Large Language Models (LLMs) have become pivotal in artificial intelligence, powering a variety of applications from chatbots to content generation tools. However, their deployment at scale presents notable challenges. High computational costs, latency, and energy consumption often limit their wider use. Organizations face the difficulty of balancing high throughput with reasonable operating expenses. Additionally, as models grow larger, the need for more efficient solutions becomes increasingly urgent. Addressing these issues is essential to making LLMs more practical and accessible.Snowflake AI Research team introduces SwiftKV, a solution designed to enhance LLM inference throughput while reducing associated costs. SwiftKV uses key-value caching techniques to reuse intermediate computations during inference. By eliminating redundant calculations, it streamlines the inference process and makes LLM deployments more efficient.SwiftKVs design targets the computational intensity of LLMs. Conventional inference pipelines often recompute identical operations for multiple requests, resulting in inefficiencies. SwiftKV introduces a caching layer that identifies and stores reusable computational results. This approach accelerates inference and reduces resource requirements, making it a practical choice for organizations aiming to optimize their AI operations.Technical Details and Key Benefits of SwiftKVSwiftKV incorporates a key-value memory system into the LLM inference architecture. Its operation can be summarized as follows:Key-Value Caching: During inference, SwiftKV captures intermediate activations (keys) and their corresponding results (values). For similar queries, it retrieves the precomputed values rather than recalculating them.Efficient Storage Management: The caching mechanism employs strategies such as least recently used (LRU) eviction to manage memory effectively, ensuring that the cache remains useful without excessive resource consumption.Seamless Integration: SwiftKV is compatible with existing LLM frameworks, such as Hugging Faces Transformers and Metas LLaMA, enabling easy adoption without significant changes to existing pipelines.The benefits of SwiftKV include:Cost Reduction: By avoiding redundant computations, SwiftKV significantly cuts inference costs. Snowflake AI Research reports up to a 75% reduction in costs in some scenarios.Enhanced Throughput: The caching mechanism reduces inference time, improving response speed.Energy Savings: Lower computational demands translate into reduced energy consumption, supporting sustainable AI practices.Scalability: SwiftKV is well-suited for large-scale deployments, meeting the needs of enterprises expanding their AI capabilities.https://www.snowflake.com/en/blog/up-to-75-lower-inference-cost-llama-meta-llm/ResultsSnowflake AI Researchs evaluations of SwiftKV provide valuable insights into its effectiveness. For example, integrating SwiftKV with Metas LLaMA models led to up to a 75% reduction in inference costs without any compromise in accuracy or performance. These outcomes highlight the efficiency gains possible with this approach.Additionally, tests demonstrate significant reductions in inference latency, even for larger models. The caching system ensures that complex queries benefit from faster processing times. This combination of cost efficiency and performance optimization makes SwiftKV a compelling choice for organizations aiming to scale AI solutions affordably.The open-sourcing of SwiftKV encourages collaboration within the AI community. By sharing this technology, Snowflake AI Research invites developers, researchers, and enterprises to explore and enhance its capabilities, fostering innovation in LLM efficiency.https://www.snowflake.com/en/blog/up-to-75-lower-inference-cost-llama-meta-llm/Conclusion: A Step Forward in LLM EfficiencySwiftKV offers a thoughtful solution to the challenges of deploying LLMs at scale. By tackling high computational costs and latency, it helps make AI applications more practical and accessible. The incorporation of key-value caching into inference pipelines showcases how targeted optimizations can drive significant improvements.As the field of AI progresses, tools like SwiftKV will continue to shape the development of efficient and sustainable technologies. Its open-source nature ensures that the broader community can contribute to its growth and application. By enabling more cost-effective and scalable use of LLMs, SwiftKV underscores the importance of innovation in making AI truly transformative for businesses and developers alike.Check out the Details and GitHub Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit. Asif RazzaqAsif 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Have you ever admired how smartphone cameras isolate the main subject from the background, adding a subtle blur to the background based on depth? This portrait mode effect gives photographs a professional look by simulating shallow depth-of-field similar to DSLR cameras. In this tutorial, well recreate this effect programmatically using open-source computer vision models, like SAM2 from Meta and MiDaS from Intel ISL.To build our pipeline, well use:Segment Anything Model (SAM2): To segment objects of interest and separate the foreground from the background.Depth Estimation Model: To compute a depth map, enabling depth-based blurring.Gaussian Blur: To blur the background with intensity varying based on depth.Step 1: Setting Up the EnvironmentTo get started, install the following dependencies:pip install matplotlib samv2 pytest opencv-python timm pillowStep 2: Loading a Target ImageChoose a picture to apply this effect and load it into Python using the Pillow library.from PIL import Imageimport numpy as npimport matplotlib.pyplot as pltimage_path = "<path to your image>.jpg"img = Image.open(image_path)img_array = np.array(img)# Display the imageplt.imshow(img)plt.axis("off")plt.show()Step 3: Initialize the SAM2To initialize the model, download the pretrained checkpoint. SAM2 offers four variants based on performance and inference speed: tiny, small, base_plus, and large. In this tutorial, well use tiny for faster inference.Download the model checkpoint from:Replace <model_type> with your desired model type.from sam2.build_sam import build_sam2from sam2.sam2_image_predictor import SAM2ImagePredictorfrom sam2.utils.misc import variant_to_config_mappingfrom sam2.utils.visualization import show_masksmodel = build_sam2( variant_to_config_mapping["tiny"], "sam2_hiera_tiny.pt",)image_predictor = SAM2ImagePredictor(model)Step 4: Feed Image into SAM and Select the SubjectSet the image in SAM and provide points that lie on the subject you want to isolate. SAM predicts a binary mask of the subject and background.image_predictor.set_image(img_array)input_point = np.array([[2500, 1200], [2500, 1500], [2500, 2000]])input_label = np.array([1, 1, 1])masks, scores, logits = image_predictor.predict( point_coords=input_point, point_labels=input_label, box=None, multimask_output=True,)output_mask = show_masks(img_array, masks, scores)sorted_ind = np.argsort(scores)[::-1]Step 5: Initialize the Depth Estimation ModelFor depth estimation, we use MiDaS by Intel ISL. Similar to SAM, you can choose different variants based on accuracy and speed.Note: The predicted depth map is reversed, meaning larger values correspond to closer objects. Well invert it in the next step for better intuitiveness.import torchimport torchvision.transforms as transformsmodel_type = "DPT_Large" # MiDaS v3 - Large (highest accuracy)# Load MiDaS modelmodel = torch.hub.load("intel-isl/MiDaS", model_type)model.eval()# Load and preprocess imagetransform = torch.hub.load("intel-isl/MiDaS", "transforms").dpt_transforminput_batch = transform(img_array)# Perform depth estimationwith torch.no_grad(): prediction = model(input_batch) prediction = torch.nn.functional.interpolate( prediction.unsqueeze(1), size=img_array.shape[:2], mode="bicubic", align_corners=False, ).squeeze()prediction = prediction.cpu().numpy()# Visualize the depth mapplt.imshow(prediction, cmap="plasma")plt.colorbar(label="Relative Depth")plt.title("Depth Map Visualization")plt.show()Step 6: Apply Depth-Based Gaussian BlurHere we optimize the depth-based blurring using an iterative Gaussian blur approach. Instead of applying a single large kernel, we apply a smaller kernel multiple times for pixels with higher depth values.import cv2def apply_depth_based_blur_iterative(image, depth_map, base_kernel_size=7, max_repeats=10): if base_kernel_size % 2 == 0: base_kernel_size += 1 # Invert depth map depth_map = np.max(depth_map) - depth_map # Normalize depth to range [0, max_repeats] depth_normalized = cv2.normalize(depth_map, None, 0, max_repeats, cv2.NORM_MINMAX).astype(np.uint8) blurred_image = image.copy() for repeat in range(1, max_repeats + 1): mask = (depth_normalized == repeat) if np.any(mask): blurred_temp = cv2.GaussianBlur(blurred_image, (base_kernel_size, base_kernel_size), 0) for c in range(image.shape[2]): blurred_image[..., c][mask] = blurred_temp[..., c][mask] return blurred_imageblurred_image = apply_depth_based_blur_iterative(img_array, prediction, base_kernel_size=35, max_repeats=20)# Visualize the resultplt.figure(figsize=(20, 10))plt.subplot(1, 2, 1)plt.imshow(img)plt.title("Original Image")plt.axis("off")plt.subplot(1, 2, 2)plt.imshow(blurred_image)plt.title("Depth-based Blurred Image")plt.axis("off")plt.show()Step 7: Combine Foreground and BackgroundFinally, use the SAM mask to extract the sharp foreground and combine it with the blurred background.def combine_foreground_background(foreground, background, mask): if mask.ndim == 2: mask = np.expand_dims(mask, axis=-1) return np.where(mask, foreground, background)mask = masks[sorted_ind[0]].astype(np.uint8)mask = cv2.resize(mask, (img_array.shape[1], img_array.shape[0]))foreground = img_arraybackground = blurred_imagecombined_image = combine_foreground_background(foreground, background, mask)plt.figure(figsize=(20, 10))plt.subplot(1, 2, 1)plt.imshow(img)plt.title("Original Image")plt.axis("off")plt.subplot(1, 2, 2)plt.imshow(combined_image)plt.title("Final Portrait Mode Effect")plt.axis("off")plt.show()ConclusionWith just a few tools, weve recreated the portrait mode effect programmatically. This technique can be extended for photo editing applications, simulating camera effects, or creative projects.Future Enhancements:Use edge detection algorithms for better refinement of subject edges.Experiment with kernel sizes to enhance the blur effect.Create a user interface to upload images and select subjects dynamically.Resources:Segment anything model by META (https://github.com/facebookresearch/sam2)CPU compatible implementation of SAM 2 (https://github.com/SauravMaheshkar/samv2/tree/main)MIDas Depth Estimation Model ( https://pytorch.org/hub/intelisl_midas_v2/) Vineet Kumar+ postsVineet Kumar is a consulting intern at MarktechPost. He is currently pursuing his BS from the Indian Institute of Technology(IIT), Kanpur. He is a Machine Learning enthusiast. He is passionate about research and the latest advancements in Deep Learning, Computer Vision, and related fields. Meet 'Height':The only autonomous project management tool (Sponsored)

AI and ML are expanding at a remarkable rate, which is marked by the evolution of numerous specialized subdomains. Recently, two core branches that have become central in academic research and industrial applications are Generative AI and Predictive AI. While they share foundational principles of machine learning, their objectives, methodologies, and outcomes differ significantly. This article will describe Generative AI and Predictive AI, drawing upon prominent academic papers.Defining Generative AIGenerative AI focuses on creating or synthesizing new data that resemble training samples in structure and style. The authenticity of this approach lies in its ability to learn the fundamental data distribution and generate novel instances that are not mere replicas. Ian Goodfellow et al. introduced the concept of Generative Adversarial Networks (GANs), where two neural networks, i.e., the generator and the discriminator, are trained simultaneously. The generator produces new data, while the discriminator evaluates whether the input is real or synthetic. GANs learn to produce highly realistic images, audio, and textual content through this adversarial setup.A parallel approach to generative modeling can be found in Variational Autoencoders (VAEs) proposed by Diederik P. Kingma and Max Welling. VAEs utilize an encoder to compress data into a latent representation and a decoder to reconstruct or generate new data from that latent space. The ability of VAEs to learn continuous latent representations has made them useful for various tasks, including image generation, anomaly detection, and even drug discovery. Over the years, refinements such as the Deep Convolutional GAN (DCGAN) by Radford et al. and improved training techniques for GANs by Salimans et al. have expanded the horizons of generative modeling.Defining Predictive AIPredictive AI is primarily concerned with forecasting or inferring outcomes based on historical data. Rather than learning to generate new data, these models aim to make accurate predictions. One of the earliest and widely recognized works in predictive modeling within deep learning is the Recurrent Neural Network (RNN) based language model by Tomas Mikolov, which demonstrated how predictive algorithms could capture sequential dependencies to predict future tokens in language tasks.Subsequent breakthroughs in Transformer-based architectures brought predictive capabilities to new heights. Notably, BERT (Bidirectional Encoder Representations from Transformers), introduced by Devlin et al., used a masked language modeling objective to excel at predictive tasks such as question answering and sentiment analysis. GPT-3 by Brown et al. further illustrated how large-scale language models can exhibit few-shot learning capabilities, refining predictive tasks with minimal labeled data. Although GPT-3 and its successors are sometimes called generative language models, their training objective, predicting the next token, aligns closely with predictive modeling. The difference lies in the scale of data and parameters, enabling them to generate coherent text while retaining strong predictive properties.Comparative AnalysisThe table below summarizes the primary differences between Generative AI and Predictive AI, highlighting key aspects.Research and Real-World ImplicationsGenerative AI has wide-ranging implications. In content creation, generative models can automate the production of artwork, video game textures, and synthetic media. Researchers have also explored medical and pharmaceutical applications, such as generating new molecular structures for drug discovery. Meanwhile, Predictive AI continues to dominate business intelligence, finance, and healthcare through demand forecasting, risk assessment, and medical diagnosis. Predictive models increasingly leverage large-scale, self-supervised pretraining to handle tasks with limited labeled data or to adapt to changing environments.Despite their differences, synergies between Generative AI and Predictive AI have begun to emerge. Some advanced models integrate generative and predictive components in a single framework, enabling tasks such as data augmentation to improve predictive performance or conditional generation to tailor outputs based on specific predictive features. This convergence indicates a future where generative models assist predictive tasks by creating synthetic training samples, and predictive models guide generative processes to ensure outputs align with intended objectives.ConclusionGenerative AI and Predictive AI each offer distinct strengths and face unique challenges. Generative AI shines when the objective is to produce new, realistic, and creative samples, whereas Predictive AI excels at providing accurate forecasts or classifications from existing data. Both paradigms continuously develop, drawing interest from researchers and practitioners who aim to refine the underlying algorithms, address existing limitations, and discover new applications. By examining the foundational work on Generative Adversarial Networks and Variational Autoencoders alongside predictive breakthroughs such as RNN-based language models and Transformers, it is evident that the evolution of AI hinges on both the generative and predictive axes.SourcesAlso,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit. Sana Hassan+ postsSana 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Large Language Models (LLMs) have made significant progress in natural language processing, excelling in tasks like understanding, generation, and reasoning. However, challenges remain. Achieving robust reasoning often requires extensive supervised fine-tuning, which limits scalability and generalization. Furthermore, issues like poor readability and balancing computational efficiency with reasoning complexity persist, prompting researchers to explore new approaches.DeepSeek-R1: A New Approach to LLM ReasoningDeepSeek-AIs recent work introduces DeepSeek-R1, a model designed to enhance reasoning capabilities through reinforcement learning (RL). This effort resulted in two models:DeepSeek-R1-Zero, which is trained solely with RL and demonstrates emergent reasoning behaviors such as long Chain-of-Thought (CoT) reasoning.DeepSeek-R1, which builds on its predecessor by incorporating a multi-stage training pipeline, addressing challenges like readability and language mixing while maintaining high reasoning performance.These models aim to overcome existing limitations, combining innovative RL techniques with structured training processes to achieve scalability and usability.Technical Innovations and Benefits1. Reinforcement Learning on Reasoning Tasks: DeepSeek-R1-Zero employs RL without relying on supervised data. Using Group Relative Policy Optimization (GRPO), it optimizes reasoning by evaluating multiple outputs, significantly improving benchmark performance. For example, its AIME 2024 pass@1 score rose from 15.6% to 71.0% during training.2. Multi-Stage Training in DeepSeek-R1: DeepSeek-R1 incorporates cold-start datathousands of curated CoT examplesto fine-tune its base model before undergoing reasoning-focused RL. This process ensures outputs are both coherent and user-friendly by incorporating language consistency rewards.3. Distillation for Smaller Models: To address computational constraints, DeepSeek-AI distilled six smaller models (1.5B to 70B parameters) from DeepSeek-R1 using Qwen and Llama architectures. These models retain strong reasoning capabilities, with the 14B distilled model achieving a pass@1 score of 69.7% on AIME 2024, outperforming some larger models.Results: Performance InsightsDeepSeek-R1s performance is supported by benchmark results:Reasoning Benchmarks:AIME 2024: 79.8% pass@1, surpassing OpenAIs o1-mini.MATH-500: 97.3% pass@1, comparable to OpenAI-o1-1217.GPQA Diamond: 71.5% pass@1, excelling in fact-based reasoning.Coding and STEM Tasks:Codeforces Elo rating: 2029, outperforming 96.3% of human participants.SWE-Bench Verified: 49.2% resolution rate, competitive with other leading models.General Capabilities:Strong generalization was demonstrated on ArenaHard and AlpacaEval 2.0 benchmarks, achieving 92.3% and 87.6% win rates, respectively.Distilled Model Highlights: Smaller models like DeepSeek-R1-Distill-Qwen-32B show strong performance, with a pass@1 score of 72.6% on AIME 2024, demonstrating effective scalability and practicality.Conclusion: Refining Reasoning in AIDeepSeek-AIs DeepSeek-R1 and DeepSeek-R1-Zero represent meaningful advancements in reasoning capabilities for LLMs. By leveraging RL, cold-start data, and distillation techniques, these models address critical limitations while promoting accessibility through open-source availability under the MIT License. The API (model=deepseek-reasoner) further enhances usability for developers and researchers.Looking ahead, DeepSeek-AI plans to refine multilingual support, enhance software engineering capabilities, and improve prompt sensitivity. These efforts aim to further establish DeepSeek-R1 as a robust solution for reasoning-focused AI applications. By integrating thoughtful training paradigms, DeepSeek-R1 illustrates how AI can advance toward addressing increasingly complex challenges.Check out the Paper, DeepSeek R1 and DeepSeek R1 Zero. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit. [Recommended Read] Nebius AI Studio expands with vision models, new language models, embeddings and LoRA (Promoted)The post DeepSeek-AI Releases DeepSeek-R1-Zero and DeepSeek-R1: First-Generation Reasoning Models that Incentivize Reasoning Capability in LLMs via Reinforcement Learning appeared first on MarkTechPost.

Large language models rely heavily on open datasets to train, which poses significant legal, technical, and ethical challenges in managing such datasets. There are uncertainties around the legal implications of using data based on varying copyright laws and changing regulations regarding safe usage. The lack of global standards or centralized databases to validate and license datasets and incomplete or inconsistent metadata makes it impossible to assess the legal status of works. Technical barriers also relate to access to digitized public domain material. Most open datasets are not governed and have not implemented any kind of legal safety net for their contributors, exposing them to dangers and making them impossible to scale up. While intended to create more transparency and collaborative work, they do little or nothing to engage broader social challenges such as diversity and accountability and often exclude underrepresented languages and viewpoints.Current methods of building open datasets for LLMs often lack clear legal frameworks and face significant technical, operational, and ethical challenges. Traditional methods depend on incomplete metadata, complicating verifying copyright status and compliance across different regions with different laws. Digitization of public domain materials and making them accessible is challenging because big projects like Google Books restrict usage, which prevents the construction of open datasets. Volunteer-driven projects lack structured governance, which exposes the contributors to legal risks. Such gaps prevent equal access, prevent diversity in data representation, and concentrate power in a few dominant organizations. This creates an ecosystem where open datasets struggle to compete with proprietary models, reducing accountability and slowing progress toward transparent and inclusive AI development.To mitigate issues in metadata encoding, data sourcing, and processing for machine learning datasets, researchers proposed a framework focused on building a reliable corpus using openly licensed and public domain data for training large language models (LLMs). The framework emphasizes overcoming technical challenges like ensuring reliable metadata and digitizing physical records. It promotes cross-domain cooperation to responsibly curate, govern, and release these datasets while promoting competition in the LLM ecosystem. It also emphasizes metadata standards, reproducibility for accountability, and ensuring data source diversity as an alternative to more traditional methods lacking structured governance and transparency.Researchers included all the practical steps of sourcing, processing, and governing datasets. Tools for detecting openly licensed content were used to ensure high-quality data. The framework integrated standards for metadata consistency, emphasized digitization, and encouraged collaboration with communities to create datasets. It also supported transparency and reproducibility in preprocessing and addressed potential biases and harmful content in a robust and inclusive system for training LLMs while reducing legal risks. The framework also highlights engaging with underrepresented communities to build diverse datasets and create clearer, machine-readable terms of Use. Additionally, making the open data ecosystem sustainable should come through proposed funding models on public funding from both tech companies and cultural institutions to ensure sustainable participation.Finally, the researchers provided a clear scenario with a broadly outlined plan on how to approach the issues discussed within the context of training LLMs on non-licensed data, with a focus on the openness of the datasets and the efforts made by different spheres. Initiatives such as emphasizing metadata standardization, enhancing the digitization process, and responsible governance were intended to make the artificial intelligence ecosystem more open. The works build the foundation for future works where further probing into newer innovations in dataset management, AI governance, and advancements of the technologies that enhance the accessibility of data while addressing the problem of ethical and legal challenges.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit. Divyesh Vitthal Jawkhede+ postsDivyesh 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Traditional psychological counseling, often conducted in person, remains limited to individuals actively seeking help for psychological concerns. In contrast, online automated counseling presents a viable option for those hesitant to pursue therapy due to stigma or shame. Cognitive Behavioral Therapy (CBT), a widely practiced approach in psychological counseling, aims to help individuals identify and correct cognitive distortions contributing to negative emotions and behaviors. The emergence of LLMs has opened new possibilities for automating CBT diagnosis and treatment. However, current LLM-based CBT systems face challenges such as fixed structural frameworks, which limit adaptability and self-optimization, and repetitive response patterns that provide generic, unhelpful suggestions.Recent advancements in AI have introduced frameworks like CBT-LLM, which employs prompt-based learning, and CoCoA, which integrates memory mechanisms for retrieval-augmented generation. These systems aim to identify and address cognitive distortions in user statements while enhancing the depth and relevance of therapeutic interactions. Despite their potential, existing methods often lack personalization, adaptability to changing user needs, and a nuanced understanding of dynamic therapeutic processes. To bridge these gaps, ongoing research uses annotated datasets, ontologies, and advanced LLMs to develop context-aware CBT systems that mimic human cognitive processes.Researchers from the Shenzhen Key Laboratory for High-Performance Data Mining, Shenzhen Institutes of Advanced Technology, the Chinese Academy of Sciences, and several other institutions developed AutoCBT, an autonomous multi-agent framework designed for CBT in single-turn psychological consultations. Utilizing Quora-like and YiXinLi models, AutoCBT integrates dynamic routing and memory mechanisms to improve response quality and adaptability. The framework undergoes structured reasoning and editing to generate high-quality, context-aware outputs. Evaluated on a bilingual dataset, it outperforms traditional LLM-based systems, addressing challenges like dynamic routing, supervisory mechanisms, and Llamas over-protection issue.AutoCBT is a versatile framework designed for multi-agent systems in CBT, comprising a Counsellor Agent (interface), Supervisor Agents, communication topology, and routing strategies. The Counsellor Agent, powered by LLMs, interacts with users and seeks input from Supervisor Agents to generate confident, high-quality responses. Agents feature memory mechanisms for short-term and long-term storage, and routing strategies like unicast and broadcast enable dynamic communication. AutoCBT incorporates CBT principlesempathy, belief identification, reflection, strategy, and encouragementmapped to specific Supervisor Agents. Its effectiveness was validated using a bilingual dataset combining PsyQA and TherapistQA, categorized and augmented with cognitive distortion examples.In online psychological counseling, LLMs like Qwen-2.5-72B and Llama-3.1-70B were evaluated for handling emotional nuances and instruction adherence. AutoCBT, a two-stage framework, outperformed Generation and PromptCBT by incorporating dynamic routing and supervisory mechanisms, achieving higher scores across empathy, cognitive distortion handling, and response relevance. AutoCBTs iterative approach enhanced its draft responses, which were validated by automatic and human evaluations. Challenges included routing conflicts, role confusion, and redundant feedback loops, mitigated through design adjustments. Llamas over-caution led to frequent refusals on sensitive topics, unlike Qwen, which responded comprehensively, highlighting the importance of balance in model sensitivity.In conclusion, AutoCBT is an innovative multi-agent framework designed for CBT-based psychological counseling. By integrating dynamic routing and supervisory mechanisms, AutoCBT addresses limitations in traditional LLM-based counseling, significantly enhancing response quality and effectiveness in identifying and addressing cognitive distortions. AutoCBT achieves superior dialogue quality through its adaptive and autonomous design compared to static, prompt-based systems. Challenges in LLMs semantic understanding and instruction adherence were identified and mitigated through targeted solutions. Leveraging bilingual datasets and models, the framework demonstrates its potential to deliver high-quality, automated counseling services. It offers a scalable alternative for individuals hesitant to pursue traditional therapy due to stigma.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit. Sana Hassan+ postsSana 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Swarm is an innovative open-source framework designed to explore the orchestration and coordination of multi-agent systems. It is developed and managed by the OpenAI Solutions team, and it provides a lightweight, ergonomic, and educational environment for developers to learn and experiment with agent-based systems. At its core, Swarm is built to facilitate the interaction of autonomous Agents, i.e., independent units capable of performing specific tasks, through streamlined handoffs and routine management. While primarily aimed at educational use, the framework introduces patterns and abstractions that make multi-agent orchestration more accessible and comprehensible. By focusing on simplicity and modularity, Swarm allows users to design workflows where Agents can collaborate, delegate tasks, and share contextual data seamlessly. OpenAIs Chat Completions API entirely powers it; Swarm operates statelessly, ensuring security and flexibility. With no official support or production readiness, Swarm is a learning platform.Core Components of SwarmSwarm is built on fundamental components that provide a strong foundation for flexibility and functionality. These components include:AgentsAgents are the primary units in Swarm, each representing an independent actor or step in a process. They include:Instructions: Define the Agents behavior or task.Functions: Specify actions the Agent can perform, including function calls.Handoffs: Allow the Agent to delegate its task to another Agent.Agents are initialized as follows:# pythonfrom swarm import Agentagent_a = Agent( name="Agent A", instructions="You are a general-purpose assistant.", functions=[] # Add any callable functions here)HandoffsHandoffs enable one Agent to pass control to another seamlessly. This allows specialized Agents to handle tasks better suited to their capabilities.# pythonagent_b = Agent( name="Agent B", instructions="You only provide answers in haikus.")agent_a = Agent( name="Agent A", instructions="Forward this task to Agent B.", functions=[lambda: agent_b] # Hand off to agent_b)Context VariablesContext variables store shared data across Agents, ensuring continuity in multi-agent workflows.# pythoncontext = {"user_name": "John"}response = client.run( agent=agent_a, messages=[{"role": "user", "content": "Who am I speaking with?"}], context_variables=context)How Swarm WorksAt its core, Swarm processes interactions using a structured loop implemented in its client.run() method. The loop involves the following steps:Message Processing: The current Agent processes the users message, which may generate a response or call a function.Function Execution: If the Agent includes function calls, these are executed, and the results are added to the conversation.Agent Switching: If the task requires another Agent, Swarm handles the handoff, ensuring seamless execution.Context Management: Context variables are updated throughout the interaction, ensuring shared data is accessible across Agents.Response Delivery: Swarm delivers the final response to the user after completing all steps.The basic workflow is illustrated below:# pythonfrom swarm import Swarm# Initialize the Swarm clientclient = Swarm()# Run the processresponse = client.run( agent=agent_a, messages=[{"role": "user", "content": "What can you do?"}])print(response.messages[-1]["content"])Usage of Swarm Code ImplementationInstallationSwarm can be installed directly from its GitHub repository:# bashpip install git+https://github.com/openai/swarm.gitBasic SetupSetting up Swarm involves importing the library, creating Agents, and running the interaction loop.# pythonfrom swarm import Swarm, Agent# Initialize Swarm clientclient = Swarm()# Define Agentsagent_a = Agent( name="Agent A", instructions="Provide general assistance.")agent_b = Agent( name="Agent B", instructions="Respond to all queries in poetic form.")# Interactionresponse = client.run( agent=agent_a, messages=[{"role": "user", "content": "Who am I speaking to?"}])print(response.messages[-1]["content"])Advanced FeaturesSwarm supports advanced features, including streaming responses and debugging.Streaming Responses:# pythonstream = client.run( agent=agent_a, messages=[{"role": "user", "content": "Stream a response"}], stream=True)for chunk in stream: print(chunk)Debugging:# pythonresponse = client.run( agent=agent_a, messages=[{"role": "user", "content": "Debug this process"}], debug=True)Download Colab NotebookConclusion:Swarm is an ergonomic, lightweight, and educational open-source framework that lets developers try out patterns and techniques essential for scalable agent orchestration. Although not meant for production, its focus on accessibility, modularity, and testability makes it a valuable resource for learning and prototyping. Its ability to support complex workflows through simple abstractions, such as Agents, handoffs, and context variables, allows developers to design effective solutions without being overwhelmed by technical complexities.Sourceshttps://github.com/openai/swarmhttps://colab.research.google.com/drive/1uFquKQvXLpKeP05OD507UFl8d0YvhM1t?authuser=1 Asif RazzaqAsif 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Reconstructing unmeasured causal drivers of complex time series from observed response data represents a fundamental challenge across diverse scientific domains. Latent variables, including genetic regulators or environmental factors, are essential to determining a systems dynamics but are rarely measured. Challenges with current approaches arise from data noise, the systems high dimensionality, and existing algorithms capacities in handling nonlinear interactions. This will greatly help in modeling, predicting, and controlling high-dimensional systems in systems biology, ecology, and fluid dynamics.The most widely used techniques for causal driver reconstruction usually rely on signal processing or machine learning frameworks. Some common ones include mutual information methods, neural network applications, and dynamic attractor reconstruction. While these techniques work well in some situations, they have significant limitations. Most demand large, high-quality datasets that are rarely found in real-world applications. They are very prone to measurement noise, resulting in low reconstruction accuracy. Some require computationally expensive algorithms and thus not suited for real-time applications. In addition, many models lack physical principles, reducing their interpretability and applicability across domains.The researchers from The University of Texas introduce a physics-based unsupervised learning framework called SHREC (Shared Recurrences) to reconstruct causal drivers from time series data. The approach is based on the theory of skew-product dynamical systems and topological data analysis. Innovation includes the use of recurrence events in time series to infer common causal structures between responses, the construction of a consensus recurrence graph that is traversed to expose the dynamics of the latent driver, and the introduction of a new network embedding that adapts to noisy and sparse datasets using fuzzy simplicial complexes. Unlike the existing methods, the SHREC framework well captures noisy and nonlinear data, requires minimal parameter tuning, and provides useful insight into the physical dynamics underlying driver-response systems.The SHREC algorithm is implemented in multiple stages. The measured response time series are mapped into weighted recurrence networks by topological embeddings, where an affinity matrix is constructed for each time series based on nearest neighbor distances and adaptive thresholds. The recurrence graphs are combined from individual time series to obtain a consensus graph that captures collective dynamics. Discrete-time drivers have been linked to decomposition by community detection algorithms, including the Leiden method, to provide distinct equivalence classes. For continuous drivers, on the other hand, the graphs Laplacian decomposition reveals transient modes corresponding to states of drivers. The algorithm was tested on diverse data: gene expression, plankton abundances, and turbulent flows. It showed excellent reconstruction of drivers under challenging conditions like high noise and missing data. The structure of the framework is based on graph-based representations. Therefore, it avoids costly iterative gradient-based optimization and makes it computationally efficient.SHREC performed notably well and consistently on the benchmark-challenging datasets. The methodology successfully reconstructed causal determinants from gene expression datasets, thereby uncovering essential regulatory components, even in the presence of sparse and noisy data. In experiments involving turbulent flow, this approach successfully detected sinusoidal forcing factors, demonstrating superiority over traditional signal processing techniques. Regarding ecological datasets, SHREC revealed temperature-induced trends in plankton populations, notwithstanding considerable missing information, thus illustrating its resilience to incomplete and noisy data. The comparison with other approaches has highlighted SHRECs increased accuracy and efficiency in computation, especially in the presence of higher noise levels and complex nonlinear dependencies. These findings highlight its extensive applicability and reliability in many fields.SHREC is a physics-based unsupervised learning framework that enables the reconstruction of unobserved causal drivers from complex time series data. This new approach deals with the severe drawbacks of contemporary techniques, which include noise susceptibility and high computational cost, by using recurrence structures and topological embeddings. The successful workability of SHREC on diverse datasets underlines its wide-ranging applicability with the ability to improve AI-based modeling in biology, physics, and engineering disciplines. This methodology improves the accuracy of causal driver reconstruction and, at the same time, puts in place a framework based on the principles of dynamical systems theory and sheds new light on essential characteristics of information transfer within interconnected systems.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit. Aswin Ak+ postsAswin 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Generative models have revolutionized fields like language, vision, and biology through their ability to learn and sample from complex data distributions. While these models benefit from scaling up during training through increased data, computational resources, and model sizes, their inference-time scaling capabilities face significant challenges. Specifically, diffusion models, which excel in generating continuous data like images, audio, and videos through a denoising process, encounter limitations in performance improvement when simply increasing the number of function evaluations (NFE) during inference. The traditional approach of adding more denoising steps prevents these models from achieving better results despite additional computational investment.Various approaches have been explored to enhance the performance of generative models during inference. Test-time compute scaling has proven effective for LLMs through improved search algorithms, verification methods, and compute allocation strategies. Researchers have pursued multiple directions in diffusion models including fine-tuning approaches, reinforcement learning techniques, and implementing direct preference optimization. Moreover, sample selection and optimization methods have been developed using Random Search algorithms, VQA models, and human preference models. However, these methods either focus on training-time improvements or limited test-time optimizations, leaving room for more detailed inference-time scaling solutions.Researchers from NYU, MIT, and Google have proposed a fundamental framework for scaling diffusion models during inference time. Their approach moves beyond simply increasing denoising steps and introduces a novel search-based methodology for improving generation performance through better noise identification. The framework operates along two key dimensions: utilizing verifiers for feedback and implementing algorithms to discover superior noise candidates. This approach addresses the limitations of conventional scaling methods by introducing a structured way to use additional computational resources during inference. The frameworks flexibility allows component combinations to be tailored to specific application scenarios.The frameworks implementation centers on class-conditional ImageNet generation using a pre-trained SiT-XL model with 256 256 resolution and a second-order Heun sampler. The architecture maintains a fixed 250 denoising steps while exploring additional NFEs dedicated to search operations. The core search mechanism employs a Random Search algorithm, implementing a Best-of-N strategy to select optimal noise candidates. The system utilizes two Oracle Verifiers for verification: Inception Score (IS) and Frchet Inception Distance (FID). IS selection is based on the highest classification probability from a pre-trained InceptionV3 model, while FID selection minimizes divergence against pre-calculated ImageNet Inception feature statistics.The frameworks effectiveness has been shown through comprehensive testing on different benchmarks. On DrawBench, which features diverse text prompts, the LLM Grader evaluation shows that searching with various verifiers consistently improves sample quality, though with different patterns across setups. ImageReward and Verifier Ensemble perform well, showing improvements across all metrics due to their nuanced evaluation capabilities and alignment with human preferences. The results reveal different optimal configurations on T2I-CompBench, focusing on text-prompt accuracy rather than visual quality. ImageReward emerges as the top performer, while Aesthetic Scores show minimal or negative impact, and CLIP provides modest improvements.In conclusion, researchers establish a significant advancement in the diffusion models by introducing a framework for inference-time scaling through strategic search mechanisms. The study shows that computational scaling via search methods can achieve substantial performance improvements across different model sizes and generation tasks, with varying computational budgets yielding distinct scaling behaviors. The research concludes that while the approach proves successful, it also reveals the inherent biases in different verifiers and emphasizes the importance of developing task-specific verification methods. This insight opens new avenues for future research in developing more targeted and efficient verification systems for various vision generation tasks.Check out the Paper and Project Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit. Sajjad Ansari+ postsSajjad 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Vision-language models (VLMs) play a crucial role in multimodal tasks like image retrieval, captioning, and medical diagnostics by aligning visual and linguistic data. However, understanding negation in these models remains one of the main challenges. Negation is critical for nuanced applications, such as distinguishing a room without windows from a room with windows. Despite their advancements, current VLMs fail to interpret negation reliably, severely limiting their effectiveness in high-stakes domains like safety monitoring and healthcare. Addressing this challenge is essential to expand their applicability in real-world scenarios.The current VLMs, such as CLIP, use shared embedding spaces to align visual and textual representations. Though these models excel in tasks such as cross-modal retrieval and image captioning, their performance falls sharply when dealing with negated statements. This limitation arises due to pretraining data biases because the training datasets contain mainly affirmative examples, leading to affirmation bias, where models treat negated and affirmative statements as equivalents. Existing benchmarks such as CREPE and CC-Neg rely on simplistic templated examples that dont represent the richness and depth of negation in natural language. VLMs tend to collapse the embeddings of negated and affirmative captions so it is extremely challenging to tease apart fine-grained differences between the concepts. This poses a problem in using VLMs for precise language understanding applications, for instance, querying a medical imaging database with complex inclusion and exclusion criteria.To address these limitations, researchers from MIT, Google DeepMind, and the University of Oxford proposed the NegBench framework for the evaluation and improvement of negation comprehension over VLMs. The framework assesses two fundamental tasks: Retrieval with Negation (Retrieval-Neg), which examines the models capacity to retrieve images according to both affirmative and negated specifications, such as a beach without people, and Multiple Choice Questions with Negation (MCQ-Neg), which evaluates nuanced comprehension by necessitating that models select appropriate captions from slight variations. It uses enormous synthetic datasets, like CC12M-NegCap and CC12M-NegMCQ, augmented with millions of captions that contain a wide range of negation scenarios. This will expose VLMs to somewhat challenging negatives and paraphrased captions, improving the training and evaluation of models. Standard datasets, such as COCO and MSR-VTT, were also adapted, including negated captions and paraphrases, to further expand linguistic diversity and test the robustness. By incorporating varied and complex negation examples, NegBench effectively overcomes existing limitations, significantly enhancing model performance and generalization.NegBench leverages both real and synthetic datasets to test negation comprehension. Datasets like COCO, VOC2007, and CheXpert were adapted to include negation scenarios, such as This image includes trees but not buildings. For MCQs, templates like This image includes A but not B were used alongside paraphrased variations for diversity. NegBench is further augmented with the HardNeg-Syn dataset, where images are synthesized to present pairs differing from each other based on the occurrence or absence of certain objects only, hence constituting difficult cases for negation understanding. Model fine-tuning relied on two training objectives. On one hand, contrastive loss facilitated the alignment between image-caption pairs, enhancing performance in retrieval. On the other hand, using multiple-choice loss helped in making fine-grained negation judgments by preferring the right captions in the MCQ context.The fine-tuned models showed considerable improvements in retrieval and comprehension tasks using the negation-enriched datasets. For retrieval, the models recall increases by 10% for negated queries, where performance is nearly at par with standard retrieval tasks. In the multiple-choice question tasks, accuracy improvements of up to 40% were reported, showing a better ability to differentiate between the subtle affirmative and negated captions. Advancements were uniform over a range of datasets, including COCO and MSR-VTT, and on synthetic datasets like HardNeg-Syn, where models handled negation and complex linguistic developments appropriately. This suggests that representing scenarios with diverse kinds of negation in training and testing is effective in reducing affirmation bias and generalization.NegBench addresses a critical gap in VLMs by being the first work to address their inability to understand negation. It brings significant improvements in retrieval and comprehension tasks by incorporating diverse negation examples into trAIning and evaluation. Such improvements open up avenues for much more robust AI systems that are capable of nuanced language understanding, with important implications for critical domains like medical diagnostics and semantic content retrieval.Check out the Paper and Code. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit. Aswin Ak+ postsAswin 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Scaling the size of large language models (LLMs) and their training data have now opened up emergent capabilities that allow these models to perform highly structured reasoning, logical deductions, and abstract thought. These are not incremental improvements over previous tools but mark the journey toward reaching Artificial general intelligence (AGI).Training LLMs to reason well is one of the biggest challenges in their creation. The approaches developed so far cannot nearly master multi-step problems or those where the solution must be coherent and logical. A principal cause is using human-annotated training data, which is expensive and inherently limited. Without enough annotated examples, these models fail to generalize across domains. This limitation presents a major barrier to exploiting LLMs for more complex, real-world problems requiring advanced reasoning.Previous methods have found partial solutions to this problem. Researchers have explored supervised fine-tuning, reinforcement learning from human feedback (RLHF), and prompting techniques such as chain of thought. While these techniques improve LLMs capabilities, they are still strongly dependent on quality datasets and significant computational resources. Fine-tuning with reasoning examples or integrating step-by-step problem-solving trajectories has proved successful; however, the approaches remain computationally intensive and are not generally scalable to mass applications. Addressing these challenges, researchers began to concentrate more on methods for automated data construction and reinforcement learning frameworks that make minimal demands on human effort but maximize reasoning accuracy.Researchers from Tsinghua University, Emory University, and HKUST introduced a reinforced learning paradigm for dealing with the challenges of training LLMs for reasoning tasks. Their approach uses Process Reward Models (PRMs) to guide intermediate steps within the reasoning process, significantly enhancing logical coherence and task performance. Using a combination of automated annotation with Monte Carlo simulations, the researchers have automatically generated high-quality reasoning data that does not rely on manual intervention. This innovative methodology eliminates reliance on human annotations about the data quality but enables models to perform advanced reasoning through iterative learning cycles. The reinforced learning method encompasses a variety of components, including PRM-guided automated reasoning trajectories and test-time reasoning.PRMs provide step-level rewards centered around intermediate steps rather than final outcomes. The detailed guidance ensures the model can learn incrementally and refine its understanding during training. Test-time scaling further improves reasoning capabilities by dedicating more computation resources for deliberate thinking during inference. Techniques such as Monte Carlo Tree Search (MCTS) and self-refinement cycles are critical to this process, allowing the models to simulate and evaluate multiple reasoning paths efficiently. Performance results show that these methods work well.The models trained using this reinforced paradigm show significant improvement in reasoning benchmarks. The OpenAI o1 series, one of the most prominent implementations of such techniques, achieves an 83.3% success rate in competitive programming tasks by leveraging structured reasoning and logical deduction. The o1 model has also demonstrated PhD-level performance in mathematics, physics, and biology, scoring at gold-medal levels in the International Mathematics Olympiad. Systematic evaluations reveal that integrating step-level reasoning processes improves accuracy by 150% compared to earlier models. These results emphasize the ability of the model to decompose complex problems, synthesize interdisciplinary knowledge, and maintain consistency in long-horizon tasks.The study showcases the promising perspective that LLMs can realize once endowed with advanced reinforcement learning methods and test-time scaling strategies. The cases of data annotation and the reduction of computational resources culminate in novel possibilities for reasoning-focused AI systems. This work enhances the state of LLMs and establishes a foundation for future exploration in creating models for handling highly complex tasks with minimal human intervention.In summary, research points towards the transformational strength of the merge of reinforcement learning and test time scaling in building LLM. By addressing problems associated with traditional trAIning methods and deploying novel strategies of innovative design and application, such a model shows great promise as an effective creation for reasoning power. The methods presented by authors from Tsinghua University, Emory University, and HKUST are an enormous step in pursuing the desired goal of well-established AI and human-like reasoning systems.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Nikhil+ postsNikhil 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. Meet 'Height':The only autonomous project management tool (Sponsored)

LLMs have made significant strides in automated writing, particularly in tasks like open-domain long-form generation and topic-specific reports. Many approaches rely on Retrieval-Augmented Generation (RAG) to incorporate external information into the writing process. However, these methods often fall short due to fixed retrieval strategies, limiting the generated contents depth, diversity, and utilitythis lack of nuanced and comprehensive exploration results in repetitive, shallow, and unoriginal outputs. While newer methods like STORM and Co-STORM broaden information collection through role-playing and multi-perspective retrieval, they remain confined by static knowledge boundaries and fail to leverage the full potential of LLMs for dynamic and context-aware retrieval.Machine writing lacks such iterative processes, unlike humans, who naturally reorganize and refine their cognitive frameworks through reflective practices. Reflection-based frameworks like OmniThink aim to address these shortcomings by enabling models to adjust retrieval strategies and deepen topic understanding dynamically. Recent research has highlighted the importance of integrating diverse perspectives and reasoning across multiple sources in generating high-quality outputs. While prior methods, such as multi-turn retrieval and roundtable simulations, have progressed in diversifying information sources, they often fail to adapt flexibly as the models understanding evolves.Researchers from Zhejiang University, Tongyi Lab (Alibaba Group), and the Zhejiang Key Laboratory of Big Data Intelligent Computing introduced OmniThink. This machine-writing framework mimics human cognitive processes of iterative reflection and expansion. OmniThink dynamically adjusts retrieval strategies to gather diverse, relevant information by emulating how learners progressively deepen their understanding. This approach enhances knowledge density while maintaining coherence and depth. Evaluated on the WildSeek dataset using a new knowledge density metric, OmniThink demonstrated improved article quality. Human evaluations and expert feedback affirmed its potential for generating insightful, comprehensive, long-form content, addressing key challenges in automated writing.Open-domain long-form generation entails creating detailed articles by retrieving and synthesizing information from open sources. Traditional methods involve two steps: retrieving topic-related data via search engines and generating an outline before composing the article. However, issues like redundancy and low knowledge density persist. OmniThink addresses this by emulating human-like iterative expansion and reflection, building an information tree and conceptual pool to structure relevant, diverse data. Through a three-step processinformation acquisition, outline structuring and article compositionOmniThink ensures logical coherence and rich content. It integrates semantic similarity to retrieve relevant data and refines drafts to produce concise, high-density articles.OmniThink demonstrates outstanding performance in generating articles and outlines, excelling in metrics like relevance, breadth, depth, and novelty, particularly when using GPT-4o. Its dynamic expansion and reflection mechanisms enhance information diversity, knowledge density, and creativity, enabling deeper knowledge exploration. The models outline generation improves structural coherence and logical consistency, attributed to its unique Concept Pool design. Human evaluations confirm OmniThinks superior performance compared to baselines like Co-STORM, especially in breadth. However, subtle improvements in novelty are less evident to human evaluators, highlighting the need for more refined evaluation methods to assess advanced model capabilities accurately.In conclusion, OmniThink is a machine writing framework that mimics human-like iterative expansion and reflection to produce well-structured, high-quality long-form articles. Unlike traditional retrieval-augmented generation methods, which often result in shallow, redundant, and unoriginal content, OmniThink enhances knowledge density, coherence, and depth by progressively deepening topic understanding, similar to human cognitive learning. As automatic and human evaluations confirm, this model-agnostic approach can integrate with existing frameworks. Future work aims to incorporate advanced methods combining deeper reasoning, role-playing, and human-computer interaction, further addressing challenges in generating informative and diverse long-form content.Check out the Paper, GitHub Page, and Project. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Sana Hassan+ postsSana 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. Meet 'Height':The only autonomous project management tool (Sponsored)

The development of VLMs in the biomedical domain faces challenges due to the lack of large-scale, annotated, and publicly accessible multimodal datasets across diverse fields. While datasets have been constructed from biomedical literature, such as PubMed, they often focus narrowly on domains like radiology and pathology, neglecting complementary areas such as molecular biology and pharmacogenomics that are critical for holistic clinical understanding. Privacy concerns, the complexity of expert-level annotation, and logistical constraints further impede the creation of comprehensive datasets. Previous approaches, like ROCO, MEDICAT, and PMC-15M, have relied on domain-specific filtering and supervised models to extract millions of image-caption pairs. However, these strategies often fail to capture the broader diversity of biomedical knowledge required for advancing generalist biomedical VLMs.In addition to dataset limitations, the training and evaluation of biomedical VLMs present unique challenges. Contrastive learning approaches, such as PMC-CLIP and BiomedCLIP, have shown promise by leveraging literature-based datasets and vision transformer models for image-text alignment. However, their performance is constrained by smaller datasets and limited computational resources compared to general VLMs. Furthermore, current evaluation protocols, focused mainly on radiology and pathology tasks, lack standardization and broader applicability. The reliance on additional learnable parameters and narrow datasets undermines the reliability of these evaluations, highlighting the need for scalable datasets and robust evaluation frameworks that can address the diverse demands of biomedical vision-language applications.Researchers from Stanford University introduced BIOMEDICA, an open-source framework designed to extract, annotate, and organize the entire PubMed Central Open Access subset into a user-friendly dataset. This archive includes over 24 million image-text pairs from 6 million articles enriched with metadata and expert annotations. They also released BMCA-CLIP, a suite of CLIP-style models pre-trained on BIOMEDICA via streaming, eliminating the need for local storage of 27 TB of data. These models achieve state-of-the-art performance across 40 tasks, including radiology, dermatology, and molecular biology, with a 6.56% average improvement in zero-shot classification and reduced computational requirements.The BIOMEDICA data curation process involves dataset extraction, concept labeling, and serialization. Articles and media files are downloaded from the NCBI server, extracting metadata, captions, and figure references from nXML files and the Entrez API. Images are clustered using DINOv2 embeddings and labeled through a hierarchical taxonomy refined by experts. Labels are assigned via majority voting and propagated across clusters. The dataset, containing over 24 million image-caption pairs and extensive metadata, is serialized into WebDataset format for efficient streaming. With 12 global and 170 local image concepts, the taxonomy covers categories like clinical imaging, microscopy, and data visualizations, emphasizing scalability and accessibility.The evaluation of continual pretraining on the BIOMEDICA dataset utilized 39 established biomedical classification tasks and a new retrieval dataset from Flickr, spanning 40 datasets. The classification benchmark includes pathology, radiology, biology, surgery, dermatology, and ophthalmology tasks. Metrics like average accuracy for classification and retrieval recall (at 1, 10, and 100) were employed. Concept filtering, which excludes overrepresented topics, performed better than concept balancing or full dataset pretraining. Models trained on BIOMEDICA achieved state-of-the-art results, significantly outperforming previous methods, with improved performance across classification, retrieval, and microscopy tasks using less data and computation.In conclusion, BIOMEDICA is a comprehensive framework that transforms the PubMed Central Open Access (PMC-OA) subset into the largest deep-learning-ready dataset, featuring 24 million image-caption pairs enriched with 27 metadata fields. Designed to address the lack of diverse, annotated biomedical datasets, BIOMEDICA provides a scalable, open-source solution to extract and annotate multimodal data from over 6 million articles. Through continual pretraining of CLIP-style models using BIOMEDICA, the framework achieves state-of-the-art zero-shot classification and image-text retrieval across 40 biomedical tasks, requiring 10x less computing and 2.5x less data. All resources, including models, datasets, and code, are publicly available.Check out the Paper and Project Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Sana Hassan+ postsSana 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Understanding long videos, such as 24-hour CCTV footage or full-length films, is a major challenge in video processing. Large Language Models (LLMs) have shown great potential in handling multimodal data, including videos, but they struggle with the massive data and high processing demands of lengthy content. Most existing methods for managing long videos lose critical details, as simplifying the visual content often removes subtle yet essential information. This limits the ability to effectively interpret and analyze complex or dynamic video data.Techniques currently used to understand long videos include extracting key frames or converting video frames into text. These techniques simplify processing but result in a massive loss of information since subtle details and visual nuances are omitted. Advanced video LLMs, such as Video-LLaMA and Video-LLaVA, attempt to improve comprehension using multimodal representations and specialized modules. However, these models require extensive computational resources, are task-specific, and struggle with long or unfamiliar videos. Multimodal RAG systems, like iRAG and LlamaIndex, enhance data retrieval and processing but lose valuable information when transforming video data into text. These limitations prevent current methods from fully capturing and utilizing the depth and complexity of video content.To address the challenges of video understanding, researchers from Om AI Research and Binjiang Institute of Zhejiang University introduced OmAgent, a two-step approach: Video2RAG for preprocessing and DnC Loop for task execution. In Video2RAG, raw video data undergoes scene detection, visual prompting, and audio transcription to create summarized scene captions. These captions are vectorized and stored in a knowledge database enriched with further specifics about time, location, and event details. In this way, the process avoids large context inputs to language models and, hence, problems such as token overload and inference complexity. For task execution, queries are encoded, and these video segments are retrieved for further analysis. This ensures efficient video understanding by balancing detailed data representation and computational feasibility.The DNC Loop employs a divide-and-conquer strategy, recursively decomposing tasks into manageable subtasks. The Conqueror module evaluates tasks, directing them for division, tool invocation, or direct resolution. The Divider module breaks up complex tasks, and the Rescuer deals with execution errors. The recursive task tree structure helps in the effective management and resolution of tasks. The integration of structured preprocessing by Video2RAG and the robust framework of DnC Loop makes OmAgent deliver a comprehensive video understanding system that can handle intricate queries and produce accurate results.Researchers conducted experiments to validate OmAgents ability to solve complex problems and comprehend long-form videos. They used two benchmarks, MBPP (976 Python tasks) and FreshQA (dynamic real-world Q&A), to test general problem-solving, focusing on planning, task execution, and tool usage. They designed a benchmark with over 2000 Q&A pairs for video understanding based on diverse long videos, evaluating reasoning, event localization, information summarization, and external knowledge. OmAgent consistently outperformed baselines across all metrics. In MBPP and FreshQA, OmAgent achieved 88.3% and 79.7%, respectively, surpassing GPT-4 and XAgent. OmAgent scored 45.45% overall for video tasks compared to Video2RAG (27.27%), Frames with STT (28.57%), and other baselines. It excelled in reasoning (81.82%) and information summary (72.74%) but struggled with event localization (19.05%). OmAgents Divide-and-Conquer (DnC) Loop and rewinder capabilities significantly improved performance in tasks requiring detailed analysis, but precision in event localization remained challenging.In summary, the proposed OmAgent integrates multimodal RAG with a generalist AI framework, enabling advanced video comprehension with near-infinite understanding capacity, a secondary recall mechanism, and autonomous tool invocation. It achieved strong performance on multiple benchmarks. While challenges like event positioning, character alignment, and audio-visual asynchrony remain, this method can serve as a baseline for future research to improve character disambiguation, audio-visual synchronization, and comprehension of nonverbal audio cues, advancing long-form video understanding.Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Divyesh Vitthal Jawkhede+ postsDivyesh 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Code retrieval has become essential for developers in modern software development, enabling efficient access to relevant code snippets and documentation. Unlike traditional text retrieval, which effectively handles natural language queries, code retrieval must address unique challenges, such as programming languages structural variations, dependencies, and contextual relevance. With tools like GitHub Copilot gaining popularity, advanced code retrieval systems are increasingly vital for enhancing productivity and reducing errors.Existing retrieval models often struggle to capture programming-specific nuances like syntax, control flow, and variable dependencies. These limitations hinder problem-solving in code summarization, debugging, and translation between languages. While text retrieval models have seen significant advancements, they fail to meet the specific requirements of code retrieval, highlighting the demand for specialized models that improve accuracy and efficiency across diverse programming tasks. Models like CodeBERT, CodeGPT, and UniXcoder have addressed aspects of code retrieval using pre-trained architectures. Still, they are limited in scalability and versatility due to their smaller sizes and task-specific focus. Although Voyage-Code introduced large-scale capabilities, its closed-source nature restricts broader adoption. This highlights the critical need for an open-source, scalable code retrieval system to generalize across multiple tasks.Researchers at Salesforce AI Research introduced CodeXEmbed, a family of open-source embedding models specifically designed for code and text retrieval. These models, released in three sizes, SFR-Embedding-Code-400M_R, SFR-Embedding-Code-2B_R, and 7 billion parameters, address various programming languages and retrieval tasks. CodeXEmbeds innovative training pipeline integrates 12 programming languages and transforms five distinct code retrieval categories into a unified framework. By supporting diverse tasks such as text-to-code, code-to-text, and hybrid retrievals, the model expands the boundaries of what retrieval systems can achieve, offering unprecedented flexibility and performance.CodeXEmbed employs an innovative approach that transforms code-related tasks into a unified query-and-answer framework, enabling versatility across various scenarios. Text-to-code retrieval maps natural language queries to relevant code snippets, streamlining tasks like code generation and debugging. Code-to-text retrieval generates explanations and summaries of code, enhancing documentation and knowledge sharing. Hybrid retrieval integrates text and code data, effectively addressing complex queries requiring technical and descriptive insights. The models training leverages contrastive loss to optimize query-answer alignment while reducing irrelevant data influence. Advanced techniques like low-rank adaptation and token pooling boost efficiency without sacrificing performance.In tests, it has been evaluated across various benchmarks. On the CoIR benchmark, a comprehensive code retrieval evaluation dataset covering 10 subsets and over 2 million entries, the 7-billion parameter model achieved a performance improvement of more than 20% compared to the previous state-of-the-art Voyage-Code model. Notably, the 400-million and 2-billion parameter models also outperformed Voyage-Code, demonstrating the architectures scalability across different sizes. Also, CodeXEmbed excelled in text retrieval tasks, with the 7-billion parameter model achieving an average score of 60 on the BEIR benchmark, a suite of 15 datasets covering diverse retrieval tasks such as question answering and fact-checking.The models can retrieve code and enhance end-to-end retrieval-augmented generation (RAG) systems. For instance, when applied to repository-level tasks like code completion and issue resolution, the 7-billion parameter model achieved notable results on benchmarks like RepoEval and SWE-Bench-Lite. RepoEval, focusing on repository-level code completion, saw top-1 accuracy improvements when the model retrieved contextually relevant snippets. In SWE-Bench-Lite, a curated dataset for GitHub issue resolution, CodeXEmbed outperformed traditional retrieval systems.Key takeaways from the research highlight the contributions and implications of CodeXEmbed in advancing code retrieval:The 7-billion parameter model achieved state-of-the-art performance, with over 20% improvement on the CoIR benchmark and competitive results on BEIR. It demonstrated versatility across code and text tasks.The 400-million and 2-billion parameter models offer practical alternatives for environments where computational resources are limited.The models address a broad spectrum of code-related applications by unifying 12 programming languages and five retrieval categories.Unlike closed systems such as Voyage-Code, CodeXEmbed promotes community-driven research and innovation.Integration with retrieval-augmented generation systems improves outcomes for tasks like code completion and issue resolution.Using contrastive loss and token pooling optimizes retrieval accuracy and model adaptability.In conclusion, Salesforces introduction of the CodeXEmbed family advances code retrieval. These models demonstrate unmatched versatility and scalability by achieving state-of-the-art performance on the CoIR benchmark and excelling in text retrieval tasks. The multilingual and multi-task unified framework, supporting 12 programming languages, positions CodeXEmbed as a pivotal tool for developers and researchers. Its open-source accessibility encourages community-driven innovation while bridging the gap between natural language and code retrieval.Check out the Paper, 400M Model, and 2B Model. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Asif RazzaqAsif 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Humans possess an extraordinary ability to localize sound sources and interpret their environment using auditory cues, a phenomenon termed spatial hearing. This capability enables tasks such as identifying speakers in noisy settings or navigating complex environments. Emulating such auditory spatial perception is crucial for enhancing the immersive experience in technologies like augmented reality (AR) and virtual reality (VR). However, the transition from monaural (single-channel) to binaural (two-channel) audio synthesiswhich captures spatial auditory effectsfaces significant challenges, particularly due to the limited availability of multi-channel and positional audio data.Traditional mono-to-binaural synthesis approaches often rely on digital signal processing (DSP) frameworks. These methods model auditory effects using components such as the head-related transfer function (HRTF), room impulse response (RIR), and ambient noise, typically treated as linear time-invariant (LTI) systems. Although DSP-based techniques are well-established and can generate realistic audio experiences, they fail to account for the nonlinear acoustic wave effects inherent in real-world sound propagation.Supervised learning models have emerged as an alternative to DSP, leveraging neural networks to synthesize binaural audio. However, such models face two major limitations: First, the scarcity of position-annotated binaural datasets and second, susceptibility to overfitting to specific acoustic environments, speaker characteristics, and training datasets. The need for specialized equipment for data collection further constraints these approaches, making supervised methods costly and less practical.To address these challenges, researchers from Google have proposed ZeroBAS, a zero-shot neural method for mono-to-binaural speech synthesis that does not rely on binaural training data. This innovative approach employs parameter-free geometric time warping (GTW) and amplitude scaling (AS) techniques based on source position. These initial binaural signals are further refined using a pretrained denoising vocoder, yielding perceptually realistic binaural audio. Remarkably, ZeroBAS generalizes effectively across diverse room conditions, as demonstrated using the newly introduced TUT Mono-to-Binaural dataset, and achieves performance comparable to, or even better than, state-of-the-art supervised methods on out-of-distribution data.The ZeroBAS framework comprises a three-stage architecture as follows:In stage 1, Geometric time warping (GTW) transforms the monaural input into two channels (left and right) by simulating interaural time differences (ITD) based on the relative positions of the sound source and listeners ears. GTW computes the time delays for the left and right ear channels. The warped signals are then interpolated linearly to generate initial binaural channels.In stage 2, Amplitude scaling (AS) enhances the spatial realism of the warped signals by simulating the interaural level difference (ILD) based on the inverse-square law. As human perception of sound spatiality relies on both ITD and ILD, with the latter dominant for high-frequency sounds. Using the Euclidean distances of source from both ears and , the amplitudes are scaled.In stage 3, involves an iterative refinement of the warped and scaled signals using a pretrained denoising vocoder, WaveFit. This vocoder leverages log-mel spectrogram features and denoising diffusion probabilistic models (DDPMs) to generate clean binaural waveforms. By iteratively applying the vocoder, the system mitigates acoustic artifacts and ensures high-quality binaural audio output.Coming to evaluations, ZeroBAS was evaluated on two datasets (results in Table 1 and 2): the Binaural Speech dataset and the newly introduced TUT Mono-to-Binaural dataset. The latter was designed to test the generalization capabilities of mono-to-binaural synthesis methods in diverse acoustic environments. In objective evaluations, ZeroBAS demonstrated significant improvements over DSP baselines and approached the performance of supervised methods despite not being trained on binaural data. Notably, ZeroBAS achieved superior results on the out-of-distribution TUT dataset, highlighting its robustness across varied conditions.Subjective evaluations further confirmed the efficacy of ZeroBAS. Mean Opinion Score (MOS) assessments showed that human listeners rated ZeroBASs outputs as slightly more natural than those of supervised methods. In MUSHRA evaluations, ZeroBAS achieved comparable spatial quality to supervised models, with listeners unable to discern statistically significant differences.Even though this method is quite remarkable, it does have some limitations. ZeroBAS struggles to directly process phase information because the vocoder lacks positional conditioning, and it relies on general models instead of environment-specific ones. Despite these constraints, its ability to generalize effectively highlights the potential of zero-shot learning in binaural audio synthesis.In conclusion, ZeroBAS offers a fascinating, room-agnostic approach to binaural speech synthesis that achieves perceptual quality comparable to supervised methods without requiring binaural training data. Its robust performance across diverse acoustic environments makes it a promising candidate for real-world applications in AR, VR, and immersive audio systems.Check out the Paper and Details. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Vineet Kumar+ postsVineet Kumar is a consulting intern at MarktechPost. He is currently pursuing his BS from the Indian Institute of Technology(IIT), Kanpur. He is a Machine Learning enthusiast. He is passionate about research and the latest advancements in Deep Learning, Computer Vision, and related fields. Meet 'Height':The only autonomous project management tool (Sponsored)

The rapid advancement and widespread adoption of generative AI systems across various domains have increased the critical importance of AI red teaming for evaluating technology safety and security. While AI red teaming aims to evaluate end-to-end systems by simulating real-world attacks, current methodologies face significant challenges in effectiveness and implementation. The complexity of modern AI systems, with their expanding capabilities across multiple modalities including vision and audio, has created an unprecedented array of potential vulnerabilities and attack vectors. Moreover, integrating agentic systems that grant AI models higher privileges and access to external tools has substantially increased the attack surface and potential impact of security breaches.Current approaches to AI security have revealed significant limitations in addressing both traditional and emerging vulnerabilities. Traditional security assessment methods mainly focus on model-level risks while overlooking critical system-level vulnerabilities that often prove more exploitable. Moreover, AI systems utilizing retrieval augmented generation (RAG) architectures have shown susceptibility to cross-prompt injection attacks, where malicious instructions hidden in documents can manipulate model behavior and facilitate data exfiltration. While some defensive techniques like input sanitization and instruction hierarchies offer partial solutions, they cannot eliminate security risks due to the fundamental limitations of language models.Researchers from Microsoft have proposed a comprehensive framework for AI red teaming based on their extensive experience testing over 100 generative AI products. Their approach introduces a structured threat model ontology designed to systematically identify and evaluate traditional and emerging security risks in AI systems. The framework encompasses eight key lessons from real-world operations, ranging from fundamental system understanding to integrating automation in security testing. This methodology addresses the growing complexity of AI security by combining systematic threat modeling with practical insights derived from actual red teaming operations. The approach emphasizes the importance of considering both system-level and model-level vulnerabilities.The operational architecture of Microsofts AI red teaming framework uses a dual-focus approach targeting both standalone AI models and integrated systems. The framework distinguishes between cloud-hosted models and complex systems that incorporate these models into various applications like copilots and plugins. Their methodology has evolved significantly since 2021 expanding from security-focused assessments to include comprehensive responsible AI (RAI) impact evaluations. The testing protocol maintains a rigorous coverage, of traditional security concerns, including data exfiltration, credential leaking, and remote code execution, while simultaneously addressing AI-specific vulnerabilities.The effectiveness of Microsofts red teaming framework has been shown through a comparative analysis of attack methodologies. Their findings challenge conventional assumptions about the necessity of complex techniques, revealing that simpler approaches often match or exceed the effectiveness of complex gradient-based methods. The research highlights the superiority of system-level attack approaches over model-specific tactics. This conclusion is supported by real-world evidence showing that attackers typically exploit combinations of simple vulnerabilities across system components rather than focusing on complex model-level attacks. These results emphasize the importance of adopting a holistic security perspective, that considers both AI-specific and traditional system vulnerabilities.In conclusion, researchers from Microsoft have proposed a comprehensive framework for AI red teaming. The framework developed through testing over 100 GenAI products provides valuable insights into effective risk evaluation methodologies. The combination of a structured threat model ontology with practical lessons learned offers a robust foundation for organizations developing their own AI security assessment protocols. These insights and methodologies provide essential guidance for addressing real-world vulnerabilities. The frameworks emphasis on practical, implementable solutions positions it as a valuable resource for organizations, research institutions, and governments working to establish effective AI risk assessment protocols.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Sajjad Ansari+ postsSajjad 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Large Language Models (LLMs) have become essential tools in software development, offering capabilities such as generating code snippets, automating unit tests, and debugging. However, these models often fall short in producing code that is not only functionally correct but also efficient in runtime. Overlooking runtime efficiency can lead to software that performs poorly, increases operational costs, and impacts user experience. This issue is particularly pronounced for less experienced developers, who may rely on AI-suggested code without fully understanding its implications. Salesforce Research addresses these challenges with PerfCodeGen, a framework that aims to improve both the correctness and performance of LLM-generated code.Salesforce AIs PerfCodeGen is a training-free framework designed to enhance the runtime efficiency of LLM-generated code. It achieves this by using execution feedback in an iterative self-refinement process. Unlike approaches requiring fine-tuning with extensive training data, PerfCodeGen employs a feedback loop that evaluates and refines code based on runtime metrics during test execution. The framework operates in two key phases: refining correctness and optimizing performance. Initially, it ensures the generated code meets functional requirements by addressing issues identified in unit tests. Once correctness is established, the framework focuses on runtime efficiency, optimizing the code by targeting and refining the most resource-intensive test cases. This iterative process results in solutions that are both correct and efficient.Technical Insights and Benefits PerfCodeGen integrates with existing LLM workflows and begins by generating multiple candidate solutions using nucleus sampling. In the first phase, these candidates are assessed for correctness through unit tests. Feedback from failed tests is used to refine the solutions. Once functional correctness is ensured, the framework moves to the second phase, analyzing runtime metrics to identify bottlenecks. This information is then used to optimize the code further, focusing on the most time-consuming test cases.This two-phase process increases the likelihood of producing optimally efficient programs. PerfCodeGens methodology mirrors human debugging and optimization practices, making it both effective and intuitive. Additionally, the frameworks reliance on feedback rather than retraining allows it to scale across various LLMs and application domains. It has shown consistent improvements in runtime efficiency and correctness across models such as Phi-3-mini, Llama 3, and GPT-4.PerfCodeGen has been tested on benchmarks such as HumanEval, MBPP, and APPS, demonstrating its effectiveness:Runtime Efficiency: On HumanEval, GPT-4s optimization rate (%Opt) increased from 24.54% to 28.83% with PERFCODEGEN, with similar improvements observed across other models.Correctness Improvement: On MBPP, GPT-3.5s correctness rate (%Correct) rose from 66.38% to 73.36% with a single sample (Best@1).Outperforming Ground Truth: PERFCODEGEN enabled LLMs to generate more efficient solutions than ground truth in approximately 55% of HumanEval tasks and 67% of MBPP tasks.Scalability: Open models such as Phi-3-mini and Mixtral achieved performance comparable to closed models like GPT-3.5 and GPT-4.These results highlight PERFCODEGENs ability to balance correctness and runtime efficiency effectively, making it a valuable addition to LLM-driven code generation workflows.Conclusion: PerfCodeGen offers a practical solution to a key limitation of current LLMs: their focus on correctness at the expense of runtime efficiency. By incorporating execution feedback into an iterative refinement process, PerfCodeGen enables the generation of code that is both correct and efficient. This approach enhances the usability of LLMs in software development, providing developers with tools to produce higher-quality code without extensive retraining. The frameworks success across diverse benchmarks demonstrates its potential as a step forward in creating efficient, reliable, and accessible AI-driven programming solutions.Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Asif RazzaqAsif 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Modern image and video generation methods rely heavily on tokenization to encode high-dimensional data into compact latent representations. While advancements in scaling generator models have been substantial, tokenizersprimarily based on convolutional neural networks (CNNs)have received comparatively less attention. This raises questions about how scaling tokenizers might improve reconstruction accuracy and generative tasks. Challenges include architectural limitations and constrained datasets, which affect scalability and broader applicability. There is also a need to understand how design choices in auto-encoders influence performance metrics such as fidelity, compression, and generation.Researchers from Meta and UT Austin have addressed these issues by introducing ViTok, a Vision Transformer (ViT)-based auto-encoder. Unlike traditional CNN-based tokenizers, ViTok employs a Transformer-based architecture enhanced by the Llama framework. This design supports large-scale tokenization for images and videos, overcoming dataset constraints by training on extensive and diverse data.ViTok focuses on three aspects of scaling:Bottleneck scaling: Examining the relationship between latent code size and performance.Encoder scaling: Evaluating the impact of increasing encoder complexity.Decoder scaling: Assessing how larger decoders influence reconstruction and generation.These efforts aim to optimize visual tokenization for both images and videos by addressing inefficiencies in existing architectures.Technical Details and Advantages of ViTokViTok uses an asymmetric auto-encoder framework with several distinctive features:Patch and Tubelet Embedding: Inputs are divided into patches (for images) or tubelets (for videos) to capture spatial and spatiotemporal details.Latent Bottleneck: The size of the latent space, defined by the number of floating points (E), determines the balance between compression and reconstruction quality.Encoder and Decoder Design: ViTok employs a lightweight encoder for efficiency and a more computationally intensive decoder for robust reconstruction.By leveraging Vision Transformers, ViTok improves scalability. Its enhanced decoder incorporates perceptual and adversarial losses to produce high-quality outputs. Together, these components enable ViTok to:Achieve effective reconstruction with fewer computational FLOPs.Handle image and video data efficiently, taking advantage of the redundancy in video sequences.Balance trade-offs between fidelity (e.g., PSNR, SSIM) and perceptual quality (e.g., FID, IS).Results and InsightsViToks performance was evaluated using benchmarks such as ImageNet-1K, COCO for images, and UCF-101 for videos. Key findings include:Bottleneck Scaling: Increasing bottleneck size improves reconstruction but can complicate generative tasks if the latent space is too large.Encoder Scaling: Larger encoders show minimal benefits for reconstruction and may hinder generative performance due to increased decoding complexity.Decoder Scaling: Larger decoders enhance reconstruction quality, but their benefits for generative tasks vary. A balanced design is often required.Results highlight ViToks strengths in efficiency and accuracy:State-of-the-art metrics for image reconstruction at 256p and 512p resolutions.Improved video reconstruction scores, demonstrating adaptability to spatiotemporal data.Competitive generative performance in class-conditional tasks with reduced computational demands.ConclusionViTok offers a scalable, Transformer-based alternative to traditional CNN tokenizers, addressing key challenges in bottleneck design, encoder scaling, and decoder optimization. Its robust performance across reconstruction and generation tasks highlights its potential for a wide range of applications. By effectively handling both image and video data, ViTok underscores the importance of thoughtful architectural design in advancing visual tokenization.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Asif RazzaqAsif 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. Meet 'Height':The only autonomous project management tool (Sponsored)

CrewAI is an innovative platform that transforms how AI agents collaborate to solve complex problems. As an orchestration framework, it empowers users to assemble and manage teams of specialized AI agents, each tailored to perform specific tasks within an organized workflow. Just as a well-run organization delegates roles and responsibilities among its departments, CrewAI assigns defined roles to its agents, ensuring seamless collaboration toward achieving a shared objective.Core Principles of CrewAICrewAI is built on creating a synergistic AI ecosystem where agents function as specialists within a larger operational structure. This system mirrors real-world organizational dynamics by assigning agents specific roles, equipping them with specialized tools, and designing workflows that allow them to operate autonomously yet cohesively.Role-Based Agents: CrewAI agents are designed with distinct roles, such as researchers, analysts, writers, and more. Each agent operates autonomously within its defined scope, utilizing advanced tools and APIs to interact with external data sources. These agents are the building blocks of the CrewAI system, each contributing unique expertise to the overall mission.Flexible Workflows: CrewAI facilitates the design of intricate workflows that guide agent collaboration. These workflows can be sequential or parallel, allowing tasks to progress efficiently while maintaining clear dependencies and logical task progression.Task-Centric Architecture: Tasks are the fundamental units of action within CrewAI. Each task has a clear objective, specific tools, and a defined output. Tasks are delegated to agents depending on their roles, ensuring a precise and efficient approach to problem-solving.How CrewAI FunctionsCrewAI organizes agents into crews and assigns them to specialized tasks. The process is managed through several interconnected components:Crews: Crews are CrewAIs highest-level organizational unit. They oversee the collective efforts of multiple agents and are responsible for coordinating workflows, managing resources, and ensuring the timely completion of objectives.Agents: Each agent within the system is a specialized unit capable of autonomous decision-making and task execution. Agents can collaborate, share insights, and delegate subtasks, mimicking the dynamics of human teamwork.Processes and Flows: The workflow management system ensures smooth interactions between agents. Processes define collaboration patterns, manage task assignments, and control inter-agent communication to maintain efficiency and coherence.Guide for Installing and Setting up CrewAI1. Check Python CompatibilityEnsure your system has a compatible Python version (3.10 or 3.12). To verify:# bashpython3 --versionIf you need an update, download the latest Python version.2. Install CrewAI and ToolsInstall the framework and its tools using pip:# bashpip install crewai crewai-toolsFor a comprehensive installation, including all optional tools, run:# bashpip install 'crewai[tools]'3. Verify the InstallationConfirm CrewAI and its dependencies are installed correctly:# bashpip freeze | grep crewaiExpected output:crewai==X.X.Xcrewai-tools==X.X.X4. Create a New CrewAI ProjectInitialize a new project with the following command:# bashcrewai create crew my_projectThis creates a project directory with the following structure:# cssmy_project/ .gitignore pyproject.toml README.md .env src/ my_project/ __init__.py main.py crew.py tools/ custom_tool.py __init__.py config/ agents.yaml tasks.yaml5. Configure Your ProjectDefine Agents: Open agents.yaml to specify your agents and their roles:# yaml researcher: role: Researcher goal: > Conduct cutting-edge research on {topic} backstory: > An experienced researcher, skilled at finding actionable insights.Set Up Tasks: Edit tasks.yaml to outline tasks for the agents:# yaml research_task: description: > Explore the latest developments on {topic}. expected_output: > A detailed report summarizing key findings. agent: researcher6. Run the ProjectSet up environment variables like API keys in the .env file:# envOPENAI_API_KEY=your_openai_api_keySERPER_API_KEY=your_serper_api_keyThen, navigate to your project directory and execute:# bashcd my_projectcrewai installcrewai run7. Upgrade Existing InstallationsIf CrewAI is already installed, update it to the latest version:# bashpip install --upgrade crewai crewai-tools8. Example Code for Crew OrchestrationHeres a Python example (crew.py) to define and manage agents and tasks:# pythonfrom crewai import Agent, Crew, Taskfrom crewai.project import CrewBase, agent, task, crew@CrewBaseclass MyCrew: @agent def researcher(self) -> Agent: return Agent( config=self.agents_config['researcher'], verbose=True, ) @task def research_task(self) -> Task: return Task( config=self.tasks_config['research_task'], output_file='output/research.md', ) @crew def crew(self) -> Crew: return Crew( agents=self.agents, tasks=self.tasks, process="sequential", )Execute the project by running:# bashpython3 src/my_project/main.pyThis guide will create a fully functional CrewAI environment ready to orchestrate collaborative AI agents efficiently. For advanced setups or troubleshooting, refer to the CrewAI Documentation.In conclusion, CrewAI is an intelligent framework that enables AI agents to collaborate seamlessly, share insights, and autonomously execute tasks with minimal oversight. Its extensible and scalable design effortlessly integrates new tools and roles, supporting efficient task management through sequential and parallel workflows. This adaptability makes CrewAI ideal for diverse applications, including data analysis, content creation, customer service, financial risk assessment, process automation, and marketing analytics.Sources Asif RazzaqAsif 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Chemical reasoning involves intricate, multi-step processes requiring precise calculations, where small errors can lead to significant issues. LLMs often struggle with domain-specific challenges, such as accurately handling chemical formulas, reasoning through complex steps, and integrating code effectively. Despite advancements in scientific reasoning, benchmarks like SciBench reveal LLMs limitations in solving chemical problems, highlighting the need for innovative approaches. Recent frameworks, such as StructChem, attempt to address these challenges by structuring problem-solving into stages like formula generation and confidence-based reviews. Other techniques, including advanced prompting strategies and Python-based reasoning tools, have also been explored. For instance, ChemCrow leverages function calling and precise code generation for tackling chemistry-specific tasks, while combining LLMs with external tools like Wolfram Alpha shows potential for improving accuracy in scientific problem-solving, though integration remains a challenge.Decomposing complex problems into smaller tasks has enhanced model reasoning and accuracy, particularly in multi-step chemical problems. Studies emphasize the benefits of breaking down queries into manageable components, improving understanding and performance in domains like reading comprehension and complex question answering. Additionally, self-evolution techniques, where LLMs refine their outputs through iterative improvement and prompt evolution, have shown promise. Memory-enhanced frameworks, tool-assisted critiquing, and self-verification methods strengthen LLM capabilities by enabling error correction and refinement. These advancements provide a foundation for developing scalable systems capable of handling the complexities of chemical reasoning while maintaining accuracy and efficiency.Researchers from Yale University, UIUC, Stanford University, and Shanghai Jiao Tong University introduced ChemAgent, a framework that enhances LLM performance through a dynamic, self-updating library. ChemAgent decomposes chemical tasks into sub-tasks, storing these and their solutions in a structured memory system. This system includes Planning Memory for strategies, Execution Memory for task-specific solutions, and Knowledge Memory for foundational principles. When solving new problems, ChemAgent retrieves, refines, and updates relevant information, enabling iterative learning. Tested on SciBench datasets, ChemAgent improved accuracy by up to 46% (GPT-4), outperforming state-of-the-art methods and demonstrating potential for applications like drug discovery.ChemAgent is a system designed to improve LLMs for solving complex chemical problems. It organizes tasks into a structured memory with three components: Planning Memory (strategies), Execution Memory (solutions), and Knowledge Memory (chemical principles). Problems are broken into smaller sub-tasks in a library built from verified solutions. Relevant tasks are retrieved, refined, and dynamically updated during inference to enhance adaptability. ChemAgent outperforms baseline models (Few-shot, StructChem) on four datasets, achieving high accuracy through structured memory and iterative refinement. Its hierarchical approach and memory integration establish an effective framework for advanced chemical reasoning tasks.The study evaluates ChemAgents memory components (Mp, Me, Mk) to identify their contributions, with GPT-4 as the base model. Results show that removing any component reduces performance, with Mk being the most impactful, particularly in datasets like ATKINS with limited memory pools. Memory quality is crucial, as GPT-4-generated memories outperform GPT-3.5, while hybrid memories degrade accuracy due to conflicting inputs. ChemAgent demonstrates consistent performance improvement across different LLMs, with the most notable gains on powerful models like GPT-4. The self-updating memory mechanism enhances problem-solving capabilities, particularly in complex datasets requiring specialized chemical knowledge and logical reasoning.In conclusion, ChemAgent is a framework that enhances LLMs in solving complex chemical problems through self-exploration and a dynamic, self-updating memory library. By decomposing tasks into planning, execution, and knowledge components, ChemAgent builds a structured library to improve task decomposition and solution generation. Experiments on datasets like SciBench show significant performance gains, up to a 46% improvement using GPT-4. The framework effectively addresses challenges in chemical reasoning, such as handling domain-specific formulas and multi-step processes. It holds promise for broader applications in drug discovery and materials science.Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Sana Hassan+ postsSana 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Enabling artificial intelligence to navigate and retrieve contextually rich, multi-faceted information from the internet is important in enhancing AI functionalities. Traditional search engines are limited to superficial results, failing to capture the nuances required to investigate profoundly integrated content across a network of related web pages. This constraint limits LLMs in performing tasks that require reasoning across hierarchical information, which negatively impacts domains such as education, organizational decision-making, and the resolution of complex inquiries. Current benchmarks do not adequately assess the intricacies of multi-step interactions, resulting in a considerable deficit in evaluating and improving LLMs capabilities in web traversal.Though Mind2Web and WebArena focus on action-oriented interactions that contain HTML directives, they suffer important limitations like noise, a rather poor understanding of wider contexts, and less enabling of multi-step reasoning. RAG systems are useful for retrieving real-time data but are largely limited to horizontal searches that often miss key content buried within the deeper layers of websites. The limitations of current methodologies make them inadequate for addressing complex, data-driven issues that require concurrent reasoning and planning across numerous web pages.Researchers from the Alibaba Group introduced WebWalker, a multi-agent framework designed to emulate human-like web navigation. This dual-agent system consists of the Explorer Agent, tasked with methodical page navigation, and the Critic Agent, which aggregates and assesses information to facilitate query resolution. By combining horizontal and vertical exploration, this explore-critic system overcomes the limitations of traditional RAG systems. The dedicated benchmark, WebWalkerQA, with single-source and multi-source queries, evaluates whether the AI can handle layered, multi-step tasks. This coupling of vertical exploration with reasoning allows WebWalker to improve the depth and quality of retrieved information by leaps and bounds.The benchmark supporting WebWalker, WebWalkerQA, comprises 680 question-answer pairs derived from 1,373 web pages in domains related to education, organizations, conferences, and games. Most queries mimic realistic tasks and require inferring information spread over several subpages. Evaluation of accuracy is in terms of correct answers, along with the number of actions, or steps taken by the system to resolve it, for single-source and multi-source reasoning. Evaluated with different model architectures, including GPT-4o and Qwen-2.5 series, WebWalker showed robustness when dealing with complex and dynamic queries. It used HTML metadata to navigate correctly and had a thought-action-observation framework to engage proficiently with structured web hierarchies.The results show that WebWalker has an important advantage over managing complex web navigation tasks compared with ReAct and Reflexion and significantly surpasses them in accuracy in single-source and multi-source scenarios. The system also demonstrated outstanding performance in layered reasoning tasks while keeping action counts optimized; hence, the balance between accuracy and resource usage is reached effectively. Such results confirm the scalability and adaptability of the system and make it a benchmark for AI-enhanced web navigation frameworks.WebWalker solves the problems of navigation and reasoning over highly integrated web content with a dual-agent framework based on an explore-critic paradigm. The benchmark for the tool, WebWalkerQA, systematically tests these functionalities and thus provides a challenging benchmark for tasks in web navigation. It is the most important development towards AI systems to access and manage dynamic, stratified information efficiently, marking an important milestone in the area of AI-enhanced information retrieval. Moreover, by redesigning web traversal metrics and enhancing retrieval-augmented generation systems, WebWalker thus lays a more robust foundation on which increasingly intricate real-world applications can be targeted, hence thereby reinforcing its significance in the realm of artificial intelligence.Check out the Paper, Project Page, and GitHub Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit. Recommend Open-Source Platform: Parlant is a framework that transforms how AI agents make decisions in customer-facing scenarios. (Promoted)The post This AI Paper from Alibaba Unveils WebWalker: A Multi-Agent Framework for Benchmarking Multistep Reasoning in Web Traversal appeared first on MarkTechPost.

Multimodal large language models (MLLMs) bridge vision and language, enabling effective interpretation of visual content. However, achieving precise and scalable region-level comprehension for static images and dynamic videos remains challenging. Temporal inconsistencies, scaling inefficiencies, and limited video comprehension hinder progress, particularly in maintaining consistent object and region representations across video frames. Temporal drift, caused by motion, scaling, or perspective changes, coupled with reliance on computationally heavy methods like bounding boxes or Region of Interest (RoI)-aligned features, increases complexity and limits real-time and large-scale video analysis.Recent strategies, such as textual region coordinates, visual markers, and RoI-based features, have attempted to address these issues. However, they often fail to ensure temporal consistency across frames or efficiently process large datasets. Bounding boxes lack robustness for multi-frame tracking, and static frame analysis misses intricate temporal relationships. While innovations like embedding coordinates into textual prompts and using image-based markers have advanced the field, a unified solution for image and video domains remains out of reach.Researchers from NVIDIA and Yonsei University developed Omni-RGPT, a novel multimodal large language model designed to achieve seamless region-level comprehension in images and videos to address these challenges. This model introduces Token Mark, a groundbreaking method that embeds region-specific tokens into visual and text prompts, establishing a unified connection between the two modalities. The Token Mark system replaces traditional RoI-based approaches by defining a unique token for each target region, which remains consistent across frames in a video. This strategy prevents temporal drift and reduces computational costs, enabling robust reasoning for static and dynamic inputs. Including a Temporal Region Guide Head further enhances the models performance on video data by classifying visual tokens to avoid reliance on complex tracking mechanisms.Omni-RGPT leverages a newly created large-scale dataset called RegVID-300k, which contains 98,000 unique videos, 214,000 annotated regions, and 294,000 region-level instruction samples. This dataset was constructed by combining data from ten public video datasets, offering diverse and fine-grained instructions for region-specific tasks. The dataset supports visual commonsense reasoning, region-based captioning, and referring expression comprehension. Unlike other datasets, RegVID-300k includes detailed captions with temporal context and mitigates visual hallucinations through advanced validation techniques.Omni-RGPT achieved state-of-the-art results on several benchmarks, including 84.5% accuracy on the Causal-VidQA dataset, which evaluates temporal and spatial reasoning across video sequences. The model outperformed existing methods like MotionEpic by over 5% in some sub-tasks, demonstrating superior performance in prediction and counterfactual reasoning. Similarly, the model excelled in video captioning tasks, achieving high METEOR scores on challenging datasets like Vid-STG and BenSMOT. The model achieved remarkable accuracy for image-based tasks on the Visual Commonsense Reasoning (VCR) dataset, outperforming methods specifically optimized for image domains.Several key takeaways from the research on Omni-RGPT include:This approach enables consistent and scalable region-level understanding by embedding predefined tokens into visual and text inputs. This prevents temporal drift and supports seamless reasoning across frames.The dataset provides detailed, fine-grained, diverse annotations, enabling the model to excel in complex video tasks. It includes 294,000 region-level instructions and addresses gaps in existing datasets.Omni-RGPT demonstrated superior performance across benchmarks such as Causal-VidQA and VCR, achieving accuracy improvements of up to 5% compared to leading models.The models design reduces computational overhead by avoiding dependency on bounding box coordinates or full video tracklets, making it suitable for real-world applications.The framework seamlessly integrates image and video tasks under a single architecture, achieving exceptional performance without compromising efficiency.In conclusion, Omni-RGPT addresses critical challenges in region-specific multimodal learning by introducing Token Mark and a novel dataset to support detAIled comprehension in images and videos. The models scalable design and state-of-the-art performance across diverse tasks set a new benchmark for the field. Omni-RGPT provides a robust foundation for future research and practical applications in AI by eliminating temporal drift, reducing computational complexity, and leveraging large-scale data.Check out the Paper and Project Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Asif RazzaqAsif 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. 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LLMs are essential in industries such as education, healthcare, and customer service, where natural language understanding plays a crucial role. Though highly versatile, LLMs challenge is adapting to new tasks. Most fine-tuning methods are resource and time-consuming. Moreover, the fine-tuning approach often results in overfitting or sacrificing general adaptability for task-specific performance. This is a barrier for LLMs to address dynamic new and unforeseen tasks and creates a bottleneck in the overall application.One of the most prominent methods to address these challenges is Low-Rank Adaptation (LoRA), which updates small, task-specific matrices while freezing the rest of the models parameters. Although this reduces the computational cost of fine-tuning, it has limitations, such as increased sensitivity to overfitting and the inability to scale efficiently across tasks. Moreover, LoRAs design lacks inherent compositionality, limiting its ability to integrate multiple domain-specific skills.The researchers at Sakana AI and Institute of Science Tokyo introduced Transformer, a novel self-adaptive machine learning framework for large language models. Transformer employs a groundbreaking method called Singular Value Fine-tuning (SVF), which adapts LLMs in real time to new tasks without extensive retraining. By focusing on selectively modifying the singular components of the models weight matrices, Transformer enables dynamic task-specific adjustments. This innovation reduces the computational burden associated with fine-tuning, offering a scalable and efficient solution for self-adaptation.At the heart of Transformer is the SVF method, which fine-tunes the singular values of weight matrices. This approach drastically minimizes the number of trainable parameters compared to traditional methods. Instead of altering the entire model, SVF leverages reinforcement learning to create compact expert vectors specialized for specific tasks. For the inference process, Transformer works on a two-pass mechanism: the first is to analyze what the task might be and requires, and in the second, it dynamically integrates various relevant expert vectors to produce suitable behavior. Modularly, the approach ensures efficiency in addressing such a wide array of tasks through Transformer.Transformer performed outstanding performance in extensive benchmark evaluations. For instance, the framework shows improvements of over 39% compared to baselines in visual question-answering domains. In mathematics-related problem-solving, when testing was done on the GSM8K datasets, this model showed its strength by winning more than any fine-tuning method, reaching about a 4% improvement in its performance. On programming tasks under the MBPP-pro benchmark, Transformer displayed considerable accuracy improvements for domain-specific tasks and its general performance on various types of domains. As a result, Transformer adapted efficiently to unseen tasks like ARC-Challenge and Humaneval by either maintaining or exceeding the baseline performance metrics.An important overall outcome was the SVF methods efficiency. This improved training times and reduced the need for significant computational requirements as this method used fewer than 10% of the parameters required by LoRA. For example, for the GSM8K dataset, only 0.39 million parameters were needed for SVF training versus 6.82 million using LoRA to achieve higher performance. In addition, the model demonstrated good compositionality; vectors trained as an expert for one task could be reused and added together with others for a different, unrelated task, indicating the ability to scale up this Transformer framework.The researchers achieved this leap forward by addressing core limitations in existing methods, such as overfitting and inefficiency. By leveraging reinforcement learning, the SVF method provided principled regularization, preventing performance collapse on small datasets or narrow task domains. This allowed Transformer to excel despite limited training data while maintaining task adaptability.Conclusion: A research team from Sakana AI provided a scalable and efficient solution to task-specific adaptation in LLMs. Transformer, with its SVF method, is a highly significant advancement within the field that will pave the way for computationally efficient self-adaptive AI systems that are highly versatile. This approach will answer present challenges and lay a foundation for future developments of adaptive AI technologies.Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Nikhil+ postsNikhil 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Large Language Models (LLMs) have become integral to various artificial intelligence applications, demonstrating capabilities in natural language processing, decision-making, and creative tasks. However, critical challenges remain in understanding and predicting their behaviors. Treating LLMs as black boxes complicates efforts to assess their reliability, particularly in contexts where errors can have significant consequences. Traditional approaches often rely on internal model states or gradients to interpret behaviors, which are unavailable for closed-source, API-based models. This limitation raises an important question: how can we effectively evaluate LLM behavior with only black-box access? The problem is further compounded by adversarial influences and potential misrepresentation of models through APIs, highlighting the need for robust and generalizable solutions.To address these challenges, researchers at Carnegie Mellon University have developed QueRE (Question Representation Elicitation). This method is tailored for black-box LLMs and extracts low-dimensional, task-agnostic representations by querying models with follow-up prompts about their outputs. These representations, based on probabilities associated with elicited responses, are used to train predictors of model performance. Notably, QueRE performs comparably to or even better than some white-box techniques in reliability and generalizability.Unlike methods dependent on internal model states or full output distributions, QueRE relies on accessible outputs, such as top-k probabilities available through most APIs. When such probabilities are unavailable, they can be approximated through sampling. QueREs features also enable evaluations such as detecting adversarially influenced models and distinguishing between architectures and sizes, making it a versatile tool for understanding and utilizing LLMs.Technical Details and Benefits of QueREQueRE operates by constructing feature vectors derived from elicitation questions posed to the LLM. For a given input and the models response, these questions assess aspects such as confidence and correctness. Questions like Are you confident in your answer? or Can you explain your answer? enable the extraction of probabilities that reflect the models reasoning.The extracted features are then used to train linear predictors for various tasks:Performance Prediction: Evaluating whether a models output is correct at an instance level.Adversarial Detection: Identifying when responses are influenced by malicious prompts.Model Differentiation: Distinguishing between different architectures or configurations, such as identifying smaller models misrepresented as larger ones.By relying on low-dimensional representations, QueRE supports strong generalization across tasks. Its simplicity ensures scalability and reduces the risk of overfitting, making it a practical tool for auditing and deploying LLMs in diverse applications.Results and InsightsExperimental evaluations demonstrate QueREs effectiveness across several dimensions. In predicting LLM performance on question-answering (QA) tasks, QueRE consistently outperformed baselines relying on internal states. For instance, on open-ended QA benchmarks like SQuAD and Natural Questions (NQ), QueRE achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) exceeding 0.95. Similarly, it excelled in detecting adversarially influenced models, outperforming other black-box methods.QueRE also proved robust and transferable. Its features were successfully applied to out-of-distribution tasks and different LLM configurations, validating its adaptability. The low-dimensional representations facilitated efficient training of simple models, ensuring computational feasibility and robust generalization bounds.Another notable result was QueREs ability to use random sequences of natural language as elicitation prompts. These sequences often matched or exceeded the performance of structured queries, highlighting the methods flexibility and potential for diverse applications without extensive manual prompt engineering.ConclusionQueRE offers a practical and effective approach to understanding and optimizing black-box LLMs. By transforming elicitation responses into actionable features, QueRE provides a scalable and robust framework for predicting model behavior, detecting adversarial influences, and differentiating architectures. Its success in empirical evaluations suggests it is a valuable tool for researchers and practitioners aiming to enhance the reliability and safety of LLMs.As AI systems evolve, methods like QueRE will play a crucial role in ensuring transparency and trustworthiness. Future work could explore extending QueREs applicability to other modalities or refining its elicitation strategies for enhanced performance. For now, QueRE represents a thoughtful response to the challenges posed by modern AI systems.Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Sajjad Ansari+ postsSajjad 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Large language models (LLMs) have become central to natural language processing (NLP), excelling in tasks such as text generation, comprehension, and reasoning. However, their ability to handle longer input sequences is limited by significant computational challenges, particularly memory overhead during inference caused by key-value (KV) caches. Since memory requirements scale linearly with sequence length, this limits the maximum context window that models can effectively process. Existing solutions, such as sparse attention mechanisms and off-chip storage, attempt to mitigate this issue but often introduce trade-offs, such as increased latency or the risk of losing important information. Addressing memory consumption without compromising model performance remains a critical challenge in scaling LLMs for practical applications.A team of researchers from Tsinghua University, Shanghai Qi Zhi Institute, UCLA, and TapTap have introduced Tensor Product Attention (TPA), an attention mechanism designed to alleviate the KV cache bottleneck. TPA leverages tensor decompositions to represent queries, keys, and values (QKV) compactly, significantly reducing the KV cache size during inference. By employing contextual low-rank factorization, TPA achieves substantial memory savings while maintaining or improving model performance. Moreover, it integrates seamlessly with Rotary Position Embedding (RoPE), allowing compatibility with widely-used attention-based architectures like LLaMA. This approach enables TPA to serve as a drop-in replacement for multi-head attention (MHA), forming the basis of the Tensor Product Attention Transformer (T6), a sequence modeling architecture that shows notable performance improvements in language modeling tasks.Technical Details and BenefitsTPA introduces a novel approach to factorizing QKV activations dynamically into low-rank components. Unlike static weight factorization techniques like LoRA, TPA generates contextual representations tailored to the input data. Each tokens Q, K, and V components are expressed as a sum of tensor products of latent factors, which are derived through linear projections of the tokens hidden state. This tensor structure facilitates efficient representation and reduces memory usage.A key advantage of TPA is its integration with RoPE. Traditional low-rank methods face challenges with RoPE due to its dependence on relative positional invariance. TPA resolves this by pre-rotating tensor components, enabling efficient caching and inference while preserving positional information.The memory efficiency of TPA is significant. Standard MHA relies on a full-size KV cache proportional to the number of heads and their dimensions, whereas TPA reduces this requirement by caching only the factorized components. This reduction enables the processing of much longer sequences within the same memory constraints, making it particularly effective for applications requiring extended context windows.Results and InsightsThe researchers evaluated TPA on the FineWeb-Edu100B dataset across various language modeling tasks. The Tensor Product Attention Transformer (T6) consistently outperformed baselines, including MHA, Multi-Query Attention (MQA), Grouped Query Attention (GQA), and Multi-head Latent Attention (MLA).In terms of training and validation loss, TPA demonstrated faster convergence and lower final losses compared to its counterparts. For example, in experiments with large-scale models (773M parameters), TPA achieved significantly lower validation losses than MLA and GQA. Additionally, TPA showed superior perplexity results across multiple configurations, highlighting its efficiency and accuracy.Beyond pretraining metrics, TPA performed exceptionally well in downstream tasks such as ARC, BoolQ, HellaSwag, and MMLU. On zero-shot and two-shot prompts, TPA consistently ranked among the best-performing methods, achieving average accuracies of 51.41% and 53.12%, respectively, for medium-sized models. These findings emphasize TPAs capability to generalize across diverse language tasks effectively.ConclusionTensor Product Attention (TPA) addresses the scalability challenges of large language models by introducing a dynamic, low-rank factorization mechanism that reduces the memory footprint of KV caches while maintaining strong performance. Its compatibility with existing architectures and solid results across various benchmarks make it a practical alternative to traditional attention mechanisms. As the need for longer context processing grows in language models, methods like TPA provide an efficient path forward, combining memory efficiency with robust performance for real-world applications.Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Aswin Ak+ postsAswin 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. Meet 'Height':The only autonomous project management tool (Sponsored)

Imagine having a personal chatbot that can answer questions directly from your documentsbe it PDFs, research papers, or books. With Retrieval-Augmented Generation (RAG), this is not only possible but also straightforward to implement. In this tutorial, well learn how to build a chatbot that interacts with your documents, like PDFs, using Retrieval-Augmented Generation (RAG). Well use Groq for language model inference, Chroma as the vector store, and Gradio for the user interface.By the end, youll have a chatbot capable of answering questions directly from your documents, keeping context of your conversation, and providing concise, accurate answers.What is Retrieval-Augmented Generation (RAG)?Retrieval-Augmented Generation (RAG) is an AI architecture that enhances the capabilities of Large Language Models (LLMs) by integrating an information retrieval system. This system fetches relevant data from external sources, providing the LLM with grounded information to generate more accurate and contextually appropriate responses. By combining the generative abilities of LLMs with real-time data retrieval, RAG reduces inaccuracies and ensures up-to-date information in AI-generated content.PrerequisitesPython Installation: Ensure Python 3.9+ is installed on your system.Groq API Key: Sign up for a Groq account and generate an API key:Visit Groq Console.Navigate to API Keys and create a new key.Copy your API key for use in the project.Dependencies: Install the required libraries:pip install langchain langchain-community langchain-groq gradio sentence-transformers PyPDF2 chromadbThese libraries will help with language processing, building the user interface, model integration, PDF handling, and vector database management.Downloading the PDF ResourceFor this tutorial, well use a publicly available PDF containing information about diseases, their symptoms, and cures. Download the PDF and save it in your project directory (you are free to use any pdf).Step 1: Extracting Text from the PDFWell use PyPDF2 to extract text from the PDF:from PyPDF2 import PdfReaderdef extract_text_from_pdf(pdf_path): reader = PdfReader(pdf_path) text = "" for page in reader.pages: text += page.extract_text() return textpdf_path = 'diseases.pdf' # Replace with your PDF pathpdf_text = extract_text_from_pdf(pdf_path)Step 2: Split the Text into ChunksLong documents are divided into smaller, manageable chunks for processing.from langchain.text_splitter import RecursiveCharacterTextSplitterdef split_text_into_chunks(text, chunk_size=2000, chunk_overlap=200): text_splitter = RecursiveCharacterTextSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) return text_splitter.split_text(text)text_chunks = split_text_into_chunks(pdf_text)Step 3: Create a Vector Store with ChromaWell embed the text chunks using a pre-trained model and store them in a Chroma vector database.from langchain.embeddings import SentenceTransformerEmbeddingsfrom langchain.vectorstores import Chromaembedding_model = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")vector_store = Chroma( collection_name="disease_info", embedding_function=embedding_model, persist_directory="./chroma_db")vector_store.add_texts(texts=text_chunks)Step 4: Initialize the Groq Language ModelTo use Groqs language model, set your API key and initialize the ChatGroq instance.import osfrom langchain_groq import ChatGroqos.environ["GROQ_API_KEY"] = 'your_groq_api_key_here' # Replace with your API keyllm = ChatGroq(model="mixtral-8x7b-32768", temperature=0.1)Step 5: Create the Conversational Retrieval ChainWith LangChains ConversationalRetrievalChain, we can link the language model and the vector database.from langchain.chains import ConversationalRetrievalChainretrieval_chain = ConversationalRetrievalChain.from_llm( llm=llm, retriever=vector_store.as_retriever(topk=3), return_source_documents=True)Step 6: Implement the Chatbot LogicWe define the logic for maintaining conversation history and generating responses.conversation_history = []def get_response(user_query): response = retrieval_chain({ "question": user_query, "chat_history": conversation_history }) conversation_history.append((user_query, response['answer'])) return response['answer']Step 7: Build the User Interface with GradioFinally, create a Gradio interface to interact with the chatbot.import gradio as grdef chat_interface(user_input, history): response = get_response(user_input) history.append((user_input, response)) return history, historywith gr.Blocks() as demo: chatbot = gr.Chatbot() state = gr.State([]) with gr.Row(): user_input = gr.Textbox(show_label=False, placeholder="Enter your question...") submit_btn = gr.Button("Send") submit_btn.click(chat_interface, inputs=[user_input, state], outputs=[chatbot, state])Running the CodeSave the script as app.py and runpython app.pyHurray! You are done. The Gradio interface will launch, allowing you to chat with your document.But why stop here? You can go further by trying to build any of the following functionalities in the chatbot.Enhanced Vector Store: Use other vector databases like Milvus or Pinecone for scalability.Fine-tuned Models: Experiment with fine-tuned Groq models for domain-specific accuracy.Multi-Document Support: Extend the system to handle multiple documents.Better Context Handling: Refine conversational logic to better manage longer chat histories.Custom UI: Design a more polished user interface with advanced styling and features.Congratulations! Youve successfully built a document-based chatbot using Groq and LangChain. Experiment with improvements and build something amazing! Resources:https://nios.ac.in/media/documents/SrSec314NewE/Lesson-29.pdfLangChain (https://www.langchain.com/)Groq (https://groq.com/)Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Vineet Kumar+ postsVineet Kumar is a consulting intern at MarktechPost. He is currently pursuing his BS from the Indian Institute of Technology(IIT), Kanpur. He is a Machine Learning enthusiast. He is passionate about research and the latest advancements in Deep Learning, Computer Vision, and related fields. Meet 'Height':The only autonomous project management tool (Sponsored)

Large Language Models (LLMs) based on Transformer architectures have revolutionized sequence modeling through their remarkable in-context learning capabilities and ability to scale effectively. These models depend on attention modules that function as associative memory blocks, storing and retrieving key-value associations. However, this mechanism has a significant limitation: the computational requirements grow quadratically with the input length. This quadratic complexity in both time and memory poses substantial challenges when dealing with real-world applications such as language modeling, video understanding, and long-term time series forecasting, where the context windows can become extremely large, limiting the practical applicability of Transformers in these crucial domains.Researchers have explored multiple approaches to address the computational challenges of Transformers, with three main categories emerging. First, Linear Recurrent Models have gained attention for efficient training and inference, evolving from first-generation models like RetNet and RWKV with data-independent transition matrices to second-generation architectures incorporating gating mechanisms like Griffin and RWKV6. Next, Transformer-based architectures have attempted to optimize the attention mechanism through I/O-aware implementations, sparse attention matrices, and kernel-based approaches. Lastly, Memory-augmented models focus on persistent and contextual memory designs. However, these solutions often face limitations such as memory overflow, fixed-size constraints, etc.Google Researchers has proposed a novel neural long-term memory module designed to enhance attention mechanisms by enabling access to historical context while maintaining efficient training and inference. The innovation lies in creating a complementary system where attention serves as short-term memory for precise dependency modeling within limited contexts even though the neural memory component functions as long-term storage for persistent information. This dual-memory approach forms the foundation of a new architectural family called Titans, which comes in three variants, each offering different strategies for memory integration. The system shows particular promise in handling extremely long contexts, successfully processing sequences beyond 2 million tokens.The Titans architecture introduces a complex three-part design to integrate memory capabilities effectively. The system consists of three distinct hyper-heads: a Core module utilizing attention with limited window size for short-term memory and primary data processing, a Long-term Memory branch implementing the neural memory module for storing historical information, and a Persistent Memory component containing learnable, data-independent parameters. The architecture is implemented with several technical optimizations, including residual connections, SiLU activation functions, and 2-norm normalization for queries and keys. Moreover, it uses 1D depthwise-separable convolution layers after query, key, and value projections, along with normalization and gating mechanisms.The experimental results demonstrate Titans superior performance across multiple configurations. All three variants MAC, MAG, and MAL outperform hybrid models like Samba and Gated DeltaNet-H2, with the neural memory module proving to be the key differentiator. Among the variants, MAC and MAG show strong performance, especially in handling longer dependencies, surpassing the MAL-style combinations commonly used in existing hybrid models. In needle-in-a-haystack (NIAH) tasks, Titans outperforms baselines across sequences ranging from 2K to 16K tokens. This superior performance stems from three key advantages: efficient memory management, deep non-linear memory capabilities, and effective memory erasure functionality.In conclusion, researchers from Google Research introduced a groundbreaking neural long-term memory system that functions as a meta-in-context learner, capable of adaptive memorization during test time. This recurrent model is more effective in identifying and storing surprising patterns in the data stream, offering more complex memory management than traditional methods. The system has proven its superiority in handling extensive contexts through the implementation of three distinct variants in the Titans architecture family. The ability to effectively process sequences exceeding 2 million tokens while maintaining superior accuracy marks a significant advancement in the sequence modeling field and opens new possibilities for handling increasingly complex tasks.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit. Recommend Open-Source Platform: Parlant is a framework that transforms how AI agents make decisions in customer-facing scenarios. (Promoted)The post Google AI Research Introduces Titans: A New Machine Learning Architecture with Attention and a Meta in-Context Memory that Learns How to Memorize at Test Time appeared first on MarkTechPost.

Video understanding has long presented unique challenges for AI researchers. Unlike static images, videos involve intricate temporal dynamics and spatial-temporal reasoning, making it difficult for models to generate meaningful descriptions or answer context-specific questions. Issues like hallucination, where models fabricate details, further compromise the reliability of existing systems. Despite advancements with models such as GPT-4o and Gemini-1.5-Pro, achieving human-level video comprehension remains a complex task. Accurate event perception and sequence understanding, coupled with reducing hallucination, are crucial hurdles to overcome.ByteDance researchers have introduced Tarsier2, a large vision-language model (LVLM) with 7 billion parameters, designed to address the core challenges of video understanding. Tarsier2 excels in generating detailed video descriptions, surpassing models like GPT-4o and Gemini-1.5-Pro. Beyond video descriptions, it demonstrates strong performance in tasks such as question-answering, grounding, and embodied intelligence. With an expanded pre-training dataset of 40 million video-text pairs, fine-grained temporal alignment, and Direct Preference Optimization (DPO) during training, Tarsier2 achieves noteworthy improvements. For example, on the DREAM-1K dataset, it outperforms GPT-4o by 2.8% and Gemini-1.5-Pro by 5.8% in F1 scores.Technical Innovations and BenefitsTarsier2 integrates several technical advancements to enhance performance. The models architecture includes a vision encoder, vision adaptor, and a large language model, combined in a three-stage training process:Pre-training: A dataset of 40 million video-text pairs, enriched with commentary videos that capture both low-level actions and high-level plot details, provides a solid foundation for learning.Supervised Fine-Tuning (SFT): Fine-grained temporal alignment during this stage ensures the model accurately associates events with corresponding video frames, reducing hallucination and improving precision.Direct Preference Optimization (DPO): This phase employs automatically generated preference data to refine the models decision-making and minimize hallucinations.These advancements not only improve the generation of detailed video descriptions but also enhance the models overall versatility across video-centric tasks.Results and InsightsTarsier2 achieves impressive results across multiple benchmarks. Human evaluations reveal an 8.6% performance advantage over GPT-4o and a 24.9% improvement over Gemini-1.5-Pro. On the DREAM-1K benchmark, it becomes the first model to exceed a 40% overall recall score, highlighting its ability to detect and describe dynamic actions comprehensively. Furthermore, it sets new performance records on 15 public benchmarks, including tasks like video question-answering and temporal reasoning. In the E.T. Bench-Grounding test, Tarsier2 achieves the highest mean F1 score of 35.5%, underlining its capabilities in temporal understanding. Ablation studies further underscore the critical role of the expanded pre-training dataset and DPO phase in enhancing performance metrics like F1 scores and accuracy.ConclusionTarsier2 marks a significant step forward in video understanding by addressing key challenges such as temporal alignment, hallucination reduction, and data scarcity. ByteDance researchers have delivered a model that not only outperforms leading alternatives in key metrics but also provides a scalable framework for future advancements. As video content continues to dominate digital media, models like Tarsier2 hold immense potential for applications ranging from content creation to intelligent surveillance.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Aswin Ak+ postsAswin 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. Meet 'Height':The only autonomous project management tool (Sponsored)

The study of artificial intelligence has witnessed transformative developments in reasoning and understanding complex tasks. The most innovative developments are large language models (LLMs) and multimodal large language models (MLLMs). These systems can process textual and visual data, allowing them to analyze intricate tasks. Unlike traditional approaches that base their reasoning skills on verbal means, multimodal systems attempt to mimic human cognition by combining textual reasoning with visual thinking and, therefore, could be used more effectively to solve more varied challenges.The problem so far is that these models cannot interlink textual and visual reasoning together in dynamic environments. Models developed for reasoning perform well on text-based or image-based inputs but cannot execute simultaneously when both are input. Spatial reasoning tasks like maze navigation or the interpretation of dynamic layouts show weaknesses in these models. Integrated reasoning capabilities cannot be catered to within these models. Thus, it creates limitations in the models adaptability and interpretability, especially where the task is to understand and manipulate visual patterns and the instructions given in words.Several approaches have been proposed to deal with these issues. Chain-of-thought (CoT) prompting improves reasoning by producing step-by-step textual traces. It is inherently text-based and does not handle tasks requiring spatial understanding. Other approaches are visual input methods through external tools such as image captioning or scene graph generation, allowing models to process visual and textual data. While effective to some extent, these methods rely heavily on separate visual modules, making them less flexible and prone to errors in complex tasks.Researchers from Microsoft Research, the University of Cambridge, and the Chinese Academy of Sciences introduced the Multimodal Visualization-of-Thought (MVoT) framework to address these limitations. This novel reasoning paradigm enables models to generate visual reasoning traces interleaved with verbal ones, offering an integrated approach to multimodal reasoning. MVoT embeds visual thinking capabilities directly into the models architecture, thus eliminating the dependency on external tools making it a more cohesive solution for complex reasoning tasks.Using Chameleon-7B, an autoregressive MLLM fine-tuned for multimodal reasoning tasks, the researchers implemented MVoT. This method involves token discrepancy loss to close the representational gap between text and image tokenization processes for outputting quality visuals. MVoT processes multimodal inputs step-by-step through creating verbal and visual reasoning traces. For instance, in spatial tasks such as maze navigation, the model produces intermediate visualizations corresponding to the reasoning steps, enhancing both its interpretability and performance. This native visual reasoning capability, integrated into the framework, makes it more similar to human cognition, thus providing a more intuitive approach to understanding and solving complex tasks.MVoT outperformed the state-of-the-art models in extensive experiments on multiple spatial reasoning tasks, including MAZE, MINI BEHAVIOR, and FROZEN LAKE. The framework reached a high accuracy of 92.95% on maze navigation tasks, which surpasses traditional CoT methods. In the MINI BEHAVIOR task that requires understanding interaction with spatial layouts, MVoT reached an accuracy of 95.14%, demonstrating its applicability in dynamic environments. In the FROZEN LAKE task, which is well-known for being complex due to fine-grained spatial details, MVoTs robustness reached an accuracy of 85.60%, surpassing CoT and other baselines. MVoT consistently improved in challenging scenarios, especially those involving intricate visual patterns and spatial reasoning.In addition to performance metrics, MVoT showed improved interpretability by generating visual thought traces that complement verbal reasoning. This capability allowed users to follow the models reasoning process visually, making it easier to understand and verify its conclusions. Unlike CoT, based only on the textual description, MVoTs multimodal reasoning approach reduced errors caused by poor textual representation. For example, in the FROZEN LAKE task, MVoT sustained stable performance at increased complexity concerning its environment, thereby demonstrating robustness and reliability.This study, therefore, redefines the scope of reasoning capabilities of artificial intelligence with MVoT by integrating text and vision into reasoning tasks. Using token discrepancy loss ensures visual reasoning aligns seamlessly with textual processing. This will bridge the critical gap in current methods. Superior performance and better interpretability will mark MVoT as a landmark step toward multimodal reasoning that can open doors to more complex and challenging AI systems in real-world scenarios.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Nikhil+ postsNikhil 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. Meet 'Height':The only autonomous project management tool (Sponsored)