researchers Propose InfiniteHiP: A Game-Changing Long-Context LLM Framework for 3M-Token Inference on a Single GPUAsif Razzaqhttps://www.marktechpost.com/author/6flvq/DeepSeek AI Introduces CODEI/O: A Novel Approach that Transforms Code-based Reasoning Patterns into Natural Language Formats to Enhance LLMs Reasoning CapabilitiesAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Google DeepMind Researchers Propose Matryoshka Quantization: A Technique to Enhance Deep Learning Efficiency by Optimizing Multi-Precision Models without Sacrificing AccuracyAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Salesforce AI Research Introduces Reward-Guided Speculative Decoding (RSD): A Novel Framework that Improves the Efficiency of Inference in Large Language Models (LLMs) Up To 4.4 Fewer FLOPs [Recommended] Join Our Telegram Channel

In large language models (LLMs), processing extended input sequences demands significant computational and memory resources, leading to slower inference and higher hardware costs. The attention mechanism, a core component, further exacerbates these challenges due to its quadratic complexity relative to sequence length. Also, maintaining the previous context using a key-value (KV) cache results in high memory overheads, limiting scalability.A key limitation of LLMs is their inability to handle sequences longer than their trained context window. Most models degrade in performance when faced with extended inputs due to inefficient memory management and growing attention computation costs. Existing solutions often rely on fine-tuning, which is resource-intensive and requires high-quality long-context datasets. Without an efficient method for context extension, tasks like document summarization, retrieval-augmented generation, and long-form text generation remain constrained.Several approaches have been proposed to tackle the problem of long-context processing. FlashAttention2 (FA2) optimizes memory consumption by minimizing redundant operations during attention computation, yet it does not address computational inefficiency. Some models employ selective token attention, either statically or dynamically, to reduce processing overhead. KV cache eviction strategies have been introduced to remove older tokens selectively, but they risk permanently discarding important contextual information. HiP Attention is another approach that attempts to offload infrequently used tokens to external memory; however, it lacks efficient cache management, leading to increased latency. Despite these advances, no method has effectively addressed all three key challenges:Long-context generalizationEfficient memory managementComputational efficiencyResearchers from the KAIST, and DeepAuto.ai introduced InfiniteHiP, an advanced framework that enables efficient long-context inference while mitigating memory bottlenecks. The model achieves this through a hierarchical token pruning algorithm, which dynamically removes less relevant context tokens. This modular pruning strategy selectively retains tokens that contribute the most to attention computations, significantly reducing processing overhead. The framework also incorporates adaptive RoPE (Rotary Positional Embeddings) adjustments, allowing models to generalize to longer sequences without additional training. Also, InfiniteHiP employs a novel KV cache offloading mechanism, transferring less frequently accessed tokens to host memory while ensuring efficient retrieval. These techniques enable the model to process up to 3 million tokens on a 48GB GPU, making it the most scalable long-context inference method.The core innovation of InfiniteHiP is its multi-stage pruning mechanism, which consistently improves context selection throughout multiple stages. Tokens are first divided into fixed-length pieces, and each piece is processed based on its attention computation contribution. A top-K selection approach ensures that only the most critical tokens are retained and others are dropped. The method followed by InfiniteHiP, unlike other hierarchical pruning models, is entirely parallelized, which renders it computationally effective. The KV cache management system optimizes memory utilization by dynamically offloading less important context tokens while maintaining retrieval flexibility. The model also utilizes multiple RoPE interpolation methods at different attention layers, thus facilitating smooth adaptation to long sequences.The model demonstrates an 18.95 speedup in attention decoding for a one million-token context compared to traditional methods without additional training. The KV cache offloading technique reduces GPU memory consumption by up to 96%, making it practical for large-scale applications. In benchmark evaluations such as LongBench and Bench, InfiniteHiP consistently outperforms state-of-the-art methods, achieving a 9.99% higher relative score than InfLLM. Also, decoding throughput is increased by 3.2 on consumer GPUs (RTX 4090) and 7.25 on enterprise-grade GPUs (L40S).In conclusion, the research team successfully addressed the major bottlenecks of long-context inference with InfiniteHiP. The framework enhances LLM capabilities by integrating hierarchical token pruning, KV cache offloading, and RoPE generalization. This breakthrough enables pre-trained models to process extended sequences without losing context or increasing computational costs. The method is scalable, hardware-efficient, and applicable to various AI applications requiring long-memory retention.Check outthePaper, Source Code and Live Demo.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/DeepSeek AI Introduces CODEI/O: A Novel Approach that Transforms Code-based Reasoning Patterns into Natural Language Formats to Enhance LLMs Reasoning CapabilitiesAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Google DeepMind Researchers Propose Matryoshka Quantization: A Technique to Enhance Deep Learning Efficiency by Optimizing Multi-Precision Models without Sacrificing AccuracyAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Salesforce AI Research Introduces Reward-Guided Speculative Decoding (RSD): A Novel Framework that Improves the Efficiency of Inference in Large Language Models (LLMs) Up To 4.4 Fewer FLOPsAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Meta AI Introduces CoCoMix: A Pretraining Framework Integrating Token Prediction with Continuous Concepts [Recommended] Join Our Telegram Channel

Adapting large language models for specialized domains remains challenging, especially in fields requiring spatial reasoning and structured problem-solving, even though they specialize in complex reasoning. Semiconductor layout design is a prime example, where AI tools must interpret geometric constraints and ensure precise component placement. Researchers are developing advanced AI architectures to enhance LLMs ability to process and apply domain-specific knowledge effectively.A major limitation of general-purpose LLMs is their inability to convert theoretical knowledge into practical solutions. While these models can accurately define technical concepts, they often fail when solving real-world tasks that require spatial reasoning and structured logic. In semiconductor layout design, AI must go beyond text-based knowledge to ensure accurate placement of vias, metal layers, and circuit components. Without precise geometric relationships, layout designs may fail due to misalignment or incorrect spacing. Current models often require multiple rounds of human correction, making their deployment inefficient.Several approaches have been developed to improve LLMs adaptability for domain-specific applications. Fine-tuning involves training LLMs with domain-specific data, but this process is time-intensive and requires significant computational resources. Retrieval-augmented generation (RAG) retrieves external knowledge to guide LLM outputs, but it does not fully address challenges related to structured problem-solving. In-context learning helps guide LLM reasoning by providing task-specific examples, yet it does not overcome spatial reasoning limitations. These methods offer incremental improvements but fail to deliver a comprehensive solution for applications requiring geometric logic.Researchers at IBM T.J. Watson Research Center and MIT-IBM Watson AI Lab introduced SOLOMON, a neuro-inspired LLM reasoning network, to enhance domain-specific adaptability. Unlike conventional approaches, SOLOMON employs a multi-agent reasoning system that dynamically processes spatial constraints and geometric relationships. The framework integrates thought assessment mechanisms to refine outputs iteratively, improving problem-solving accuracy. SOLOMON leverages prompt engineering techniques to guide LLM-generated solutions, allowing it to adapt to semiconductor layout tasks with minimal retraining.The architecture of SOLOMON is inspired by neuroscience and incorporates the Free Energy Principle, which optimizes reasoning by reducing discrepancies between expected and observed outcomes. The framework consists of three primary components: Thought Generators, Thought Assessors, and a Steering Subsystem. Thought Generators utilize diverse LLMs to produce multiple reasoning pathways, ensuring a broad range of solutions for complex tasks. The Thought Assessor evaluates these outputs, selecting the most logical and structured approach. The Steering Subsystem allows researchers to modify objectives dynamically, enabling more precise domain adaptation. Unlike fine-tuning, this architecture does not require continuous retraining, making it more efficient for specialized applications.Researchers conducted experiments on 25 semiconductor layout tasks to evaluate SOLOMONs effectiveness. The framework was compared to five baseline LLMs, including GPT-4o, Claude-3.5-Sonnet, and Llama-3 models. Each task assessed the models ability to generate geometric structures while maintaining spatial accuracy. SOLOMON demonstrated improvements in reducing runtime errors and scaling inaccuracies. The framework exhibited better spatial reasoning capabilities, improving placement precision and reducing mistakes in generated designs. SOLOMON instances also matched or exceeded the performance of o1-preview in multiple test categories, with the Claude-based SOLOMON performing strongly in certain complex tasks.A key advantage of SOLOMON is its ability to correct logical inconsistencies and arithmetic errors in geometric designs. The Thought Assessor continuously refines generated layouts by analyzing previous iterations, mitigating common hallucination issues in traditional LLMs. The system effectively reduces misinterpretations and enhances the reliability of AI-generated designs. SOLOMON synchronizes reasoning across multiple LLMs when presented with ambiguous layout specifications, ensuring consistent and precise output. By incorporating hierarchical assessment mechanisms, the framework significantly improves AI-driven design accuracy.This research highlights the importance of enhancing LLM reasoning capabilities rather than increasing model size. SOLOMON offers a structured and efficient approach for applying AI to domain-specific problem-solving, particularly in semiconductor layout design. Future research will focus on expanding the framework to other engineering applications, refining multimodal reasoning capabilities, and introducing iterative learning mechanisms to enhance AI decision-making. The introduction of SOLOMON represents a substantial advancement in making AI-driven tools more precise, adaptive, and effective for real-world industrial challenges.Check outthePaper.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our75k+ ML SubReddit. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper from Apple Introduces a Distillation Scaling Law: A Compute-Optimal Approach for Training Efficient Language ModelsNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper from UC Berkeley Introduces a Data-Efficient Approach to Long Chain-of-Thought Reasoning for Large Language ModelsNikhilhttps://www.marktechpost.com/author/nikhil0980/Meta AI Introduces PARTNR: A Research Framework Supporting Seamless Human-Robot Collaboration in Multi-Agent TasksNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance [Recommended] Join Our Telegram Channel