Meta's Autodata represents a significant shift in AI training methodology, moving beyond static data preparation. Instead of relying on one-shot prompts or simple generation recipes, Autodata frames data creation as an agentic process. An AI agent iteratively generates examples, systematically tests them, and analyzes its failures. Through this mini research loop, the agent continuously updates its own data generation "recipe" until useful datasets are produced.
Ollama version 0.31 significantly speeds up Gemma 4 performance on Apple Silicon using MLX. This acceleration, which achieves nearly a 90% speed increase in coding benchmarks, is attributed to the improved and default multi-token prediction (MTP). MTP allows Ollama to automatically tune drafting tokens without hindering generation when speculation becomes ineffective. Currently, Gemma 4 is the first model benefiting from this update, with more support planned for other models. Users can access the faster version by pulling or running `gemma4:12b-mlx` with Ollama.
Discussions at AIEWF heavily focused on "loops," indicating a shift toward automation in AI engineering. Experts highlighted theories like Geoffrey Huntley's concept of using agents repeatedly against a single specification to create persistent workers. Software factories were introduced as encompassing the entire autonomous software development lifecycle, beyond just coding. Speakers emphasized connecting agents not only to tasks but also to user feedback and deployment stages for enhanced output. Ultimately, these concepts suggest that future software engineering will resemble building systems that automate product creation itself.
Ahmad Osman, founder of Osmantic, advocates for local AI—running models on personal hardware—because open-source LLMs are becoming credible alternatives to proprietary frontier models. His argument stresses the existential importance of being able to operate intelligence systems without external permission. Workshops demonstrated performance comparisons between various local setups and cloud models, showing substantial recent advancements in the field. Osman highlighted that a complete AI system involves more than just a model; it requires surrounding infrastructure. He expects developers and enterprises to increasingly adopt local AI as essential, robust infrastructure.
Devin Fusion, from Cognition, is a multi-model harness that reduces expenses by 35% on the FrontierCode benchmark by mixing frontier and cost-effective models. It utilizes a dual-agent architecture with a sidekick for dynamic model routing. Gemini's image generation feature is now free for US users and can create images based on inferred user preferences. Cursor for iOS, in public beta, allows developers to manage projects remotely via cloud or local agents. The industry is moving towards a modular AI ecosystem, described as the "economy of tokens," enabling open-weights models to reduce costs significantly.
Frequently, readers ask for guidance on going deeper into a topic. The author notes that for business leaders, the core challenge is not information overload but rather knowing which tools to use and how to integrate them effectively. To address this implementation gap, an "Executive Pass" was created. This pass targets individuals who are ready to move beyond mere curiosity toward practical application and clear decision-making paths. The content encourages readers to explore this focused resource if they seek fewer moving parts in their workflow.
Forward Deployed Engineering (FDE) currently lacks a consistent definition across the AI industry. Natalie Meurer suggests FDE is defined by accountability to customers, rather than a singular skill set or role structure. The function of engineering roles is converging, leading to hybrid specializations like "agent engineer" at Sierra. Agent engineers combine systems integration and agent development with deep knowledge of customer operations and the end-user experience. This specialization aims to capture the technical work shape while maintaining strong customer focus.
Framing advanced AI tools as "employees" or "coworkers," rather than software, leads managers to perform worse, according to a study by Professor Emma Wiles. Participants found themselves responsible for fewer errors when AI was labeled an agentic employee compared to a standard chatbot. This framing can cause human workers to shift responsibility and over-rely on escalating AI mistakes rather than trusting their own judgment. Furthermore, this trend risks creating a mechanism to deflect blame in critical fields like healthcare and government for underlying human failures. Experts argue AI should be optimized to enhance human capabilities rather than marketed as replacements for human workers.
Framing advanced AI tools as "employees" or coworkers leads humans to make fewer errors because of heightened perceived accountability within the roleplay. However, this practice risks setting unrealistic expectations for AI capability while potentially disadvantaging human workers. Research indicates that when treated as agents, people became less personally responsible for the output and were more likely to escalate questionable work rather than correcting it themselves. This dynamic creates a dangerous precedent where failures in critical sectors like healthcare or government could unfairly result in blaming technology instead of human error. Experts caution against positioning AI as replacements, suggesting instead that tools should be optimized to genuinely augment human capabilities.
Morgan Stanley deployed an agentic system called FIXR to automate P&L reconciliation, reducing the task time from six hours to two or three hours. The key innovation involved keeping humans tightly "in the loop," where agents propose resolutions based on learned rules that controllers must review and approve. This process allows the system to iteratively build repeatable, automated logic by codifying human decisions into firm rules. Furthermore, the implementation prioritized fixing existing processes before introducing AI, focusing on demonstrable improvements across global workflows. The methodology maintains human accountability while enabling significant automation where possible.
A United Nations report cautions that rapidly growing artificial intelligence could worsen global inequality due to uneven adoption. The analysis suggests that mere access to AI tools does not guarantee equal benefits worldwide. Countries dependent on foreign models, cloud infrastructure, and data pipelines risk losing practical control over AI standards and local relevance. The UN proposes a shared framework to guide the responsible development of AI technologies. This framework aims to mitigate risks associated with uneven technological investment globally.
Manny Rutinel's House campaign in Colorado's Democratic primary is receiving significant funding from Silicon Valley donors. At least $2 million has been contributed by committees associated with figures like Eric Schmidt and Chris Larsen. Rutinel, a progressive candidate, is running for the eighth congressional district against Shannon Bird. His platform emphasizes affordability and calls for regulations regarding ICE, while also highlighting his Latino heritage. These major tech donations are fueling competition between pro- and anti-AI regulatory factions in the race.
Artificial intelligence shares experienced a volatile week, though signs of a complete market crash are not apparent. Beyond the AI sector, California is proposing a new tax targeting billionaires. In other technology news, OpenAI adjusted its AI model release schedule following a request from the Trump administration. Additionally, Meta paused using employee trackers for AI training amid growing privacy concerns. Separate developments include discussions about potential social media age restrictions in the UK and fears regarding government website redesigns causing surveillance worries.
Ollama enables running AI agents directly from both the terminal and code editors. Users can launch available integrations via `ollama launch` to explore current functionalities. Available terminal tools include Claude Code, a comprehensive coding agent, and OpenCode, an open-source editor that iterates on code. For personal assistance, users can connect agents like OpenClaw and Hermes Agent for tasks in messaging apps. Additionally, Ollama models can be integrated into editors such as VS Code through the Copilot Chat model picker.
Cognition's Devin Fusion is a multi-model harness combining frontier and cost-effective AI models. This system reduces expenses by 35% on the FrontierCode benchmark while sustaining high performance levels. It employs a dual-agent architecture, utilizing a main agent and a sidekick for dynamic model routing. This optimization helps improve task handling efficiency and prevents costly cache misses. Further efficiency gains are achieved via Fable 5 integration, which promises advancements as models evolve.
TLDR AI is a free daily newsletter offering an AI news digest in under five minutes. It summarizes recent model releases, research papers, and new tools with links to their sources. The content is highly dense and geared towards engineers and researchers who need technical information. This focus distinguishes it from more general or "how-to" oriented newsletters. With 1.1 million subscribers, it remains one of the world's largest AI industry summaries.
Novacomp utilized IBM Bob, an AI-first development environment, to modernize a business-critical Java REST API built on legacy technologies. This project required re-platforming a layered logical monolith into a cloud-native, microservices architecture while maintaining strict functional equivalence. Normally requiring weeks of effort from a multi-person team due to dependency issues and security concerns, the upgrade was completed in just two days by a single engineer using IBM Bob. The AI assistant provided detailed codebase analysis, suggested structural improvements, and helped map out future upgrade roadmaps. Ultimately, this approach proved significantly faster than traditional modernization methods for preserving critical business logic.
Anthropic unveiled Claude Science at an event attended by pharmaceutical executives, biotech founders, and researchers. This new flagship product is designed to assist with scientific research. Similar to Claude Code for software engineering, Claude Science can execute meaningful work autonomously from high-level instructions. It provides advanced capabilities specifically tailored for the complexities of scientific investigation.
Gemini Spark has launched on macOS, integrating advanced AI capabilities into the operating system. Users can now leverage its functionality to execute complex tasks with natural language prompts. For instance, a user could prompt it to monitor specific career opportunities like "interior design internships for this summer." Furthermore, Gemini Spark enhances connectivity by integrating with various applications across macOS. These updates aim to streamline workflows and provide users with powerful, context-aware assistance directly within their system environment.
Presents the eleventh annual Environmental Report. The accompanying visuals include a varied collage featuring landscapes, search results related to sustainability, wind turbines, and Google Maps imagery. This report details environmental efforts across various sectors or initiatives. It serves as an update on corporate ecological performance and goals for the coming year. Readers are invited to review the comprehensive findings within the document.
A video titled "Start building with Nano Banana 2 Lite and Gemini Omni Flash" is presented. The content features a title card that explicitly reads, "Build with our generative media models." No further descriptive information or technical details are provided beyond this initial visual element. The presentation serves as an introduction to utilizing these named generative AI tools for creation. Essentially, it acts as a promotional teaser showcasing the capability of generating media through specific model suites.
Infrastructural decisions for production AI have shifted focus from peak chip performance to cost metrics like tokens per dollar. Organizations now prioritize the efficiency of delivering usable tokens within budget and latency constraints. NVIDIA addresses this by offering an inference software stack codesigned with various hardware components. This integrated approach spans GPUs, CPUs, networking, and entire systems. Furthermore, the stack is supported by a broad open-source ecosystem to ensure optimized, cost-effective deployment.
Describes three distinct workflows designed to enhance the accuracy of vision AI agents. These methods leverage synthetic data generation alongside fine-tuning techniques for improved performance. Vision AI agents are increasingly valuable tools for converting real-world video data into actionable industrial intelligence within factory settings. The content is part of a broader series detailing developer and enterprise applications using OpenUSD and NVIDIA Omniverse.
Designs for GPU-accelerated query engines frequently face limitations related to available memory and input/output bandwidth. Advances in NVIDIA hardware, such as High Bandwidth Memory (HBM), are crucial for overcoming these bottlenecks. These technological improvements allow for faster processing speeds in data querying applications. By leveraging specialized GPU architectures, developers can build more efficient and powerful query engines. Ultimately, integrating HBM and other innovations significantly boosts performance for large-scale data analytics workloads.
Omniverse NuRec serves as a specialized neural reconstruction pipeline designed to generate detailed 3D models of physical environments. It processes data gathered from various sensors to achieve high levels of fidelity in the resulting reconstructions. Optimizing this computational process involves utilizing NVIDIA Nsight Developer Tools. These tools enable developers to analyze and improve the performance bottlenecks within the complex reconstruction workflow. Through optimization, efficiency in building realistic 3D digital twins can be significantly enhanced.
SkillOpt addresses the unreliability of AI agents whose skills are subject to manual, non-guaranteed modifications. It revolutionizes this by transforming skill editing into a systematic training process. This method allows for improving agent behavior reliably without necessitating changes to the underlying model weights. Originally presented at Microsoft Research, SkillOpt treats agent skills as trainable parameters.
Anthropic's Claude is targeting another application within the scientific domain. The company is involved in developing artificial intelligence tools for research. This focus suggests a continued commitment to integrating AI into scientific practices. However, Anthropic is reportedly proceeding carefully in this area. This cautious approach reflects acknowledged challenges associated with deploying AI within the science field.
Anthropic has made its Claude models accessible within the Microsoft Foundry platform. This integration provides businesses with expanded capabilities for developing sophisticated AI solutions. Enterprises can now leverage these models for constructing specialized, autonomous AI agents. Such availability broadens industry access to advanced generative AI technology. The inclusion facilitates the creation of domain-specific automated systems.
NVIDIA released Nemotron-Labs-TwoTower, an open-weight diffusion language model utilizing a frozen autoregressive Nemotron-3-Nano-30B-A3B backbone. The model's release aims to address the throughput limitations inherent in traditional token-by-token auto-regressive text generation. Discrete diffusion models overcome this serial decoding process, thereby boosting overall generation speed. This new framework represents an advancement for language model deployment by offering superior throughput capabilities. The model is made available under the NVIDIA Nemotron Open Model License.
Google Research introduced TabFM, a novel foundation model specifically designed for tabular data. This model enables zero-shot classification and regression tasks using in-context learning. Notably, predictions are generated from a single forward pass without needing per-dataset training or hyperparameter tuning. Furthermore, the process bypasses the need for explicit feature engineering.
CUP (Common Useful Python) is a utility toolkit from Baidu designed to strengthen Python workflows. The tutorial demonstrates its installation in a Colab-friendly setting while detailing several key subsystems. Covered functionalities include logging, decorators, handling nested configurations, caching mechanisms, and ID generation. Furthermore, it addresses complex topics like thread pools, scheduling, and Linux resource monitoring. Each module is shown with practical applications such as automation, concurrency implementation, and reliability testing.
Claude Sonnet 5 shows significantly improved performance in agentic coding capabilities compared to previous versions and its lower-tier sibling. These advancements allow it to approach the high performance of Opus 4.8 while maintaining more cost-effective API token pricing. The comparison evaluates these models across benchmarking, current API costs, and overall efficiency. Users can now balance powerful AI features with budget constraints using Sonnet 5.
Pushes the boundaries of synthetic data generation. Explores a new approach focused on "autodata." This method allows AI models to learn by creating their own instructional material. Essentially, the model generates its own lessons or feedback mechanisms during training. This self-directed learning promises more sophisticated and resource-efficient AI development.
Discussions at the AI Engineer World's Fair focused heavily on several key technological trends. Prominent topics included the use of complex loops in AI systems and advanced agent engineering techniques. Furthermore, there was significant buzz surrounding the concept of automated "software factories." Another highly discussed element throughout the event was the continued development and adoption of open-source models.
Details about "Sonnet 5" are available today. Tomorrow will feature content titled "Fable 5." The overarching message implies that all sections or features are currently accessible. Nothing further is specified regarding the nature of the opening, beyond general availability. Essentially, new content for both a sonnet and a fable format is scheduled for consecutive days.
Ahmad Osman presented his view after attending multiple AIEWF workshops. He argues that localized artificial intelligence development is rapidly advancing. This progress is visible across various computing platforms. Local AI deployment is expanding beyond personal devices like laptops and smartphones. Furthermore, it is reaching robust, enterprise-level infrastructure.
The Department of Commerce has lifted export controls regarding Claude Fable 5 and Mythos 5. Consequently, Anthropic will begin restoring access to these models starting tomorrow. Further updates regarding the restoration process will be shared with users soon. This notice pertains to the availability of Anthropic's AI models.
Nano Banana 2 Lite, also called Gemini 3.1 Flash Lite Image, is an image model designed to be both fast and cost-effective for scaling needs. When prompted in AI Studio to create a "Where's Waldo" style picture featuring a raccoon with a ham radio, the results were favorably compared to previous attempts using other Nano Banana models. However, the author noted that even this version incorrectly spelled "Forest Festival" on two separate occasions during testing. The model is associated with Google and falls under generative AI/text-to-image technologies within the Gemini suite.
Version 1.10 of shot-scraper has been released. The primary new feature is the introduction of `shot-scraper video storyboard.yml`. This feature allows users to have an agent record video demonstrations of shot-scraper's functionality. Detailed instructions for using this video capability are available in the documentation section labeled "Have your agent record video demos of its work with shot-scraper video."
Learn how to build and deploy your own AI agent using AWS services like Strands and AgentCore. The resource provides instructions for creating functional agents within a cloud environment. It details the process of deploying these self-built agents on Amazon Web Services. Content was initially published on the Towards Data Science platform.
Context Engineering for Retrieval Augmented Generation (RAG) is a practice notable from 2025, as named by Tobi Lütke and Andrej Karpathy. For processing a single document, the system emits typed pieces that combine before reaching one LLM call. The architecture involves four distinct types of inputs to generate comprehensive RAG answers. Beyond this core functionality, follow-up development is planned for corpus, conversation, and tool extensions. The discussion was initially published on Towards Data Science.
Enhance your coding agent capabilities by constructing a model ensemble. The original post detailing this method was published on Towards Data Science. Combining multiple models can lead to improved overall performance for code execution commands. Specific instructions are provided for maximizing the effectiveness of the Codex Exec command within such an ensemble setup. Following these steps helps build a significantly more powerful AI coding agent.
Details a practical guide for implementing hybrid workflows utilizing both local and cloud Large Language Models (LLMs). The walkthrough specifically demonstrates using Gemma 4 alongside GPT-5.4. This methodology allows users to leverage the strengths of both deployment environments in one process. A key focus is on achieving structured outputs and consistent reasoning across this mixed setup. Provides actionable patterns for when developers shouldn't have to choose exclusively between local or cloud LLMs.
ScarfBench establishes a standardized benchmark specifically designed to evaluate AI agents tasked with migrating enterprise Java frameworks. It assesses an agent's ability to handle complex, real-world migration scenarios inherent in maintaining legacy systems. The benchmark tests crucial skills like dependency resolution, modernizing outdated APIs, and ensuring functional parity across different versions. By providing quantifiable metrics, ScarfBench allows developers and enterprises to compare the effectiveness of various AI tools for critical code modernization projects. Ultimately, it aims to reduce risk and accelerate the adoption of AI in enterprise-level Java maintenance workflows.
Phillip Isola provides an explanation of how AI agents function amidst current industry speculation. He clarifies the operational mechanics behind these sophisticated systems. The discussion addresses the rapid pace of advancement within the field. Furthermore, Isola examines potential future trajectories for agentic AI technology. This serves to ground understanding beyond mere hype.
The author discusses encountering high variance in returns when using Soft Actor-Critic (SAC) with curriculum learning. Training progress plateaus, but this plateau often does not represent the actual optimal solution. Investigation of the variance shows multiple instances where episode returns achieve optimal results. The core questions raised are how to converge toward these observed optimal solutions or whether such variability is an inherent aspect of Reinforcement Learning (RL).
A Reinforcement Learning agent using Maskable PPO is developed for a constrained Gomoku variant on a 20x20 board, where moves must remain within a growing cluster. The system uses a custom CNN feature extractor incorporating inputs like my pieces, opponent pieces, and a threat map. While the agent shows strong performance against depth-3 minimax in evaluation (12 wins/8 losses over 20 games), its main deficiency is defensive play regarding dangerous opponent positions. The author seeks advice on improving this weakness, considering options such as reward shaping, curriculum enhancement, or network architecture changes.
A developer plans to use Reinforcement Learning, such as Unity ML-Agents, for enemy AI rather than traditional behavior trees. The goal is to make the bots exhibit smart, human-like decision-making within a complex game environment. A key requirement is achieving controllable difficulty levels for these AI agents. The original post seeks suggestions on methods to effectively modulate the difficulty of RL-trained opponents.
Tracking hidden state trajectories reveals a "dynamic grammar" common across various large language models, describing generation as passing through sequential internal states. This universal sequence is found to be Branching (exploration) $\rightarrow$ Adaptive/Stable processing $\rightarrow$ Decision commitment. The research posits that functional information resides in the relative geometry of the representation space rather than specific neurons. Ambiguous prompts cause delays in this process by increasing exploration time rather than causing outright model chaos. Observing these dynamic signatures suggests architectural structure influences behavior more significantly than parameter count alone.
Tesla's early life experiences are described through nine "lives." His first life began during a storm; his second involved surviving deep underwater currents. He navigated rising water at a dam and experienced an overnight entombment in stone. Later challenges included recovering from cholera fever and learning to live with constant, inescapable noise. The article concludes by mentioning his near-fatal accident involving a taxi and the final renewal brought by a dove's appearance.
Artificial intelligence offers significant potential to revolutionize agriculture across several key areas. Promising applications exist particularly for industries facing challenges like fluctuating fertilizer costs and erratic weather patterns. Predictive AI models have demonstrated the capability to improve crop yields. However, experts warn that investment in AI technology should be preceded by substantial foundational groundwork within the sector. Currently, the available data necessary to fully support widespread AI implementation remains a critical gap.
NVIDIA has developed a comprehensive GPU-accelerated computing stack over the past decade to support sophisticated workflows in life sciences research. This stack encompasses hardware, frameworks, libraries, models, and domain-specific tools. Recently, Anthropic announced Claude Science, an AI workbench designed specifically for scientific applications. The BioNeMo Agent Toolkit is central to delivering accelerated AI capabilities within this burgeoning field. These combined resources aim to enable researchers to iterate faster on complex biological problems.
Couchbase announced its AI Data Plane, an operational platform integrating persistent agent memory, real-time context retrieval, and an enterprise MCP server. This architecture runs consistently across cloud, on-premises, and disconnected edge environments using Couchbase Lite. The plane includes guardrails for agent memory like token constraints and a distinct Agent Catalog for function discovery. Its core advantage lies in its caching-based, ACID-compliant foundation, which enables fast local context processing even without network connectivity. Industry experts note the platform's strength is its broad reach from cloud to edge, meeting diverse enterprise operational needs.
A May poll by health research firm KFF found a link between frequent AI chatbot use for health advice and belief in anti-vaccine myths among US adults. Specifically, those who regularly consult chatbots are more likely to accept falsehoods regarding vaccines. These debunked myths include claims that shots cause autism or that the measles vaccine is riskier than the virus itself. This correlation persisted even when controlling for demographics like age, race, education, and political affiliation.
Automaker Ford discovered significant flaws in the AI cameras employed for its design and manufacturing inspection processes. These faulty systems have been colloquially labeled "Greybeards." The poor performance of this artificial intelligence led to operational setbacks for the company. As a result, Ford was forced to reconsider its reliance on fully automated systems. Consequently, the manufacturer had to rehire human workers to compensate for the shortcomings of the AI technology.
Navigating the intersection of skepticism, workplace dysfunction, and advancing artificial intelligence presents unique challenges. The guide offers advice for individuals who do not subscribe to belief in AI's sentience while simultaneously dealing with an emotionally volatile boss. Specific strategies involve maintaining professional boundaries to manage the unpredictable work environment. Furthermore, it recommends focusing on skill diversification that remains valuable regardless of technological upheaval. Ultimately, self-preservation through emotional and career preparedness is key for non-believers facing this combination of stressors.
A political compass quiz called "The AI Compass" assesses user alignment with various views on Artificial Intelligence and AI ethics. The questionnaire consists of 29 questions designed to categorize the respondent into one of thirty established archetypes. One user successfully took the quiz and was identified as "The Garage Tinkerer." Technically, the application is implemented as a single-page React app using a specific JavaScript tag trick to bypass build requirements. The quiz is categorized under tags including AI, generative-AI, LLMs, and AI ethics.
To succeed in today's job market, particularly within data science, simply having skills is no longer enough. Confidence and preparation are key components of a successful behavioral interview. The piece offers specific advice to help candidates navigate these types of interviews effectively. Reviewing these tips can significantly boost an interviewee's readiness.
Emily Bender clarified common misunderstandings regarding the "Stochastic Parrots" paper from 2021. She emphasized that the term specifically refers only to large language models generating synthetic text, not all artificial intelligence systems. Furthermore, she stated the concept was intended as a description of function, not meant to be an insult or minimization. Bender noted that using "Artificial Intelligence" broadly obscures discussions about specific technologies like machine translation or protein folding. Her goal with these metaphors is to illustrate precisely how LLMs operate by pattern repetition rather than genuine understanding.
Jaiveer Singh focuses on the foundational infrastructure necessary for practical robotics advancement. His work involves improving the internal components of machines, such as the circuit boards themselves. He also develops critical software that enables developers to interpret data from robotic cameras. Furthermore, he addresses the complex engineering required for robots to move beyond demonstration environments. Ultimately, his efforts aim to accelerate the real-world applicability and deployment speed of robotics technology.
Developing RoboSpace, a browser-based robotics simulator over eleven months, revealed that user feedback was more impactful than user growth metrics. The author notes that some of the best features were derived directly from input gathered from researchers and developers. A key lesson learned is the importance of actively listening to users, followed by shipping updates and repeating the cycle. Consequently, the author is now engaging universities and robotics labs in conversations about current simulation workflows and pain points. They encourage others in the field to share which simulators they use or what tools need improvement.
The author is questioning the feasibility of using motion priors within a Time-Consistent Controller (TQC) framework. A major technical challenge noted is the significant difference in data collection time between on-policy Proximal Policy Optimization (PPO) and TQC, specifically citing PPO's 22-second collection time versus TQC's approximately one second for comparable steps. The author is currently struggling with training a hybrid robot to climb stairs above a 3cm height. After two weeks of adjustment using weight tuning and curriculum design, the robots are still failing to perform the stair-climbing task.
Research investigates when natural language feedback leads to genuine improvement in multi-turn language agents beyond mere repeated attempts. To isolate true feedback utility, the study implemented a controlled student-teacher protocol across multiple benchmark tasks involving thirteen open-weight models. Findings indicate that self-generated feedback offers minimal benefit over unguided self-refinement. Substantially larger gains were observed only with strong external teachers, suggesting useful feedback must provide specific guidance. Ultimately, interactive performance relies more on the student's capacity to utilize feedback than simply the availability of the feedback itself.
Contrastive Reflection is presented as an iterative framework for optimizing prompts in agentic Information Retrieval (IR) workflows. It starts from a task-centric quality definition using structured traces from QA and grading agents to pinpoint error-related behavioral segments. The system then adds nearby successful examples and uses a Teacher LLM to propose specific, targeted prompt edits. These candidate edits are only adopted if they demonstrably improve validation performance while passing regression checks. On HotpotQA, this contrastive repair significantly increased held-out exact-match accuracy from 51.4% to 60.4%.
Discovering reusable simulation models is challenging due to the existence of many existing models. Recent advancements in Artificial Intelligence offer promise by enabling model retrieval at a semantic level using natural language queries. This study experimentally investigates how data representation, transformer embeddings, and retrieval strategies affect this discovery process. Performance metrics like recall@5 and nDCG@5 were used across various query types for evaluation. Results indicate that both the initial data format and incorporating reranking methods are critical for successful model discovery, particularly with complex queries.
New benchmark, BayesBench, evaluates how well Large Language Models (LLMs) update their beliefs in multi-turn conversations as new evidence accumulates. The system tests LLM performance across three increasingly complex tasks: Bayesian estimation, Bayesian prediction, and latent-framed Bayesian prediction. Comparing results across seven different LLMs, the evaluation found that larger models improve at inferring underlying latent structures from sequential evidence. However, these gains do not consistently transfer to downstream predictive tasks. This exposes a distinct gap between an LLM's ability to perform inference versus rationally updating predictions based on those inferences.
Research introduced LearnStop, a checkpoint stopper for reasoning models assessing prefix correctness using multiple online features like answer confidence and entropy. Across various tasks and model settings (including GSM8K and MATH-500), performance improvement with learned multi-feature stopping is task-dependent. Such advanced stopping rules notably improve free-form math performance but are often matched or surpassed by simpler scalar thresholds, such as confidence or stability rules. The benefit of learned stopping is highest when a question requires significant reasoning without one obvious reliable scalar signal for termination. Overall, its value relies on the specific trajectory structure rather than serving as a universal replacement for existing stopping criteria.
Current AI agents often assume users possess complete preferences, prompting clarification questions when tasks are underspecified. This assumption is flawed because many users lack necessary domain knowledge to fully specify their needs. The paper introduces CoPref, a model using the Search-Experience-Credence framework to describe how agents help users construct preferences through dialogue. Researchers designed CoShop, an interactive benchmark for agentic recommender systems where conversation guides preference construction. Evaluation on five frontier models showed limited accuracy (under 56% after five turns), indicating poor performance due to insufficient knowledge expansion during interaction.
Artificial intelligence is increasingly applied to law, with agentic AI using LLMs taking autonomous actions. Investigating this area, the paper explores multi-agent deliberation (MAD) methods for legal reasoning tasks using LLMs. Two novel frameworks, inspired by courtroom procedures and legal argumentation, are introduced within the study. Experiments show these multi-agent frameworks match baseline LLM performance but yield distinct answers. Specifically, these approaches can solve cases missed by baselines, proving superior for questions requiring multiple perspectives and critical thinking.
ML engineering agents inefficiently rediscover known techniques across separate competitions due to cold starts. HASTE introduces a hierarchical multi-agent system organizing knowledge into global, domain, and competition-specific tiers. An orchestrator manages this structure and promotes learning between the skill tiers using LLM abstraction. Evidence shows that tiered loading significantly outperforms flat loading on skill inventory retention across multiple tasks. On a benchmark of 22 Kaggle competitions, HASTE achieved a 77.3% medal rate by utilizing warm starts that selectively reloaded learned skills.
The existing LLM Jury method of aggregating multiple evaluator scores suffers from unbounded bias when single judges exhibit typical failures like mode collapse or sycophancy. To address this, the authors propose RoPoLL, which replaces the simple aggregation function with a robust mean estimator, specifically using the geometric median (GM). This approach maintains the panel structure while improving robustness by achieving an optimal finite-sample breakdown point of 1/2. Testing across various judges and corruption regimes demonstrated that RoPoLL significantly outperforms standard PoLL across all biased corruption types. For instance, a small committee using RoPoLL achieved superior performance compared to much larger models under significant contamination rates.
AgRefactor is an LLM-based, multi-agent workflow designed to automate the challenging process of refactoring general software into code compatible with High-Level Synthesis (HLS). It features a self-evolving memory system that accumulates knowledge across tasks to boost robustness and efficiency on new programs. To improve scalability and reduce costs, it integrates automated refactoring tools alongside LLM rewriting capabilities. On eleven rigorous real-world benchmarks, AgRefactor matched or exceeded the performance of existing state-of-the-art methods. Furthermore, its agentic optimizations achieved significant speedups compared to leading pruning and tuning techniques.
Discovering governing partial differential equations (PDEs) directly from observational data is crucial for interpretable scientific machine learning. The presented competitive optimization framework, MCO-PDE, addresses limitations of single-dataset approaches by integrating multiple sources. It trains independent neural surrogates for each dataset and uses a soft-competitive weighting mechanism to determine a consensus global coefficient. By combining this with a genetic algorithm, the method simultaneously identifies both the functional forms and parameters of governing physical laws. Testing showed that fusing data from just 50 observations per dataset successfully recovered canonical equations in real-world wave-tank experiments.
Presents a new benchmark, NHANES Accelerometry Cardiometabolic Benchmark, using 1,381 adults with combined accelerometry, lab biomarkers, diet, and anthropometrics from NHANES 2003-2006. Evaluated three machine learning models (ridge regression, XGBoost, TabPFN v2) to predict HbA1c, triglycerides, and CRP using activity data. TabPFN v2 outperformed other methods for predicting HbA1c ($\text{R}^2=0.156$) and CRP ($\text{R}^2=0.383$), noting limited predictability for triglycerides. The study utilized split conformal prediction to generate 90% prediction intervals, finding marginal coverage met for some markers but failing at the subgroup level for clinical fairness. Observations of localized undercoverage demonstrate that achieving conditional coverage is challenging compared to aggregate performance guarantees.
Large language models often lack the structural consistency needed for reliable deployment across varied instances. Workflows offer a principled solution by encoding reusable, task-level algorithmic patterns. MetaFlow addresses the difficulty of manual workflow design by framing generation as a meta-learning problem. It trains through supervised fine-tuning on synthetic data followed by Reinforcement Learning with verifiable rewards using execution feedback. This results in a model capable of generating effective workflows that generalize strongly to both untrained tasks and novel operator sets across various domains.
Deterministic few-step generation works for continuous image latents but fails for text latents because a smooth map cannot resolve discrete branch choices before a sharp categorical readout. The failure depends on decoder sharpness rather than latent transport accuracy. Diagnostics like DABI show that continuous text decoders amplify boundary perturbations significantly, unlike image decoders. Two mechanisms bypass this limitation: categorical commitment in autoregressive models and stochastic re-injection during ODE generation. Ultimately, there is an unavoidable trade-off where achieving high determinism requires increasing stiffness, making the accurate separation of multiple latent modes costly.
Hierarchical Global Attention (HGA) offers a drop-in replacement for dense causal attention in transformers without needing retraining or adding calibration parameters. It achieves low memory usage by performing hierarchical, two-level routing instead of calculating full attention over long contexts. First, it retrieves relevant chunks using compact RoPE-aware summaries, and then refines this selection before exact token attention is applied only to the most relevant groups. This approach allows storing historical key/value pairs in host RAM or NVMe while minimizing GPU memory usage during attention computation. Tests show that HGA maintains high accuracy (within $0.01$--$0.02$ nats) compared to dense attention across context lengths up to 64K tokens using minimal sparsity.
ReactionAtlas is a new method that constructs entire chemical reaction networks *ab origine* without requiring predefined rules. It uses a machine-learned generative model to propose reactions from initial "seed" molecules, which are then filtered by a DFT-trained Machine Learned Force Field (MLFF) to identify valid transition states. Starting with eight pre-biotic seeds, the system discovered approximately 47,000 reactions involving around 12,000 compounds. The MLFF successfully matches reference PBE0 calculations in 85% of cases with high accuracy ($\le 0.5$ Å RMSD). This tool enables mapping large-scale carbohydrate chemistry, revealing novel pathways for processes like the formose cycle relevant to abiogenesis.
Introduces "process sidecars," a two-coefficient edit family $\hat{\theta}(\lambda,\gamma)=\theta_{\mathrm{AMS}}-\lambda\Delta_{\mathrm{M}}-\gamma\hat{R}_{\mathrm{S}\leftarrow\mathrm{M}}$, to model the complex task of revoking learned memories after subsequent safety fine-tuning. This method addresses that simply subtracting memory updates fails because later safety optimization shifts the memory's direction. The authors prove this sidecar accurately recovers counterfactual safety parameters up to second order by modeling AdamW as an augmented state map. Experimental validation shows this process-sidecar edit improves refusal closure compared to naive task arithmetic across three models.
Group Relative Policy Optimization (GRPO) training dynamics are currently described empirically using ad-hoc fits. Researchers developed a first-principles, closed-form reduced-order model of these dynamics. This model recasts empirical parameters into measurable physical quantities, including defining the plateau and timescale through an underlying potential's fixed point and stiffness. Furthermore, it predicts system behaviors like group-size invariance with a $1/G$ fluctuation and a distinct stability threshold for refresh intervals. The model successfully fit training rewards ($\text{R}^2 \geq 0.91$) and accurately predicted expected transitions in controlled settings involving softmax-bandit reductions.
Introduces Depth-wise Gradient Augmentation, an optimization paradigm that transforms layer-wise updates by considering block-wise changes along the depth dimension. A specific technique within this is Gradient Smoothing, which operates on existing block-wise optimizer updates using local smoothing operators like Window Smoothing. This method requires minimal overhead and integrates easily with current training pipelines. Evaluations across various tasks, including language model pretraining, RL fine-tuning, diffusion modeling, and ViT classification, showed consistent improvements in optimization and generalization. The results confirm Depth-wise Gradient Augmentation's potential for leveraging cross-depth structure during deep learning optimization.
Modeling high-resolution fields from coarse inputs via probabilistic downscaling is crucial for atmospheric and climate science. The common mean--residual approach often produces biased and under-dispersive results in real-world applications due to residual target misspecification. ReMatch addresses this by aligning the training residual distribution toward the test-time regime using optimal transport within a PCA space. This method preserves the mean--residual structure while mitigating the train--test mismatch affecting the stochastic generator. Evaluation on synthetic and real wind field tasks demonstrates that ReMatch significantly reduces under-dispersion and improves calibration compared to existing methods.
Enhances theoretical understanding of Convolutional Neural Networks (CNNs) as feature extractors by applying Cover's function-counting theory. The study focuses on "separation capacity," a combinatorial measure derived from counting realizable binary label assignments. Authors first extend Cover's framework with a practically useful formulation for this separation capacity. Utilizing this, they identify factors governing the separation capacity of CNN architectures called scattering networks based on their building blocks. Finally, the paper offers concrete guidelines for designing effective scattering networks.
Alternating recurrent events, which involve trigger points followed by refractory periods, are significant across fields like behavioral science and biostatistics. Analyzing these requires addressing statistical complexities such as correlated observations and censoring. The authors developed an online dynamic prediction framework utilizing neural network theory for statistical application. This approach employs inverse probability weighted pseudo-observations to predict subsequent event-free time. Evaluation showed the model performed well in simulations and excelled at predicting periods of low mood among medical residents.
Deep learning often operates using learning rates beyond those suggested by traditional optimization theory, pushing models near instability. Previous research mostly analyzed this "edge of stability" using deterministic gradient descent, neglecting stochastic settings. This work provides rigorous convergence guarantees for Stochastic Gradient Descent (SGD) on the multiclass cross-entropy loss for both linear classifiers and two-layer neural networks. The authors demonstrate that SGD's inherent randomness causes dynamics to shift between curvature-driven oscillations and a controlled expected loss decrease. Crucially, they prove that SGD self-stabilizes the process, ensuring convergence in the best-iterate sense even when large learning rates are used.
Gaussian process inference faces cubic computational limitations, particularly when performing posterior inference on dense spatio-temporal grids. State-space SPDE formulations offer linear time complexity but still exhibit cubic spatial costs, worsening significantly if observation points differ from prediction locations. To overcome these bottlenecks, a new method called the Vanilla-SPDE Exchange is proposed. This exchange leverages an equivalence between standard and SPDE GP inference formulations. The resulting hybrid scheme demonstrates improved computational efficiency through both theoretical analysis and numerical testing.
Classical $k$-means struggles with missing data, and mean-imputation methods introduce bias when data is Missing Not At Random (MNAR). This paper introduces MNAR-$k$-means, a novel clustering technique designed for magnitude-decaying MNAR scenarios where smaller absolute values are more likely to be missing. The proposed method incorporates constraints on the imputation values, which offers strong mathematical interpretability. Authors prove that this approach achieves statistical consistency in estimating true cluster centers compared to fully observed data. Empirical tests confirm that MNAR-$k$-means improves clustering performance and reduces estimation bias across real-world datasets.
Computational cost frequently limits the use of conformal prediction for uncertainty quantification. Existing fast methods, like Jackknife+ and Jackknife-minmax, reduce computation but still require full leave-one-out refitting for all data points. This paper introduces approximate leave-one-out (ALO) estimators to further accelerate this process while establishing asymptotic coverage and efficiency. The mathematical proofs adapt techniques from high-dimensional cross-validation risk estimation for conformal prediction's specific needs regarding residuals. Simulation results confirm that ALO-based methods maintain performance comparable to exact methods but achieve significant reductions in runtime.
MediEncoder introduces a representation-learning framework for nonlinear, high-dimensional causal mediation analysis. It addresses the limitations of existing methods that often rely on linearity or struggle with structural dependence among variables. The framework utilizes a coupled encoder-decoder architecture and a cross-factor network to jointly learn low-dimensional representations for covariates and mediators. These learned features then power a cross-fitted influence function estimator designed to estimate natural direct and indirect effects. Evaluations demonstrate that MediEncoder improves estimation accuracy compared to competing dimension-reduction techniques, particularly in complex biomedical datasets like Alzheimer's research.
Small area estimation utilizes diverse methods to improve precision by pooling information across regions. Two main approaches are structured Gaussian smoothing and using heavy-tailed global-local priors, exemplified by the horseshoe prior. Researchers analyze the horseshoe Fay-Herriot model, demonstrating that its posterior mean resists shrinkage for highly significant areas unlike Gaussian models. Furthermore, standardizing design variances allows the contraction rate to match minimax theory even in heteroscedastic settings. The findings suggest using the horseshoe as a default prior because it aggressively borrows strength while preserving unique signals from exceptional areas without requiring graph tuning.
Developed the NHANES Accelerometry Cardiometabolic Benchmark using 1,381 adults with hip-worn accelerometry alongside various biomarkers and lifestyle data from NHANES 2003-2006. The study assessed ridge regression, XGBoost, and TabPFN v2 for predicting HbA1c, triglycerides, and CRP from activity phenotypes and covariates. TabPFN v2 showed the best performance across all measures, although triglyceride prediction remained highly challenging ($R^2 < 0.05$). Using split conformal prediction generated distribution-free 90% intervals, showing marginal coverage for CRP and HbA1c but not triglycerides. Furthermore, subgroup analysis revealed localized undercoverage, indicating potential gaps in clinical fairness relative to overall coverage guarantees.