Anthropic has introduced Claude Tag, an advanced AI agent designed for Slack that represents a major evolution in LLM user experience across its various interfaces. Key features include the ability to tag colleagues who own related code and utilizing git webhooks to wait for lengthy blocking dependencies over days. The agent can summarize entire conversation threads into documents with actionable item lists. Furthermore, Tag possesses ambient behavior modes, allowing it to respond to channels without direct tagging and proactively follow up across different channels. These capabilities establish Tag as a significant advancement signaling the future of automated work processes.
Answering questions at a BIO AI Summit revealed widespread misunderstanding regarding AI agents and tools. Specifically, there is confusion about whether equipping an agent with scientific toolkits provides dangerous new knowledge. The NVIDIA BioNeMo Agent Toolkit functions like laboratory equipment or a repair kit, providing specialized, callable skills for life-sciences tasks. It allows agents to invoke specific tools such as protein folding or molecular docking, rather than fundamentally changing the AI's inherent knowledge base.
The updated Terms of Service will take effect on July 30, 2026, and clarify user expectations regarding the services. Changes include a new section addressing background internet usage and updates to dispute settlement procedures. For EEA residents, changes incorporate current local laws, including a withdrawal button. Users outside the EEA may see added disclosures, such as those related to AI outputs. If users disagree with the terms, they must remove their content and discontinue service use.
Following Anthropic's creation of its coding-capable AI model, Mythos, and subsequent public release of a modified version named Fable, the federal government placed export controls on it. Hours after these actions, Anthropic revoked access to both AI models. The incident has spurred calls in Europe for increased domestic AI development but is complicated by China's highly capable and accessible open-source models. These developments suggest companies might pivot toward Chinese alternatives to avoid US or European restrictions.
A solar-powered platform, designed to improve aerial internet connectivity, is slated for deployment starting in August. The roughly 200-foot-long craft was developed by New Mexico's company, Sceye. It will travel across the southwestern US and cross the Pacific Ocean to Japan's coast. Upon arrival near the ocean, it plans to operate at an altitude of about 18 kilometers above sea level. This airborne system aims to provide enhanced internet service from a high vantage point over the water.
OpenAI is actively contributing to establishing common standards for advanced artificial intelligence systems. This effort involves supporting the development of rigorous evaluation frameworks. Furthermore, they are promoting best practices in AI safety across the industry. OpenAI's involvement emphasizes the need for global collaboration in this rapidly advancing field. These initiatives are being facilitated through their support for the Appia Foundation.
YouTube is releasing new analytics tools specifically for creator partnerships. These resources are designed to enhance the effectiveness of advertising campaigns. The launch will coincide with Cannes Lions in 2026. The new tools aim to provide deeper insights for advertisers. This support package seeks to strengthen creative campaign development.
NVIDIA and AWS have partnered to address the challenges of deploying large-scale AI systems in production. Their joint efforts provide solutions for critical requirements such as low-latency inference and fast vector search. The collaboration focuses on offering strong GPU price-performance alongside manageable infrastructure growth. NVIDIA's AI technology is integrated across Amazon OpenSearch and Amazon EC2 services. These integrations offer enterprises practical pathways to deploy sophisticated AI applications.
Businesses are seeking to develop tailored Artificial Intelligence solutions integrated into their specific workflows. Initial corporate efforts focused on gaining access to and piloting various frontier and open AI models. The current trend is moving beyond simple accessibility toward creating specialized AI agents. These advanced systems possess capabilities for complex reasoning and tool utilization. Building this trustworthy, purpose-built AI is the focus for modern enterprises.
Governments across Europe are actively debating and developing strategies to enhance their digital sovereignty in the face of advancing Artificial Intelligence. Key focus areas include establishing robust domestic AI frameworks and regulating technology usage within national borders. Policy discussions center on reducing over-reliance on non-European technological giants for critical AI infrastructure. Implementing cross-border cooperation is seen as necessary to create a unified European AI regulatory front. Ultimately, these efforts aim to ensure that Europe retains control over its technological destiny in the age of AI.
Getty Images is entering a content deal with OpenAI. This move comes amidst declining stock value for the image provider over the past year. The downturn correlates with the increasing emergence and capability of generative AI technologies. Details of the specific terms of the partnership were not provided in the excerpt. Overall, the agreement signals Getty's strategic response to market pressures from AI.
Krea released two open-weights models, "Krea 2 Raw" and "Krea 2 Turbo," to improve AI image generation beyond monotonous results. These models offer increased visual variety and customization compared to typical generators. Krea 2 Turbo stands out for its rapid 2-second generation speed at high throughput. Usage requires a custom license, necessitating payment for enterprises with over 50 seats and mandatory technical safeguards against illegal content. Both models are available for public download on Hugging Face.
Cory Doctorow argues that AI development promises job displacement for an entire spectrum of workers, creating "reverse centaurs" who assist machines like warehouse staff or self-driving truck monitors. He points to the pervasive nature of this technological encroachment across various industries. Despite warnings from figures like Elon Musk regarding existential threats, Doctorow critiques the grandiose claims of those developing AI. He asserts that the current discourse surrounding AI is largely about resource exploitation rather than human flourishing. Ultimately, the critique suggests that the promise of AI ignores its inherent potential to undermine autonomous living.
Meta has temporarily halted an internal program that monitored employee computer activity. The system tracked details such as keystrokes, mouse clicks, and onscreen content. This data collection was intended to generate datasets for training Meta's artificial intelligence models. The pause followed increasing privacy concerns among staff. Additionally, roughly 1,600 workers signed a petition opposing the tracking tool.
A German court recently ruled that Google is legally responsible for statements made within its AI search summaries. The court dismissed arguments suggesting users should independently verify the information or that they understood the potential unreliability of AI-generated content. Instead, the ruling classified the chatbot's summaries as an expression of Google’s own business activities. This case reflects ongoing legal debates concerning who holds responsibility for digital content online. Historically, law distinguishes between neutral carriers, which merely transmit speech, and publishers, which select and are thus liable for published content.
Concerns are rising among US parents and experts regarding the integration of AI in educational settings. Skepticism exists over the actual efficacy of using artificial intelligence tools like Google Gemini for learning. One parent noted an instance where a sixth-grade assignment required students to seek feedback from an AI chatbot on a science experiment. This prompted the parent to criticize such use, arguing it teaches children to rely on machines for thinking. Instead, suggesting collaboration among peers was deemed a more beneficial educational approach.
Evaluating agents using external LLMs incurs significant costs due to every API call made during trace evaluation. These costs escalate rapidly at scale because they are proportional to trace volume and tokens used per trace. Sampling external LLMs, often done for cost control, risks missing critical, high-impact low-frequency incidents. Fiddler Centor Models evaluate every trace without external LLM API charges or sampling. Consequently, Centor Models can be up to 98% cheaper than external LLMs even when the latter sample only a small percentage of traces.
Recent content covers diverse advanced AI topics including Joint Embedding Predictive Architecture (JEPA) and recursive self-improvement in AI. Discussions explain the mechanics of LLM inference and how attention mechanisms using Queries, Keys, and Values function. Furthermore, there are guides detailing local AI agents like Hermes vs. OpenClaw, and best practices for skill engineering. Responsible AI is highlighted as essential infrastructure for operational AI agents. The material also touches on specialized architectures such as xLSTM and world foundation model platforms from NVIDIA.
ASML's executive vice president of technology is showcasing a massive lithography machine crucial for advanced microchip production. This 150-ton apparatus utilizes extreme-ultraviolet (EUV) light generated by lasers to pattern transistor features on silicon wafers. The new machines can achieve an unprecedented resolution of eight nanometers, allowing for denser circuitry in powerful chips. These state-of-the-art devices are reaching chipmaking factories at a cost of $400 million each. Such technology is vital for sustaining Moore's Law and meeting the ravenous computing demands of the growing AI industry.
Rapid advancements in Artificial Intelligence create an increasing demand for vast amounts of data from enterprises. A significant hurdle is that much necessary information remains unstructured or inaccessible. This limitation restricts AI models' ability to effectively utilize available data. Overcoming this challenge requires developing a dedicated web data infrastructure layer. Such a layer aims to unlock and structure the pervasive, yet often hidden, data available across the web.
At age 18, the author traveled to Kuwait shortly after the first Gulf War concluded in 1991. The environment was highly chaotic with scarce electricity relying on generators. Debris and unexploded ordnance littered the area. Massive oil fires illuminated the surrounding desert landscape.
The final match between Argentina and France at the 2022 Men's World Cup in Qatar was anticipated as a major soccer event. With only twelve minutes left in extra time, referees faced significant pressure for crucial decisions. The tension was high leading into this late-game period. A key moment involved Lionel Messi on the pitch during this critical stretch of play.
AI factories face significant operational costs, with power consumption representing up to 40% of overall expenses. Energy is expended across various processes, including overhead, data ingestion, and both model training and inference generation. Optimizing energy efficiency requires making comprehensive improvements throughout the entire AI workflow. Implementing full-stack optimizations for both inference and training are crucial strategies to reduce this high energy drain. Such optimizations aim to maximize performance while significantly minimizing the substantial electrical demands of modern AI infrastructure.
Low-latency inference is becoming crucial as AI systems advance toward complex, multiagent workflows. Autoregressive Large Language Models (LLMs) require efficient decoding methods to maintain performance. DFlash Speculative Decoding offers a significant improvement for these processes. This technique boosts inference performance by up to 15 times on NVIDIA Blackwell hardware. Implementing DFlash allows developers to handle complex, real-time AI interactions more efficiently.
A tutorial demonstrates an offline pipeline using Graphify to convert a multi-module Python application into a knowledge graph. The process involves installing Graphify and locally extracting the graph structure using tree-sitter, eliminating dependency on APIs or LLMs. The resulting `graph.json` file is loaded into NetworkX for in-depth analysis. This analysis includes examining file types, relationship dynamics, centrality scores, and identifying functional communities within the codebase. Finally, static and interactive visualizations are generated to illustrate connections between modules, classes, functions, and database objects.
Nous Research incorporated a new `/learn` command into the Hermes Agent Skills System. This feature automatically generates standards-compliant `SKILL.md` files. It accepts input from various sources, including local directories, documentation URLs, past conversations, or pasted notes. The live agent utilizes its existing tools to source this material and subsequently writes the necessary skill without manual coding or a dedicated ingestion engine. This process streamlines workflow capture by directly creating executable skills from diverse inputs.
Generative AI has profoundly transformed software development, moving beyond simple autocomplete to enable full application generation. These advanced tools utilize large language models trained on code to understand context and user intent. They can produce working frontends, backends, and entire infrastructure layers with minimal manual configuration. The comparison piece reviews 16 top generative AI coding tools expected in 2026. These tools are suitable for various levels of developers, including early-level and seasoned software engineers.
Mistral AI released OCR 4 on June 23, 2026, enhancing document processing beyond simple text extraction. The new model provides structured output for each block, including a bounding box, typed classification, and confidence scores at both page and word levels. It supports a wide range of 170 languages. Functioning within a single self-hosted container, OCR 4 integrates into RAG, agentic, and enterprise search workflows via one API endpoint. This capability delivers citation-ready data for advanced AI applications.
Datalab unveiled "lift," a new 9B open-weights vision model. This model specializes in converting content from PDFs and images into structured, schema-matching JSON format. It employs schema-constrained decoding to ensure output validity and utilizes trained abstention to accurately return null for missing fields. Benchmark testing demonstrated its high performance, achieving 90.2% field accuracy across a set of 225 documents.
A tutorial details building a multilingual pipeline using NVIDIA Canary-1B-v2 for speech tasks. The process involves loading the model onto a GPU runtime and preparing audio to 16 kHz mono format for English ASR. Subsequently, it translates the transcribed speech into French, German, Spanish, and Italian while capturing word and segment timestamps. Furthermore, the guide covers exporting these translated subtitles as an SRT file. Finally, it demonstrates testing long-form transcription, batch processing, and benchmarking inference speed.
China's supercomputer, LineShine, has achieved the number one ranking on the Top500 list. This places it above its US counterparts, marking a significant technological milestone for China. The machine successfully displaced El Capitan, which was previously ranked at the top of the list. Its debut achievement is notable as it marks the first time since 2017 that a Chinese supercomputer has topped such a prominent global ranking.
Global markets experienced a tech sell-off after investors questioned high valuations and heavy spending on AI infrastructure. The downturn occurred as market focus shifted away from geopolitical events in the US with Iran. The Nasdaq index notably closed down by 2.2% on Tuesday due to this shift. Furthermore, the S&P 500 dropped by 1.43% during the afternoon session.
ElevenLabs launched an officially licensed replica of Michael Caine's voice to narrate an audiobook version of Homer’s epic poem, *Odyssey*. Director Christopher Nolan is preparing a blockbuster cinematic adaptation of *The Odyssey* for global theaters next month. This upcoming film is projected to emulate the box office and critical success of Nolan’s prior work, such as *Oppenheimer*. Michael Caine is one of Nolan's most frequent collaborators, having worked with him on eight films including the Dark Knight trilogy. The audiobook release precedes the major theatrical premiere of the epic tale.
Covers an in-depth examination of distillation, a critical technique within modern artificial intelligence. Distillation is highlighted as a particularly important method for advancing AI capabilities. The discussion provides significant technical detail on this specific machine learning process. It serves as an educational resource detailing the mechanism and significance of knowledge transfer in models.
Claude is being introduced as a potential new coworker within Slack. It is positioned to integrate into daily workflow communications alongside existing tools. Developers will have the capability to build custom, "Clippy-like" desktop pets using this technology. This suggests an expansion of AI assistance beyond simple chat functions. The focus is on making artificial intelligence an integrated and interactive part of the virtual workspace.
Version 1.0a35 introduces significant database management improvements to Datasette. Users can now define and create tables via a new interface, supporting features like primary keys, foreign keys, and custom constraints. The release also adds an "Alter table" function allowing users to modify existing tables by adding, renaming, or dropping columns, and updating constraints. Furthermore, documentation for the Template context has been significantly improved, listing available variables as stable until Datasette 2.0. These updates enhance database structure control and template customization within Datasette.
A developer built a playground user interface using Claude Code to test functionality involving OPFS (Origin Private File System). The goal is to explore if Datasette Lite, a Python Datasette application running in the browser via Pyodide and WebAssembly, can edit local, persistent SQLite files. This setup specifically addresses potential file system access capabilities within a web browser environment. The implementation aims to make such advanced data manipulation possible directly through the client side.
A new approach for Retrieval Augmented Generation (RAG) involves implementing anchor detection using parallel detectors. These specialized detectors are employed to gather information before a final Large Language Model (LLM) call is made. This method aims to improve RAG performance by structuring the information retrieval process. The technique suggests integrating multiple specialized detectors rather than relying on sequential filtering methods. Ultimately, it streamlines the architecture to finish with a single, powerful LLM prompt.
Covers the fundamental concept of utilizing loops within coding agents. Provides guidance on implementing effective looping structures for programming tasks. The information was originally published on the Towards Data Science platform. Understanding loops is key to developing advanced coding capabilities. Techniques are detailed for building powerful functionalities into code using Claude.
Author spent significant time struggling with a complex data preprocessing task using the Pandas library. The author then sought assistance from Gemini for the same problem. Gemini quickly provided a solution that resolved the issue in seconds, contrasting sharply with the hours of manual effort. This experience highlights Gemini's speed in solving technical coding problems. However, the piece also emphasizes that understanding core data science fundamentals remains crucial for identifying optimal solutions.
Suggests a paradigm shift in understanding Retrieval-Augmented Generation (RAG) systems for enterprise use cases. Advocates viewing retrieval as a filtering process rather than traditional searching by mere string matching. Recommends developers focus on techniques like "filtering line\_df and toc\_df" within document intelligence workflows. Highlights the strategy of using small anchors to expand context, improving accuracy. This model provides a necessary conceptual framework for building robust enterprise RAG solutions.
Guides readers through building a personal, local AI coding agent. The process involves specific steps, starting from installing Ollama. Users can then launch the OpenCode platform using a locally hosted model like Gemma 4. This comprehensive guide aims to enable users to run advanced AI coding tools without reliance on cloud services.
Provides twenty-four functional examples for building agentic applications using CUGA. These examples are showcased on a lightweight, easily deployable harness. The resource aims to simplify the process of creating advanced AI agents. Developers can utilize these practical demonstrations to accelerate development. Ultimately, it equips users with ready-to-use templates for agentic workflows.
A new PhD student in Computer Science is seeking suggestions for interesting open research questions in Reinforcement Learning (RL). The individual has prior experience with RL and representation learning from a master's degree. They are interested in projects related to core RL, potentially using environments like Atari. Alternatively, the student is looking for research opportunities situated within the reasoning or Large Language Model (LLM) domains. Guidance, helpful sources, or summaries of the current research state in these areas are highly appreciated.
A user is seeking recommendations for video games suitable as environments for reinforcement learning (RL) training. The primary constraint for these suggestions is that the games must not require high-end graphics hardware. This need arises because the user desires an easily runnable training setup. No specific game titles or expert advice were provided in the content excerpt. Essentially, the post is a request for low-resource, game environments for RL research.
Modern AI progress was heavily defined by sheer model scale, leading to massive, powerful frontier models capable of diverse tasks. However, these large models are costly, slow, and impractical for many real-world, niche applications. Distillation techniques are positioned as the solution, moving focus beyond mere size to practical intelligence. This approach aims to create smaller, more specialized, and efficient AI systems suitable for specific enterprise or local use cases. The goal is achieving reliable, repeatable competence rather than just generic, all-encompassing brilliance.
UK startup Bactery is developing batteries capable of generating usable electricity from bacteria found in soil. This technology can be buried passively, providing power for over 25 years without any required maintenance. The initial application focuses on powering agricultural sensors. Ultimately, the goal is to scale up this system to potentially offset household electricity consumption using a garden as a micro-power plant.
NVIDIA BioNeMo is an open developer platform designed for AI-driven life science research. It accelerates innovation by providing tools and models across the entire AI lifecycle, transforming physical lab results into digital insights. The platform rests on five pillars: large-scale data access, open-source biological model design, GPU-optimized libraries, customizable training frameworks, and production deployment methods. To address sparse biological data, NVIDIA is partnering to release comprehensive datasets like the AlphaFold Database. BioNeMo components are released under various licenses, including CC BY 4.0 for data and Apache 2.0 for code.
Longevity science is seeing billions invested in methods to reverse aging by returning cells to a younger state. An upcoming session will explore the potential and feasibility of these experimental "reprogramming" treatments. Mary Beth Griggs and Jessica Hamzelou are scheduled to discuss this new focus area of longevity research. The event is slated for Tuesday, June 30, 2026. Past sessions have covered topics ranging from world models in AI to high-profile legal cases like Musk v. Altman.
Stripe is funding an effort aimed at combating respiratory infections. This initiative joins support from both Anthropic and OpenAI. The goal addresses common illnesses like the cold, which cannot currently be prevented entirely. While traditional advice includes Vitamin C supplementation and social distancing, this new venture aims for a different solution. The involvement of major tech companies highlights significant backing for developing countermeasures against such infections.
The piece suggests Super Mario involves surprising mathematical elements. It sets the scene with Mario, an ambitious plumber from Brooklyn, rescuing his love from captivity. His journey takes him across dangerous, monster-infested, pipe-filled terrains. The text implies that basic school problems don't capture the depth of calculation required in the game. This narrative frames a quest built upon hidden mathematical challenges inherent to the gameplay.
Current national discussions regarding education frequently focus on the potential and risks associated with artificial intelligence. Despite this, MIT is actively addressing an older, significant issue. The core concern involves the highly uneven access to resources among American high school students. This work represents a deep but established area of educational importance. Ultimately, the initiative aims to improve student opportunities despite the current technological focus.
A three-year-old immunology mystery was solved with the assistance of GPT-5 Pro. The AI provided crucial new insights regarding T cell behavior. This breakthrough represents a significant advancement in understanding complex immune system functions. Potential applications for this discovery include advancements in cancer research. Furthermore, it could greatly aid ongoing autoimmune disease studies.
Google DeepMind is collaborating with the legendary footballer Pelé to preserve cultural heritage. The visual materials highlight various stages of film production involving Pelé. These visuals include scenes from on-set filmmaking environments. They also showcase footage from dedicated soccer training sessions. Additionally, the imagery features user interfaces for professional video editing software.
No-code Artificial Intelligence (AI) is emerging as a significant development in technology. This trend democratizes AI creation, making advanced capabilities accessible without deep programming expertise. It suggests that traditional coding skills may be becoming less exclusive for building AI applications. The shift indicates that users can now build sophisticated AI tools through visual or simplified interfaces. Essentially, no-code platforms are lowering the barrier to entry for AI development.
MIT researchers discussed several key issues regarding artificial intelligence's broader societal impact at the AI and Society Forum. Specific areas of concern included changes to traditional employment structures. Furthermore, the forum addressed the potential implications of AI for democratic processes. The discussions highlighted significant questions surrounding technology's role in society. Overall, expert input focused on navigating responsible integration of advanced AI systems.
Emotion AI estimates human feelings using facial expressions, voice tone, and behavior across applications like interviews and call centers. While early systems struggled with nuance, the field is evolving toward "human-context AI" to interpret overlapping, real-world signals. Current advancements fuse physiological, environmental, and personal data streams for increased accuracy in emotion recognition. Despite technological growth, machines struggle because they capture signals without understanding the essential context behind them. Researchers are focusing on making computers read the entire scene rather than just discrete inputs like a screen.
Garment workers in an Indian factory were required by management to wear head-mounted cameras while on shift. Initially finding the requirement amusing, the workers' mood shifted toward concern over the surveillance. Supervisors directed the installation of these small cameras onto the foreheads of staff members working a specific line. The purpose behind this unusual filming arrangement was not explained to the employees.
Introducing RIFT-Bench, a methodology designed for dynamic red-teaming of autonomous, large language model (LLM) powered agentic AI systems. It addresses limitations in current security evaluations by providing unified testing across diverse and heterogeneous architectures. The framework operates through two automated phases: Discovery, which maps the system's structure, and Scanning, which executes adaptive adversarial attacks. RIFT-Bench evaluates the target system itself using a wide variety of dynamically adaptable probes targeting multiple attack vectors. Demonstrating its effectiveness across 45 different agentic systems, it also supports direct assessment of existing mitigation strategies.
Neuro-Symbolic Drive is a new framework that improves Vision-Language Action (VLA) models by grounding their reasoning in explicit rules. It supervises the VLA using structured, rule-grounded reasoning traces extracted from classical rule-based planners. These traces detail the planner's internal decisions and are directly coupled to the executed trajectory during fine-tuning of Qwen3.5-4B. By deriving supervision this way, it ensures causal connectivity between the reasoning steps and the resulting motion planning. On benchmarks, this method significantly reduced error rates, such as decreasing ADE@3s from 0.47 to 0.26 with three cameras.
The paper examines the philosophical and technical boundaries between automation and genuine agency in modern AI systems like LLM "coding agents." It analyzes current AI agent architectures across five dimensions: goal, identity, decision-making, self-regulation, and learning. The authors argue that true agency requires these structures to be intrinsically internalized within the system, rather than relying solely on external engineering scaffolds. This distinction separates mere "agentic" tools from truly autonomous "agentive" systems capable of open-world operation. To address this gap, a proposed Goal-Identity-Configurator (GIC) architecture integrates goal decomposition, identity evolution, and world model simulation for general-purpose agency.
A hierarchical multi-agent reinforcement learning framework addresses the trade-off between strong empirical performance and theoretical safety guarantees in safety-critical systems. It enforces hard safety constraints at a low level using a constraint manifold, even under mild assumptions. High-level policy learning manages effective coordination among multiple agents. The proposed method offers theoretical safety guarantees for the multi-agent setting and ensures stationary learning dynamics for stable training. Empirically, it shows competitive performance with near-perfect safety rates and good generalization across different agent numbers and obstacles.
As AI deployment increases in diverse, high-stakes settings, model alignment must generalize beyond training data, especially for reinforcement learning (RL) which risks misalignment via reward hacking. Researchers built a dataset spanning varied domains to train beneficial traits like truthfulness and fairness using RL. Training models on this dataset improved performance on over 80% of independent out-of-distribution alignment benchmarks compared to baselines. Notably, training limited to one domain, such as health, provided broad improvements in non-health areas by mitigating hacking and deception. Furthermore, beneficial trait RL enhanced alignment persistence, showing greater resistance against adversarial prompts and harmful fine-tuning attempts.
Mechanistic interpretability has advanced in locating circuits, yet explaining their function is challenging. Researchers introduced AgenticInterpBench, a benchmark using 84 synthetic transformer circuits for component annotation. They propose HyVE, an agentic explainer that iteratively hypothesizes and validates circuit components through observation. Testing showed HyVE recovers useful explanations across various large language model backbones, although performance varies. Ultimately, while LM agents show promise, ensuring reliable validation remains the primary hurdle for automated circuit explanation.
Recommender systems often trap users in "filter bubbles" by optimizing solely for immediate engagement. Traditional single-objective models cannot balance this against crucial values like information diversity and provider fairness. The proposed solution is a multi-objective reinforcement learning framework treating recommendations as a semantic Markov decision process. Utilizing a Pareto-DQN agent, the system manages engagement, diversity, and fairness as separate reward signals, preventing simple scalarization. Experiments demonstrate that this method improves societal objectives while maintaining minimal impact on user engagement by mapping the Pareto frontier.
Mental disorders, particularly depression, pose significant risks to female sex workers due to violence, stigma, and economic hardship. Recognizing the limitations of current machine learning models for this complex population, a hybrid predictive model was proposed. This model combines an ensemble feature selection approach (using ANOVA and mutual information) with Harris Hawks optimization-tuned logistic regression. When tested on 3,005 female sex workers, the system achieved high performance metrics: 95.78% accuracy, 95.77% F1 score, and 0.96 AUC, outperforming traditional classifiers. The resulting explainable AI tool identifies key contributors to depression, such as PTSD, client-related violence, and occupational factors, facilitating early, evidence-based care.
Current methods for transferring reasoning skills often rely on imitating specific solution steps, which leads to memorization rather than generalizable problem-solving. Strategy-Guided Policy Optimization (SGPO) overcomes this by distilling reusable strategy descriptions instead of entire answer trajectories. SGPO constructs both autonomous and strategically guided paths for comparison, using a token-level forward-KL objective for selective knowledge transfer. The framework employs adaptive instance weighting to guide learning when necessary and reduce it as the model improves. Experiments on mathematical benchmarks show SGPO significantly outperforms existing baselines across different model types.
Algorithms are central to contemporary scientific research within AI, but existing studies often overlook their collective influence through interconnections. This study constructs large-scale co-occurrence networks by analyzing the full text of academic papers in natural language processing (NLP). Deep learning models were used to extract algorithm entities and build cumulative, overall, and annual co-occurrence networks. Analyzing these structures reveals that algorithm networks exhibit complex network features, with connections becoming denser over two decades. The research demonstrates that highly influential algorithms maintain strong core positions within the network structure across time.
An automated search pipeline was developed to systematically explore heterogeneous 4-Expert Mixture-of-Experts (MoE4) architectures using the LEMUR dataset. This generator combines base architecture families into ensembles controlled by a convolutional gating network, utilizing temperature scaling and mixup augmentation. Over a 28-day campaign, the pipeline generated 1,021 testable candidate models across 4,463 total combinations. A significant finding revealed that due to search method limitations, nearly the entire explored space was biased toward the AirNet family. Within this limited scope, ShuffleNet and MobileNetV3 consistently produced the highest accuracy ensembles.
Comparing weight updates from six offline reasoning training methods (including SFT, RFT, DFT, RIFT, Offline GRPO, and DPO) using identical math rollouts reveals distinct geometric differences. SFT, RFT, and RIFT exhibit highly colinear weight deltas with similar accuracy on GSM8K. Conversely, DFT diverges significantly from reward-weighted methods, while Offline GRPO adds a large component orthogonal to the SFT direction. Notably, DPO achieves the highest reported accuracies on both GSM8K and AIME26 within this protocol, despite occupying a near-orthogonal subspace.
Causal reasoning is crucial for data-driven decisions but faces challenges when data are distributed due to privacy or logistical constraints. Federated learning (FL) addresses this by enabling collaborative analysis without centralizing raw data, leading to federated causal discovery (FCD) and inference (FCI). This survey provides a comprehensive review of the field using multi-dimensional taxonomies organized by structural learning approach, data partitioning, and knowledge sharing. It details FCD methods across methodological paradigms and federation topologies, while categorizing FCI based on target estimands and estimation strategies. Finally, it unifies FCD and FCI, positioning them as sequential stages in a complete federated causal reasoning pipeline that shares concerns regarding privacy and efficiency.
Physical neural networks aim for low-power machine learning by integrating computation with analogue device physics. This research proposes placing trainable nonlinear functions directly onto physical connections, moving beyond architectures where only scalar weights are supported. By implementing these functions using analogue band-pass filters on programmable arrays, the resulting networks excel at representing smooth, continuously valued targets like robotic kinematics and continuous control. These specialized networks require significantly fewer nodes and connections compared to multilayer perceptrons for such tasks. A dedicated CMOS implementation is projected to achieve power consumption around 30 microwatts, confirming that trainable connection nonlinearity provides the primary advantage.
A new gray-box workflow, PC-MCMC-CIGP, addresses the challenge of extracting governing equations from noisy chemical time-series data by combining multiple methods. This technique integrates spike-and-slab topology sampling and hard conservation principles with a Chemical-Informed Gaussian Process (CIGP) for parameter calibration. The core contribution lies in coupling these components into a physically constrained workflow featuring explicit uncertainty-aware acquisition choices. On the $\text{H}_2 + \text{Br}_2$ benchmark, the method successfully distinguishes true elementary reaction pathways from misleading phenomenological models. Furthermore, applying this to styrene epoxidation improved final yield by 12.5% compared to established Gaussian Process optimization baselines.
Domain generalization struggles with label mismatch when dealing with unseen target domains. Open set domain generalization aims to recognize novel, unobserved classes within these new environments. Traditional one-vs-all classifiers suffer from decision boundary skew due to sample imbalance, causing over-rejection of out-of-distribution data. To address this, the paper proposes dualistic Meta-Learning with joint Domain-Class matching (MEDIC). MEDIC optimizes boundaries by simultaneously considering implicit gradient matching across inter-domain and inter-class task splits.
Researchers re-evaluated bivariate causal direction methods on identical datasets using a "same-hands" protocol with no tuning allowed. A minimal, parameter-free baseline involving sorted-conditional compression achieved 74.7% weighted accuracy. Performance comparisons showed that published figures for advanced methods like SLOPE and RECI often relied on biased or inconsistent evaluation protocols. Under this common ruler, high-performing methods clustered in the low-to-mid 70s, with the simple compressor achieving comparable results to the best reported scores. The study details mechanisms inflating published metrics, such as test-set model selection, and provides corrected benchmarks for established causal inference techniques.
Molecular surfaces contain geometric and physicochemical patterns vital for antibody-antigen recognition and epitope prediction. Current methods often fail to model discontinuous epitopes because they rely only on sequences or backbone structures. SurfBind introduces a novel, surface-centric learning framework that operates directly on molecular surface representations. It integrates diverse cues using a Transformer architecture featuring patch-level modeling and binder-aware cross-attention. Testing on established benchmarks like SAbDab and DB5.5 confirms SurfBind achieves state-of-the-art performance across different antibody types and conformational states.
Terrestrial water storage (TWS) change, critical for understanding the global water cycle, has been observed by GRACE satellites since 2002. To extend this record, researchers developed a novel approach using a multi-variate time series Graph Neural Network (MTGNN). This model reconstructs monthly TWS anomalies back to 1940 by learning from daily ERA5 meteorological data and existing GRACE observations. The MTGNN incorporates spatial dependencies via an adjacency matrix combining proximity and lagged climatic correlations. Evaluation showed the graph-based model achieved a high basin-mean Pearson correlation of 0.94, proving statistically competitive with fewer required predictors than prior methods.
Degeneracies occur when multiple parameters or labels result in similar data, complicating machine learning and inverse problems. The degeneracy distillery is a new method that detects and resolves these parameter degeneracies automatically and symbolically using only parameter-data pairs. It characterizes degeneracies as an inherent property of the physical model by analyzing the information geometry of the likelihood. The technique estimates and flattens the Fisher information matrix globally, unlike posterior methods that flatten only at one point. This results in discovering symbolic coordinate transformations that reduce simulation needs for neural posterior estimation by up to tenfold while providing deeper physical insights.
Visual assessment of residual plots for diagnosing linear models is currently subjective and difficult to scale manually. A proposed solution involves automating this process using a new R package that incorporates a computer vision model. This model evaluates the residuals by predicting a Visual Signal Strength (VSS). The development also includes an accompanying Shiny application to ensure user-friendliness. Ultimately, this tool provides analysts with quantitative support for assessing overall model fit.
Addresses model selection among probabilistic time series models by utilizing proper scoring rules to generate an average score for each individual time series. Previous methods often use summary statistics like mean, median, and mean rank, which can yield conflicting model selection decisions due to the skewness of the score distribution. The study demonstrates that as the test set size increases, these diverse criteria converge on the same conclusion. Specifically for short test sets, only the mean score reliably identifies the true underlying model. Analysis using intermittent time series confirms this convergence trend and highlights the necessity of large test sets.
Degeneracies occur when multiple parameters or labels yield similar data, complicating machine learning and inverse problems because these methods rely on parameter distinguishability. Introducing the "degeneracy distillery," a method estimates and resolves these degenerate parameter combinations both automatically and symbolically using the Fisher information matrix derived from parameter-data pairs. By analyzing the likelihood's information geometry, degeneracies are characterized as intrinsic to the physical model itself, independent of observed data. The technique discovers symbolic coordinate transformations that identify parameters with independent data effects. These resulting coordinates flatten the Fisher information globally in expectation, significantly reducing simulation needs for subsequent posterior estimation compared to point-based methods while providing deeper physical insight.
Survival analysis demands large, diverse patient datasets, which are often restricted by privacy concerns. Federated learning (FL) offers a solution by training models across multiple institutions without centralizing raw data. This study systematically evaluated three survival models—Cox, DeepSurv, and RSF—using cross-institutional breast cancer data. Results indicated that FL generally outperforms local training and approaches centralized performance; Random Survival Forest demonstrated the best overall balance of robustness. The research also recommended specific federated optimization strategies (FedAvg and FedProx) based on varying client distribution diversity for clinical implementation.
Proposes a robust estimation scheme for linear state-space models facing compound-Gaussian noise, typical in radio interferometry affected by RFI. The method utilizes Stochastic Approximation Expectation-Maximization (SAEM), modifying the standard E-step with Monte Carlo sampling and Gibbs updates. This approach achieves tractable inference despite the heavy-tailed likelihood imposed by non-Gaussian noise. Numerical experiments demonstrate that this technique substantially enhances reconstruction fidelity compared to Gaussian EM or RTS smoothers. These findings confirm the value of incorporating heavy-tailed state-space modeling for interference-prone imaging applications.
Presents a dual-edge spatial-Jacobian image graph for enhancing the interpretability of diabetic retinopathy (DR) grading from fundus photos. This framework represents each image as a node with four aligned evidence streams: vessel information, lesion maps, contrastive embeddings, and morphometric biomarkers. It models relationships using two edge branches—one encoding vessel-lesion geometry and another detailing embedding-biomarker sensitivity. The combined structure processes data through lightweight attention mechanisms. Tested on 2,910 images, the model achieved strong performance metrics like 0.8312 quadratic weighted kappa for overall grading.
AI-assisted interviews allow respondents to naturally describe experiences which are then mapped into structured survey variables by an AI system. Because this mapping process is fallible and potentially varies across subgroups, the paper introduces Adaptive Matrix Validation (AMV). AMV requires respondents to complete an interview followed by a small set of randomized structured questions for statistical adjustment. The estimation technique first calibrates the AI-mapped values using validation answers from other participants before correcting errors with the target respondent's specific validation data. The proposed methods provide estimators and planning formulas for item means, subgroup estimates, and regression coefficients derived from these complex interview mappings.
Emphasizing model transparency due to increasing reliance on machine learning in various sectors, this work addresses the limitations of current methods which primarily reveal correlations. It proposes integrating causal machine learning techniques with inherently interpretable models specifically for cross-sectional data. The methodology is evaluated based on both predictive accuracy and interpretability. Results demonstrate that the proposed approach maintains competitive prediction performance while offering structural transparency regarding causal relationships. This contributes to decision support by enabling informed, transparent, and causally grounded "what-if" scenario evaluations.
Data augmentation exploits known invariances by transforming training samples using a group action. Computational costs restrict using transformations from large groups entirely. This research proposes using Fourier analysis to investigate if sampling only a subset of these group elements retains the statistical benefits of full augmentation. Results demonstrate that for many classical learning problems, partial augmentation achieves the same minimax rates as full augmentation with vanishing error. Conversely, the study proves that achieving exact invariance through data augmentation necessitates averaging over the entire group, precluding use of strict subsets.
NoLimits.jl is an open-source Julia package designed for flexible and composable nonlinear mixed-effects modeling. Its macro-based language allows users to build observation and latent-state models from diverse components like ODEs, Markov models, and neural networks. The package supports adaptable distributions for both observations and random effects. It offers a unified interface for frequentist inference using methods such as Laplace approximation and SEM, alongside Bayesian MCMC. These features significantly broaden the scope of estimable nonlinear mixed-effects models within one open framework.