Announcing new features and models for the Gemini API, with the introduction of Gemini 2.5 Flash Preview with improved reasoning and efficiency, Gemini 2.5 Pro and Flash text-to-speech supporting multiple languages and speakers, and Gemini 2.5 Flash

Google Gemini models offer several advantages when building AI agents, such as advanced reasoning, function calling, multimodality, and large context window capabilities. Open-source frameworks like LangGraph, CrewAI, LlamaIndex, and Composio can be

Maryam Ashoori, Head of Product for watsonx.ai at IBM, joins Ryan and Eira to talk about the complexity of enterprise AI, the role of governance, the AI skill gap among developers, how AI coding tools impact developer productivity, what chain-of-thou

With its first gameplay reveal during this year's Day of the Devs, indie developer Something We Made finally showed off the sequel to their inaugural title TOEM: A Photo Adventure from 2021. This charming photography adventure brought an easygoing monochrome adventure to hundreds of fans and left us wanting more. TOEM 2 opens with the same plucky character from the first title dropped into the world with little more than ambition and a trusty camera. While the first game focused on ascending the TOEM mountain, I didn't catch nary a whiff of the actual motivations and reasoning for the journey this time around. Instead, I was given free reign to meander around the idyllic village and solve a variety of problems for the townsfolk, from trying to retrieve a potion of liquid courage for a scaredy-cat knight to taking photos of three goats in order to permit a bridge troll to let me pass. In my twenty or so minutes of play, I was able to help solve the small-scale problems of four individuals and be rewarded with a stamp for my collection each time. The camera remains the player's best tool in the world of TOEM 2 with players not only able to use their photography skills to solve the plights of the ordinary person, but also a variety of attachments to use as tools in your adventure. The very first unlockable upgrade I earned for my camera was a hammer upgrade that let me smash through select stone blocks that hindered my progression. Using the hammer is just like the other attachments from the first TOEM: simply point and shoot (or in this case, tap on the rocks). It took a moment to realize that there's a small minigame to using the hammer with players having to tap out morse code with short and long taps in order to break those rocks. 2 of 9 One puzzle I encountered was take a 3x3 cube of blocks and chisel away to make a matching sculpture to the quest giver. Rather than trying to memorize the layout or run back and forth between the source sculpture and what I was crafting, I stopped to take photos of each side of the cube and use those to remember the requested shape. The developers from Something We Made seemed surprised that this was a valid solution to their puzzle and it was nice to see the camera being used as a note taking device as well as the tool you'll use to take pictures of every animal around the landscape. New to TOEM 2 is also the addition of jumping, and in true platformer style, I had to test it out by jumping around on top of any short wall I could find and try to scale up the world. I couldn't find any unintended out-of-bounds areas, but I was at least able to find some climbable areas that would lead to new hats for the playable character. With jumping now on the menu, I wouldn't be surprised if the Honk attachment gets phased out for TOEM 2. Sadly, there's still a fair bit of time before TOEM 2 is ready to be in players' hands. Developer Something We Made and publisher popagenda have penciled in this quaint photographical adventure for 2026 across a slew of unannounced consoles as well as PC. Deal of the Day

Autoregressive image generation has been shaped by advances in sequential modeling, originally seen in natural language processing. This field focuses on generating images one token at a time, similar to how sentences are constructed in language models. The appeal of this approach lies in its ability to maintain structural coherence across the image while allowing for high levels of control during the generation process. As researchers began to apply these techniques to visual data, they found that structured prediction not only preserved spatial integrity but also supported tasks like image manipulation and multimodal translation effectively. Despite these benefits, generating high-resolution images remains computationally expensive and slow. A primary issue is the number of tokens needed to represent complex visuals. Raster-scan methods that flatten 2D images into linear sequences require thousands of tokens for detailed images, resulting in long inference times and high memory consumption. Models like Infinity need over 10,000 tokens for a 1024×1024 image. This becomes unsustainable for real-time applications or when scaling to more extensive datasets. Reducing the token burden while preserving or improving output quality has become a pressing challenge. Efforts to mitigate token inflation have led to innovations like next-scale prediction seen in VAR and FlexVAR. These models create images by predicting progressively finer scales, which imitates the human tendency to sketch rough outlines before adding detail. However, they still rely on hundreds of tokens—680 in the case of VAR and FlexVAR for 256×256 images. Moreover, approaches like TiTok and FlexTok use 1D tokenization to compress spatial redundancy, but they often fail to scale efficiently. For example, FlexTok’s gFID increases from 1.9 at 32 tokens to 2.5 at 256 tokens, highlighting a degradation in output quality as the token count grows. Researchers from ByteDance introduced DetailFlow, a 1D autoregressive image generation framework. This method arranges token sequences from global to fine detail using a process called next-detail prediction. Unlike traditional 2D raster-scan or scale-based techniques, DetailFlow employs a 1D tokenizer trained on progressively degraded images. This design allows the model to prioritize foundational image structures before refining visual details. By mapping tokens directly to resolution levels, DetailFlow significantly reduces token requirements, enabling images to be generated in a semantically ordered, coarse-to-fine manner. The mechanism in DetailFlow centers on a 1D latent space where each token contributes incrementally more detail. Earlier tokens encode global features, while later tokens refine specific visual aspects. To train this, the researchers created a resolution mapping function that links token count to target resolution. During training, the model is exposed to images of varying quality levels and learns to predict progressively higher-resolution outputs as more tokens are introduced. It also implements parallel token prediction by grouping sequences and predicting entire sets at once. Since parallel prediction can introduce sampling errors, a self-correction mechanism was integrated. This system perturbs certain tokens during training and teaches subsequent tokens to compensate, ensuring that final images maintain structural and visual integrity. The results from the experiments on the ImageNet 256×256 benchmark were noteworthy. DetailFlow achieved a gFID score of 2.96 using only 128 tokens, outperforming VAR at 3.3 and FlexVAR at 3.05, both of which used 680 tokens. Even more impressive, DetailFlow-64 reached a gFID of 2.62 using 512 tokens. In terms of speed, it delivered nearly double the inference rate of VAR and FlexVAR. A further ablation study confirmed that the self-correction training and semantic ordering of tokens substantially improved output quality. For example, enabling self-correction dropped the gFID from 4.11 to 3.68 in one setting. These metrics demonstrate both higher quality and faster generation compared to established models. By focusing on semantic structure and reducing redundancy, DetailFlow presents a viable solution to long-standing issues in autoregressive image generation. The method’s coarse-to-fine approach, efficient parallel decoding, and ability to self-correct highlight how architectural innovations can address performance and scalability limitations. Through their structured use of 1D tokens, the researchers from ByteDance have demonstrated a model that maintains high image fidelity while significantly reducing computational load, making it a valuable addition to image synthesis research. Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/Teaching AI to Say ‘I Don’t Know’: A New Dataset Mitigates Hallucinations from Reinforcement FinetuningNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces LLaDA-V: A Purely Diffusion-Based Multimodal Large Language Model for Visual Instruction Tuning and Multimodal ReasoningNikhilhttps://www.marktechpost.com/author/nikhil0980/NVIDIA AI Introduces Fast-dLLM: A Training-Free Framework That Brings KV Caching and Parallel Decoding to Diffusion LLMsNikhilhttps://www.marktechpost.com/author/nikhil0980/Meet NovelSeek: A Unified Multi-Agent Framework for Autonomous Scientific Research from Hypothesis Generation to Experimental Validation

One of the key use cases for generative AI involves answering questions over private datasets, with retrieval-augmented generation (RAG) as the go-to framework. As new RAG techniques emerge, there’s a growing need to benchmark their performance across diverse datasets and metrics.  To meet this need, we’re introducing BenchmarkQED, a new suite of tools that automates RAG benchmarking at scale, available on GitHub (opens in new tab). It includes components for query generation, evaluation, and dataset preparation, each designed to support rigorous, reproducible testing.   BenchmarkQED complements the RAG methods in our open-source GraphRAG library, enabling users to run a GraphRAG-style evaluation across models, metrics, and datasets. GraphRAG uses a large language model (LLM) to generate and summarize entity-based knowledge graphs, producing more comprehensive and diverse answers than standard RAG for large-scale tasks.  In this post, we walk through the core components of BenchmarkQED that contribute to the overall benchmarking process. We also share some of the latest benchmark results comparing our LazyGraphRAG system to competing methods, including a vector-based RAG with a 1M-token context window, where the leading LazyGraphRAG configuration showed significant win rates across all combinations of quality metrics and query classes. In the paper, we distinguish between local queries, where answers are found in a small number of text regions, and sometimes even a single region, and global queries, which require reasoning over large portions of or even the entire dataset.  Conventional vector-based RAG excels at local queries because the regions containing the answer to the query resemble the query itself and can be retrieved as the nearest neighbor in the vector space of text embeddings. However, it struggles with global questions, such as, “What are the main themes of the dataset?” which require understanding dataset qualities not explicitly stated in the text.   AutoQ: Automated query synthesis This limitation motivated the development of GraphRAG a system designed to answer global queries. GraphRAG’s evaluation requirements subsequently led to the creation of AutoQ, a method for synthesizing these global queries for any dataset. AutoQ extends this approach by generating synthetic queries across the spectrum of queries, from local to global. It defines four distinct classes based on the source and scope of the query (Figure 1, top) forming a logical progression along the spectrum (Figure 1, bottom). Figure 1. Construction of a 2×2 design space for synthetic query generation with AutoQ, showing how the four resulting query classes map onto the local-global query spectrum.  AutoQ can be configured to generate any number and distribution of synthetic queries along these classes, enabling consistent benchmarking across datasets without requiring user customization. Figure 2 shows the synthesis process and sample queries from each class, using an AP News dataset. Figure 2. Synthesis process and example query for each of the four AutoQ query classes.  About Microsoft Research Advancing science and technology to benefit humanity View our story Opens in a new tab AutoE: Automated evaluation framework  Our evaluation of GraphRAG focused on analyzing key qualities of answers to global questions. The following qualities were used for the current evaluation: Comprehensiveness: Does the answer address all relevant aspects of the question?  Diversity: Does it present varied perspectives or insights?  Empowerment: Does it help the reader understand and make informed judgments?  Relevance: Does it address what the question is specifically asking?   The AutoE component scales evaluation of these qualities using the LLM-as-a-Judge method. It presents pairs of answers to an LLM, along with the query and target metric, in counterbalanced order. The model determines whether the first answer wins, loses, or ties with the second. Over a set of queries, whether from AutoQ or elsewhere, this produces win rates between competing methods. When ground truth is available, AutoE can also score answers on correctness, completeness, and related metrics. An illustrative evaluation is shown in Figure 3. Using a dataset of 1,397 AP News articles on health and healthcare, AutoQ generated 50 queries per class (200 total). AutoE then compared LazyGraphRAG to a competing RAG method, running six trials per query across four metrics, using GPT-4.1 as a judge. These trial-level results were aggregated using metric-based win rates, where each trial is scored 1 for a win, 0.5 for a tie, and 0 for a loss, and then averaged to calculate the overall win rate for each RAG method. Figure 3. Win rates of four LazyGraphRAG (LGR) configurations across methods, broken down by the AutoQ query class and averaged across AutoE’s four metrics: comprehensiveness, diversity, empowerment, and relevance. LazyGraphRAG outperforms comparison conditions where the bar is above 50%. The four LazyGraphRAG conditions (LGR_b200_c200, LGR_b50_c200, LGR_b50_c600, LGR_b200_c200_mini) differ by query budget (b50, b200) and chunk size (c200, c600). All used GPT-4o mini for relevance tests and GPT-4o for query expansion (to five subqueries) and answer generation, except for LGR_b200_c200_mini, which used GPT-4o mini throughout. Comparison systems were GraphRAG (Local, Global, and Drift Search), Vector RAG with 8k- and 120k-token windows, and three published methods: LightRAG (opens in new tab), RAPTOR (opens in new tab), and TREX (opens in new tab). All methods were limited to the same 8k tokens for answer generation. GraphRAG Global Search used level 2 of the community hierarchy. LazyGraphRAG outperformed every comparison condition using the same generative model (GPT-4o), winning all 96 comparisons, with all but one reaching statistical significance. The best overall performance came from the larger budget, smaller chunk size configuration (LGR_b200_c200). For DataLocal queries, the smaller budget (LGR_b50_c200) performed slightly better, likely because fewer chunks were relevant. For ActivityLocal queries, the larger chunk size (LGR_b50_c600) had a slight edge, likely because longer chunks provide a more coherent context. Competing methods performed relatively better on the query classes for which they were designed: GraphRAG Global for global queries, Vector RAG for local queries, and GraphRAG Drift Search, which combines both strategies, posed the strongest challenge overall. Increasing Vector RAG’s context window from 8k to 120k tokens did not improve its performance compared to LazyGraphRAG. This raised the question of how LazyGraphRAG would perform against Vector RAG with 1-million token context window containing most of the dataset. Figure 4 shows the follow-up experiment comparing LazyGraphRAG to Vector RAG using GPT-4.1 that enabled this comparison. Even against the 1M-token window, LazyGraphRAG achieved higher win rates across all comparisons, failing to reach significance only for the relevance of answers to DataLocal queries. These queries tend to benefit most from Vector RAG’s ranking of directly relevant chunks, making it hard for LazyGraphRAG to generate answers that have greater relevance to the query, even though these answers may be dramatically more comprehensive, diverse, and empowering overall. Figure 4. Win rates of LazyGraphRAG (LGR) over Vector RAG across different context window sizes, broken down by the four AutoQ query classes and four AutoE metrics: comprehensiveness, diversity, empowerment, and relevance. Bars above 50% indicate that LazyGraphRAG outperformed the comparison condition.  AutoD: Automated data sampling and summarization Text datasets have an underlying topical structure, but the depth, breadth, and connectivity of that structure can vary widely. This variability makes it difficult to evaluate RAG systems consistently, as results may reflect the idiosyncrasies of the dataset rather than the system’s general capabilities. The AutoD component addresses this by sampling datasets to meet a target specification, defined by the number of topic clusters (breadth) and the number of samples per cluster (depth). This creates consistency across datasets, enabling more meaningful comparisons, as structurally aligned datasets lead to comparable AutoQ queries, which in turn support consistent AutoE evaluations. AutoD also includes tools for summarizing input or output datasets in a way that reflects their topical coverage. These summaries play an important role in the AutoQ query synthesis process, but they can also be used more broadly, such as in prompts where context space is limited. Since the release of the GraphRAG paper, we’ve received many requests to share the dataset of the Behind the Tech (opens in new tab) podcast transcripts we used in our evaluation. An updated version of this dataset is now available in the BenchmarkQED repository (opens in new tab), alongside the AP News dataset containing 1,397 health-related articles, licensed for open release.   We hope these datasets, together with the BenchmarkQED tools (opens in new tab), help accelerate benchmark-driven development of RAG systems and AI question-answering. We invite the community to try them on GitHub (opens in new tab).  Opens in a new tab

Text embedding and reranking are foundational to modern information retrieval systems, powering applications such as semantic search, recommendation systems, and retrieval-augmented generation (RAG). However, current approaches often face key challenges—particularly in achieving both high multilingual fidelity and task adaptability without relying on proprietary APIs. Existing models frequently fall short in scenarios requiring nuanced semantic understanding across multiple languages or domain-specific tasks like code retrieval and instruction following. Moreover, most open-source models either lack scale or flexibility, while commercial APIs remain costly and closed. Qwen3-Embedding and Qwen3-Reranker: A New Standard for Open-Source Embedding Alibaba’s Qwen Team has unveiled the Qwen3-Embedding and Qwen3-Reranker Series—models that set a new benchmark in multilingual text embedding and relevance ranking. Built on the Qwen3 foundation models, the series includes variants in 0.6B, 4B, and 8B parameter sizes and supports a wide range of languages (119 in total), making it one of the most versatile and performant open-source offerings to date. These models are now open-sourced under the Apache 2.0 license on Hugging Face, GitHub, and ModelScope, and are also accessible via Alibaba Cloud APIs. These models are optimized for use cases such as semantic retrieval, classification, RAG, sentiment analysis, and code search—providing a strong alternative to existing solutions like Gemini Embedding and OpenAI’s embedding APIs. Technical Architecture Qwen3-Embedding models adopt a dense transformer-based architecture with causal attention, producing embeddings by extracting the hidden state corresponding to the [EOS] token. Instruction-awareness is a key feature: input queries are formatted as {instruction} {query}<|endoftext|>, enabling task-conditioned embeddings. The reranker models are trained with a binary classification format, judging document-query relevance in an instruction-guided manner using a token likelihood-based scoring function. The models are trained using a robust multi-stage training pipeline: Large-scale weak supervision: 150M synthetic training pairs generated using Qwen3-32B, covering retrieval, classification, STS, and bitext mining across languages and tasks. Supervised fine-tuning: 12M high-quality data pairs are selected using cosine similarity (>0.7), fine-tuning performance in downstream applications. Model merging: Spherical linear interpolation (SLERP) of multiple fine-tuned checkpoints ensures robustness and generalization. This synthetic data generation pipeline enables control over data quality, language diversity, task difficulty, and more—resulting in a high degree of coverage and relevance in low-resource settings. Performance Benchmarks and Insights The Qwen3-Embedding and Qwen3-Reranker series demonstrate strong empirical performance across several multilingual benchmarks. On MMTEB (216 tasks across 250+ languages), Qwen3-Embedding-8B achieves a mean task score of 70.58, surpassing Gemini and GTE-Qwen2 series. On MTEB (English v2): Qwen3-Embedding-8B reaches 75.22, outperforming other open models including NV-Embed-v2 and GritLM-7B. On MTEB-Code: Qwen3-Embedding-8B leads with 80.68, excelling in applications like code retrieval and Stack Overflow QA. For reranking: Qwen3-Reranker-0.6B already outperforms Jina and BGE rerankers. Qwen3-Reranker-8B achieves 81.22 on MTEB-Code and 72.94 on MMTEB-R, marking state-of-the-art performance. Ablation studies confirm the necessity of each training stage. Removing synthetic pretraining or model merging led to significant performance drops (up to 6 points on MMTEB), emphasizing their contributions. Conclusion Alibaba’s Qwen3-Embedding and Qwen3-Reranker Series present a robust, open, and scalable solution to multilingual and instruction-aware semantic representation. With strong empirical results across MTEB, MMTEB, and MTEB-Code, these models bridge the gap between proprietary APIs and open-source accessibility. Their thoughtful training design—leveraging high-quality synthetic data, instruction-tuning, and model merging—positions them as ideal candidates for enterprise applications in search, retrieval, and RAG pipelines. By open-sourcing these models, the Qwen team not only pushes the boundaries of language understanding but also empowers the broader community to innovate on top of a solid foundation. Check out the Paper, Technical details, Qwen3-Embedding and Qwen3-Reranker. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Asif RazzaqWebsite |  + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Coding Guide to Building an Iterative AI Workflow Agent Using LangGraph and GeminiAsif Razzaqhttps://www.marktechpost.com/author/6flvq/From Clicking to Reasoning: WebChoreArena Benchmark Challenges Agents with Memory-Heavy and Multi-Page TasksAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Mistral AI Introduces Mistral Code: A Customizable AI Coding Assistant for Enterprise WorkflowsAsif Razzaqhttps://www.marktechpost.com/author/6flvq/NVIDIA AI Releases Llama Nemotron Nano VL: A Compact Vision-Language Model Optimized for Document Understanding

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Google has released an updated preview of​​ Gemini 2.5 Pro, its “most intelligent” model, first announced in March and upgraded in May, as a preview, intending to release the same model to general availability in a couple of weeks.  Enterprises can test building new applications or replace earlier versions with an updated version of the “I/O edition” of Gemini 2.5 Pro that, according to a blog post by Google, is more creative in its responses and outperforms other models in coding and reasoning.  During its annual I/O developer conference in May, Google announced that it updated Gemini 2.5 Pro to be better than its earlier iteration, which it quietly released. Google DeepMind CEO Demis Hassabis said the I/O edition is the company’s best coding model yet.  But this new preview, called Gemini 2.5 Pro Preview 06-05 Thinking, is even better than the I/O edition. The stable version Google plans to release publicly is “ready for enterprise-scale capabilities.” The I/O edition, or gemini-2.5-pro-preview-05-06, was first made available to developers and enterprises in May through Google AI Studio and Vertex AI. Gemini 2.5 Pro Preview 06-05 Thinking can be accessed via the same platforms.  Performance metrics This new version of Gemini 2.5 Pro performs even better than the first release.  Google said the new version of Gemini 2.5 Pro improved by 24 points in LMArena and by 35 points in WebDevArena, where it currently tops the leaderboard. The company’s benchmark tests showed that the model outscored competitors like OpenAI’s o3, o3-mini, and o4-mini, Anthropic’s Claude 4 Opus, Grok 3 Beta from xAI and DeepSeek R1.  “We’ve also addressed feedback from our previous 2.5 Pro releases, improving its style and structure — it can be more creative with better-formatted responses,” Google said in the blog post.  What enterprises can expect Google’s continuous improvement of Gemini 2.5 Pro might be confusing for many, but Google previously framed these as a response to community feedback. Pricing for the new version is $1.25 per million tokens without caching for inputs and $10 for the output price.  When the very first version of Gemini 2.5 Pro launched in March, VentureBeat’s Matt Marshall called it “the smartest model you’re not using.” Since then, Google has integrated the model into many of its new applications and services, including “Deep Think,” where Gemini considers multiple hypotheses before responding.  The release of Gemini 2.5 Pro, and its two upgraded versions, revived Google’s place in the large language model space after competitors like DeepSeek and OpenAI diverted the industry’s attention to their reasoning models.  In just a few hours of announcing the updated Gemini 2.5 Pro, developers have already begun playing around with it. While many found the update to live up to Google’s promise of being faster, the jury is still out if this latest Gemini 2.5 Pro does actually perform better.  Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

Pentagram's latest project introduces a striking new identity for TwelveLabs, an AI company redefining how machines comprehend video. At its heart, the rebrand marks a conceptual shift, positioning video not as a linear sequence of frames but as a volume. For TwelveLabs, this reframing is more than a technological advancement, as it's setting them apart in the fast-moving AI landscape. For Jody Hudson-Powell and the Pentagram team, it became the foundation for the entire creative direction. "The 'video as volume' idea became our way of visualising their technology," Jody explains. "Traditionally, AI sees video as separate frames – individual moments, isolated. But TwelveLabs understands everything at once, an interconnected whole. This shift in perspective became the core idea guiding all our design choices." Turning such an abstract and dynamic concept into a tangible brand identity was no small feat. In a sector often awash with dense data visualisations and corporate clichés, Pentagram's work for TwelveLabs stands out for its clarity and restraint. Described by the team as "smart, elegant, alive", the identity not only enhances the product's appearance but also helps explain it. "Abstract concepts can easily get complicated, but by staying closely focused on 'video as volume,' every decision felt purposeful and naturally clear," says Jody. A key part of this clarity is the system's use of diagrams and modular layouts. These elements do the heavy lifting when it comes to representing TwelveLabs' complex technology in a way that's accessible to a wide range of audiences, from engineers and developers to enterprise clients and creatives. "Complexity becomes understandable when you strip things back to what's truly important," Jody says. "We spent time refining the diagrams to say just enough – clear for everyone but authentic to those who really know the tech. When engineers see their work clearly represented, and others intuitively understand it, you've found some interesting ground." What's striking is how the identity balances the technical with the emotive. It's a visual language that feels structured but not cold, intelligent but not aloof. According to Jody, this tension was carefully calibrated. "Technology can feel distant, cold, but it's built around human experiences: images, memories, moments," he explains. "Our system provided clarity and structure, creating natural space for these human elements to emerge, balancing intelligence and warmth." Motion, too, plays a critical role in bringing the brand to life. Given that TwelveLabs' platform revolves around continuous, connected reasoning, it made sense for motion to echo that logic, being subtle but purposeful. "Motion always means something, even subtly," says Jody. "Every bit of movement mirrored the continuous reasoning of the platform. Allowing room for gentle expressiveness and even playfulness didn't take away from clarity – it enhanced it." One of the most distinctive elements of the new identity is the horse symbol. On the surface, it's a nod to Eadweard Muybridge's pioneering photographic studies of motion and an iconic reference for any visual technology. But there's a deeper connection, as TwelveLabs already named its AI models after horses. The Pentagram team simply brought that story to the forefront. "Often, the best ideas are already there, just waiting to be noticed," says Jody. "We simply amplified that story, connecting it back to Muybridge's historical images, allowing it to clearly communicate motion and intelligence in a fresh yet familiar way." Throughout the project, the team had to consider the diversity of TwelveLabs' audience, from developers and researchers to large-scale enterprise clients and the broader creative community. The result is an identity system that feels accessible without being simplistic and capable of meeting users at every level of expertise. "We aimed for a universal tone – clear, direct, and welcoming for everyone, regardless of their expertise," Jody says. "Precision doesn't have to be complicated, and clarity invites everyone in. Finding a voice that felt calm, clear, and honest meant meeting each person exactly where they are." The outcome is an identity that doesn't just repackage complex AI technology. It embodies the very qualities that make TwelveLabs' approach revolutionary: interconnected, intelligent, and distinctly human.

Limited-time offer: Get more than 30% off a Vox Membership. Join today to support independent journalism. Federal civil servants are supposed to enjoy robust protections against being fired or demoted for political reasons. But President Donald Trump has effectively stripped them of these protections by neutralizing the federal agencies that implement these safeguards.An agency known as the Merit Systems Protection Board (MSPB) hears civil servants’ claims that a “government employer discriminated against them, retaliated against them for whistleblowing, violated protections for veterans, or otherwise subjected them to an unlawful adverse employment action or prohibited personnel practice,” as a federal appeals court explained in an opinion on Tuesday. But the three-member board currently lacks the quorum it needs to operate because Trump fired two of the members.Trump also fired Hampton Dellinger, who until recently served as the special counsel of the United States, a role that investigates alleged violations of federal civil service protections and brings related cases to the MSPB. Trump recently nominated Paul Ingrassia, a far-right podcaster and recent law school graduate to replace Dellinger.The upshot of these firings is that no one in the government is able to enforce laws and regulations protecting civil servants. As Dellinger noted in an interview, the morning before a federal appeals court determined that Trump could fire him, he’d “been able to get 6,000 newly hired federal employees back on the job,” and was working to get “all probationary employees put back on the job [after] their unlawful firing” by the Department of Government Efficiency and other Trump administration efforts to cull the federal workforce. These and other efforts to reinstate illegally fired federal workers are on hold, and may not resume until Trump leaves office.Which brings us to the US Court of Appeals for the Fourth Circuit’s decision in National Association of Immigration Judges v. Owen, which proposes an innovative solution to this problem.As the Owen opinion notes, the Supreme Court has held that the MSPB process is the only process a federal worker can use if they believe they’ve been fired in violation of federal civil service laws. So if that process is shut down, the worker is out of luck.But the Fourth Circuit’s Owen opinion argues that this “conclusion can only be true…when the statute functions as Congress intended.” That is, if the MSPB and the special counsel are unable to “fulfill their roles prescribed by” federal law, then the courts should pick up the slack and start hearing cases brought by illegally fired civil servants.For procedural reasons, the Fourth Circuit’s decision will not take effect right away — the court sent the case back down to a trial judge to “conduct a factual inquiry” into whether the MSPB continues to function. And, even after that inquiry is complete, the Trump administration is likely to appeal the Fourth Circuit’s decision to the Supreme Court if it wants to keep civil service protections on ice.If the justices agree with the circuit court, however, that will close a legal loophole that has left federal civil servants unprotected by laws that are still very much on the books. And it will cure a problem that the Supreme Court bears much of the blame for creating.The “unitary executive,” or why the Supreme Court is to blame for the loss of civil service protectionsFederal law provides that Dellinger could “be removed by the President only for inefficiency, neglect of duty, or malfeasance in office,” and members of the MSPB enjoy similar protections against being fired. Trump’s decision to fire these officials was illegal under these laws.But a federal appeals court nonetheless permitted Trump to fire Dellinger, and the Supreme Court recently backed Trump’s decision to fire the MSPB members as well. The reason is a legal theory known as the “unitary executive,” which is popular among Republican legal scholars, and especially among the six Republicans that control the Supreme Court.If you want to know all the details of this theory, I can point you to three different explainers I’ve written on the unitary executive. The short explanation is that the unitary executive theory claims that the president must have the power to fire top political appointees charged with executing federal laws – including officials who execute laws protecting civil servants from illegal firings.But the Supreme Court has never claimed that the unitary executive permits the president to fire any federal worker regardless of whether Congress has protected them or not. In a seminal opinion laying out the unitary executive theory, for example, Justice Antonin Scalia argued that the president must have the power to remove “principal officers” — high-ranking officials like Dellinger who must be nominated by the president and confirmed by the Senate. Under Scalia’s approach, lower-ranking government workers may still be given some protection.The Fourth Circuit cannot override the Supreme Court’s decision to embrace the unitary executive theory. But the Owen opinion essentially tries to police the line drawn by Scalia. The Supreme Court has given Trump the power to fire some high-ranking officials, but he shouldn’t be able to use that power as a back door to eliminate job protections for all civil servants.The Fourth Circuit suggests that the federal law which simultaneously gave the MSPB exclusive authority over civil service disputes, while also protecting MSPB members from being fired for political reasons, must be read as a package. Congress, this argument goes, would not have agreed to shunt all civil service disputes to the MSPB if it had known that the Supreme Court would strip the MSPB of its independence. And so, if the MSPB loses its independence, it must also lose its exclusive authority over civil service disputes — and federal courts must regain the power to hear those cases.It remains to be seen whether this argument persuades a Republican Supreme Court — all three of the Fourth Circuit judges who decided the Owen case are Democrats, and two are Biden appointees. But the Fourth Circuit’s reasoning closely resembles the kind of inquiry that courts frequently engage in when a federal law is struck down.When a court declares a provision of federal law unconstitutional, it often needs to ask whether other parts of the law should fall along with the unconstitutional provision, an inquiry known as “severability.” Often, this severability analysis asks which hypothetical law Congress would have enacted if it had known that the one provision is invalid.The Fourth Circuit’s decision in Owen is essentially a severability opinion. It takes as a given the Supreme Court’s conclusion that laws protecting Dellinger and the MSPB members from being fired are unconstitutional, then asks which law Congress would have enacted if it had known that it could not protect MSPB members from political reprisal. The Fourth Circuit’s conclusion is that, if Congress had known that MSPB members cannot be politically independent, then it would not have given them exclusive authority over civil service disputes.If the Supreme Court permits Trump to neutralize the MSPB, that would fundamentally change how the government functionsThe idea that civil servants should be hired based on merit and insulated from political pressure is hardly new. The first law protecting civil servants, the Pendleton Civil Service Reform Act, which President Chester A. Arthur signed into law in 1883.Laws like the Pendleton Act do more than protect civil servants who, say, resist pressure to deny government services to the president’s enemies. They also make it possible for top government officials to actually do their jobs.Before the Pendleton Act, federal jobs were typically awarded as patronage — so when a Democratic administration took office, the Republicans who occupied most federal jobs would be fired and replaced by Democrats. This was obviously quite disruptive, and it made it difficult for the government to hire highly specialized workers. Why would someone go to the trouble of earning an economics degree and becoming an expert on federal monetary policy, if they knew that their job in the Treasury Department would disappear the minute their party lost an election?Meanwhile, the task of filling all of these patronage jobs overwhelmed new presidents. As Candice Millard wrote in a 2011 biography of President James A. Garfield, the last president elected before the Pendleton Act, when Garfield took office, a line of job seekers began to form outside the White House “before he even sat down to breakfast.” By the time Garfield had eaten, this line “snaked down the front walk, out the gate, and onto Pennsylvania Avenue.” Garfield was assassinated by a disgruntled job seeker, a fact that likely helped build political support for the Pendleton Act.By neutralizing the MSPB, Trump is effectively undoing nearly 150 years worth of civil service reforms, and returning the federal government to a much more primitive state. At the very least, the Fourth Circuit’s decision in Owen is likely to force the Supreme Court to ask if it really wants a century and a half of work to unravel.See More: