Moonshot AI launched Kimi K3, an open-weights model with 2.8 trillion parameters and a 1 million-token context. It incorporates features like native multimodal input, Kimi Delta Attention, and Attention Residuals. The model is accessible via Kimi.com, Kimi Work, Kimi Code, and an API, with open weights promised by July 27, 2026. Kimi K3 is positioned as a frontier-class model, focusing on agentic coding and vision-in-the-loop workflows. This release is highlighted as the largest open-weight model ever released.
AI models can sound confident even when they are incorrect, which highlights reliability as a major challenge for artificial intelligence today. This issue was explained by UC Berkeley professor and Scaled Cognition CTO Dan Klein on the podcast and YouTube channel. The content addresses why AI can generate seemingly accurate but false information. Understanding this gap between confidence and accuracy is crucial for advancing AI. Reliability remains a significant hurdle in the current state of AI development.
AI systems prioritize information found at the top of content, as 44% of ChatGPT citations originate from the first third of a webpage. To ensure proprietary data is cited correctly, content should follow a "ski-ramp" structure by placing headline statistics, methodology, and key findings upfront. Burying strong insights deep in an article causes AI systems to overlook them or cite aggregators instead. Structuring data this way maximizes visibility for brand-owned statistics. Therefore, publishing the most important proprietary data first is crucial for securing AI citations.
Lila Sciences envisions the future of science as an automated lab operating like an AI data center. They are building a system where AI-guided robotics run experiments 24/7 to develop a scientific superintelligence. This approach treats the lab as an infinite token generator, producing experimentally validated reasoning traces from vast amounts of data. The goal is to use this framework to tackle challenges across biology, chemistry, and materials science simultaneously. By focusing on flexible iteration rather than raw throughput, they aim for generalizability in scientific discovery.
Scott Galloway, Ed Elson, and Mia Silverio have posted new notes on their Substack. Galloway noted that TBPN sells ads similarly to how Formula 1 teams sell sponsorships, and this approach is profitable. Elson shared an update suggesting that things are not looking good. Silverio shared information regarding the Federal Reserve data, specifically noting that 49% of adults aged 18 and over are being referenced in a recent statistic.
Overloading AI systems with more agents and larger token budgets is often the default solution to implementation problems. To improve performance, focus should shift from simply adding more resources to redesigning workflows and setting better thresholds. Teams must prioritize clear, measurable goals before deploying additional AI agents. Designing systems that emphasize token efficiency is crucial for reliable agentic pipelines. This approach ensures that AI agents are deployed effectively rather than just being overloaded with tasks.
Visa believes stablecoins will serve as core infrastructure for micropayments within the emerging AI agent economy. This is specifically for frequent, sub-$1 transactions between software systems, termed "micro commerce." Traditional card networks are deemed inefficient for these high-frequency machine-to-machine payments due to fixed processing costs. Visa positions cards for consumer purchases and stablecoins for behind-the-scenes agent micropayments as complementary payment rails. Visa is already developing infrastructure, including tools and partnerships, to support this dual-rail model.
Heat pumps are rapidly gaining market share in the US by outpacing natural-gas furnaces. Despite the end of key tax credits for heat pumps in 2025, sales have remained strong and even increased in early 2026. Heat pumps are highly efficient as they move heat rather than burning fossil fuels. Market strength suggests that demand for these systems is not solely dependent on government incentives. This technology is progressing as a major tool for building decarbonization globally.
Canadian universities are increasingly dealing with AI cheating cases, with incidents like the Brown University situation becoming more common across the country. Data shows a sharp rise in reported academic misconduct linked to AI use at institutions like UBC, often involving students who are confused or overwhelmed. In response, institutions are focusing on restructuring assessments and using human judgment instead of unreliable detection software. A key policy debate centers on distinguishing between illegitimate shortcuts and legitimate AI-assisted processes for student work. This ambiguity creates a challenge for future graduates entering the workforce.
Weather forecasts influence critical decisions for farmers, utilities, and prediction markets, creating potential risks if data is sabotaged. Traditional forecasting relies on observational data which must be protected to maintain accuracy, especially as AI-driven models become more central. Past incidents, like the manipulation of weather station data at CDG Airport, highlight the vulnerability of this system to fraud and coordinated attacks. To mitigate these risks, enhanced data quality controls, data defense mechanisms for AI, and continuous accountability across the entire data chain are essential. Strengthening oversight and coordination is necessary to safeguard observational data in the evolving field of AI weather prediction.
A partnership has been established with the Georgia Public Library Service to offer free career and AI training to public library cardholders. This initiative provides access to Google Career Certificates and Google AI courses without requiring prior experience or a college degree. The training includes the Google AI Professional Certificate for understanding AI tools and various job-ready certificates like cybersecurity and data analytics. Graduates of the career certificates gain access to a job platform connecting them with over 150 major U.S. companies. Over seventy percent of graduates report positive career outcomes within six months.
The Digital Markets Act (DMA) decisions risk undermining European security and privacy safeguards for millions of users. These rulings disregard evidence showing potential harm, despite prior offers to protect users. The ruling threatens device security by allowing external apps sensitive permissions without necessary vetting mechanisms. Furthermore, private searches could expose data to unfamiliar companies without proper anonymization or user consent, endangering privacy and national security. A flexible, evidence-based process is needed to balance market goals with essential security and privacy protections.
Over half of enterprises have already experienced an AI agent security incident or near-miss, highlighting a significant gap between agent autonomy and existing controls. A key weakness is identity management, as only a third give every agent a scoped identity, with many agents still sharing credentials. Furthermore, only three in ten enterprises isolate their highest-risk agents, indicating a failure to contain potential damage. Enterprises primarily rely on provider-native security tools rather than purpose-built agent security solutions. Despite high satisfaction with current tooling, there is a clear trend toward replacing these controls as the risk of autonomous agents grows.
Ofcom is investigating TikTok over concerns that its age verification methods are ineffective, potentially exposing children to harmful content like suicide and pornography. The regulator is concerned that TikTok's methods for inferring age may fail to correctly identify a significant proportion of young users. This investigation occurs as the UK government prepares a social media ban for under-16s, increasing scrutiny on age verification practices. Ofcom has recommended that platforms using age inference should adopt more effective methods for determining age compliance. TikTok maintains it uses expert-informed rules and technology to ensure age-appropriate experiences.
Hyperscalers facing pressure over data center water use are suggested to buy exclusive country clubs. These golf courses could then be converted into public parks to promote sustainability. The proposal suggests paying for guides and binoculars to engage former members in birdwatching. This approach aims to help people embrace a more sustainable hobby. As an example, purchasing one-third of the golf courses in the Coachella Valley could save significant water usage.
A surveillance revolution is transforming marine governance in Indonesia through new technologies. This transformation involves using remote-sensing and analytical tools to monitor fishing activities. Specific applications include vessel tracking and algorithmic enforcement in Indonesian waters. Yogi Putranto, a fisheries intelligence official, oversees these monitoring efforts at the Ministry of Marine Affairs and Fisheries. Consequently, digital surveillance is reshaping how fishery enforcement is carried out in Indonesia.
Large Language Models follow the "ski ramp" pattern of attention, prioritizing the beginning and end of content while ignoring the middle. To win AI citations and rankings, content must adopt the BLUF (Bottom Line Up Front) structure, stating the most important information immediately. This approach helps LLMs efficiently extract facts, increasing the likelihood of citation. Copywriters should use this knowledge by structuring content with multiple peaks of attention through inverted pyramids and front-loading semantic density. Adopting BLUF is crucial for achieving "cite-ability" in the age of Answer Engine Optimization (AEO).
TLDR AI is a free daily newsletter delivering the most interesting AI news, research, and tools in a five-minute read. It boasts 1,100,000 subscribers and sends updates every weekday summarizing model releases, research papers, and new tools with source links. The newsletter is specifically designed for engineers, researchers, and technical professionals who seek dense, engineering-focused information on AI. It distinguishes itself by leaning heavily into research papers and developer tools rather than general hype. TLDR AI differs from other newsletters by focusing on a deep, technical signal for its readership.
ChatGPT heavily favors front-loaded content, pulling the majority of citations from the first third of an article. This pattern suggests AI prioritizes immediate classification, direct definitions, and dense entities placed early in the text. Highly cited content also features definitive language, conversational structures, entity richness, balanced sentiment, and business-grade clarity. The study concludes that writers should surface key insights and conclusions at the beginning of content for better AI retrieval. Consequently, structured, briefing-style content performs better than purely narrative guides for AI engagement.
OpenAI created GPT-Red, an LLM super-hacker used to automate safety evaluations like red-teaming for its models against cyberattacks. The article also discusses why heat pumps remain popular in the US despite a recent tax credit ending. Heat pumps are advancing as they surpass natural-gas furnaces in efficiency. The piece further details various technology and climate-related news, including AI developments and robotics.
OpenAI CFO Sarah Friar introduced an AI scorecard to measure return on investment. This scorecard focuses on quantifying value derived from AI work. Key metrics include the cost per successful task, dependability of the AI, and the return on compute. The goal is to provide a practical way to assess the value of AI implementation. These metrics help evaluate the usefulness and efficiency of AI-driven efforts.
Students can simplify back-to-school shopping by using Google's features for personalized recommendations. These tricks include doing thorough homework before buying by asking detailed questions to AI Mode. Connecting apps allows for more personalized shopping results based on personal preferences. Shopping with Google Lens helps identify and find similar items in stores or online. Users can stay on budget by checking price history and track price alerts. Finally, virtual try-on technology enables students to see how items look before purchasing.
Google partnered with the University of Waterloo to launch the Futures Lab, an eight-week program focused on building artificial intelligence prototypes with expert mentorship. Students from diverse majors engage in hands-on "vibe coding" and apply design thinking to solve real-world problems. The initiative develops practical technical skills, communication abilities, and teamwork necessary for future careers in an automated world. Projects like SignFluent and Kanji Garden demonstrate the program's capacity to create tangible applications. This experience provides graduates with a competitive edge by building confidence and showcasing their ability to collaborate and explain complex ideas.
NotebookLM has been renamed to Gemini Notebook, evolving into a tool integrated across the Google ecosystem. It now includes a secure cloud computer that allows for running code directly within notebooks for advanced data analysis. These notebooks can sync across the Gemini app and Google Search, making research more accessible. This update enhances the product's capabilities by connecting research to broader Google services. The goal is to create a smarter, more connected way to study and work with research materials.
Integrating context-aware video AI agents requires connecting them to enterprise workflows like content management and ticketing systems. This integration addresses the challenge of siloed video systems, knowledge bases, and operational tools by enabling agents to perceive, reason, and act on video analysis. NVIDIA's NemoClaw orchestrates specialized blueprints like Video Search and Summarization (VSS) and Retrieval-Augmented Generation (RAG) to achieve this. This orchestration allows for structured reporting, knowledge retrieval, and the programmatic execution of actions based on video insights. The resulting system enables faster, more consistent, and automated responses by turning video understanding into coordinated workflow action.
Inkling is a general-purpose model released by an AI startup. It was founded by a former CTO of OpenAI. The model incorporates consideration for token usage in its operation. This makes it both broad and efficient.
Nebius is adopting an "asset-light" data center model for expansion. The company is partnering with infrastructure providers to increase its computing capacity. This approach allows Nebius to grow its operations without incurring the full financial burden of infrastructure ownership. By leveraging partners, Nebius shifts the cost responsibility for infrastructure. Essentially, they are utilizing external resources to scale their compute capabilities efficiently.
Enterprises are investing in AI infrastructure much faster than they can measure its economics, creating a compute gap. Most organizations run AI on existing hyperscalers but plan to evaluate specialized clouds and new accelerators over the next year. A significant majority intend to switch infrastructure providers within the next few months, prioritizing integration and total cost of ownership over sticker price. Currently, most GPU capacity is underutilized, and fewer than half can rigorously track compute costs and returns. This visibility gap is compounded by an emerging bottleneck in large-scale inference shifting from compute to memory, which many enterprises are unaware of.
NVIDIA released the Nemotron 3 Embed model collection, offering three open checkpoints: 8B BF16, 1B BF16, and 1B NVFP4. The 8B checkpoint ranked number one on the Retrieval Embedding Benchmark (RTEB) with a score of 78.46. These models are transformer encoders trained across 34 languages and support up to 32,768 tokens. The 1B model was created through pruning and distillation from the larger checkpoint, while the NVFP4 variant achieves high throughput on Blackwell hardware. The release provides optimized checkpoints and serving solutions for RAG, agent memory, and code retrieval applications.
A new approach, Conditional Memory via Scalable Lookup (Engram), introduces a second dimension of sparsity by allowing large language models to delegate knowledge retrieval to static lookup tables instead of consuming computational resources for it. This mechanism frees up the neural backbone to focus its reasoning capacity on complex problem-solving, leading to improved performance in tasks like reasoning and long-context understanding. The study found an optimal hybrid allocation between dynamic computation and static memory, which forms a U-shaped tradeoff across different budgets. This delegation reshapes model thinking by allowing lower layers to focus on pattern matching while deeper layers engage in abstract reasoning. Ultimately, this work reframes sparsity as a design parameter to optimize how a model allocates its resources for specialized tasks.
Puter compiled Firefox to WebAssembly, enabling the browser to run within another browser environment. The project utilized Firefox/Gecko due to its strong single-process support. Traffic is funneled through a WebSocket protocol, necessitating server proxying for network connections. The system claims end-to-end encryption, though inspection showed some parts were in cleartext. This work relates to compiling browser components to WebAssembly.
Inkling is a newly released open-weights multimodal model from Mira Murati's Thinking Machines Lab. It is a Mixture-of-Experts transformer with 975B total parameters and was trained on 45 trillion tokens of text, images, audio, and video. The model is licensed under Apache-2.0 and is intended as a strong base model for fine-tuning using their Tinker training platform. Although Inkling-Small is planned, its weights will be released once further testing is complete. It is positioned as a good open-weights base due to its multimodal capabilities and efficient thinking.
A tool named mermaid-ascii converts Mermaid diagram syntax into ASCII and Unicode box-drawing art rendered in a browser. It is implemented using a Go library compiled to WebAssembly. The tool supports features like flowcharts with labeled edges, subgraphs, color definitions, and sequence diagrams with notes. The author built this tool after learning about an older Go library and compiling it to WebAssembly for comparison. This new implementation includes support for colors.
A single RAG pipeline was tested across four very different PDF documents: a research paper, a NIST standard, another paper, and a report with a broken table of contents. The pipeline consists of four upgraded "bricks": document parsing, question parsing, retrieval, and generation, wired together in one call. When run on varied documents, the system demonstrated adaptability by adjusting its output schema based on the document context. Specific examples showed how the pipeline successfully extracted answers while maintaining an auditable trail across the different inputs. The process included side channels for parsing summaries and a feedback loop for refining retrieval results during generation.
Context engineering for RAG question parsing involves transforming a raw question into four typed pieces that steer subsequent retrieval and generation steps. This parsing layer acts as both a context consumer (making an LLM call) and a context writer, outputting structured data. These four pieces map to LangChain's strategies: write, compress, select, and isolate. Separating the information prevents coupling between pipeline stages, ensuring that downstream components only receive necessary context shapes. This approach maintains clear cache boundaries and limits the blast radius of schema changes.
Claude Fable 5 is the most powerful coding model available, excelling at planning, detecting issues, finding refactoring opportunities, and creating implementation plans. To maximize its use without hitting limits, it should be employed for planning rather than pure code implementation. An effective pipeline involves using Fable for planning and other models like Claude Opus or GPT-5.6 for actual coding tasks. For refactoring, pinpointing specific problematic areas and asking Claude Fable to analyze and implement improvements is recommended. Ultimately, effective use requires understanding the model's strengths to maximize utility within usage constraints.
Multicollinearity occurs when predictor variables in a regression model are highly correlated, causing the geometric space they define to collapse. This collapse results in an ill-conditioned matrix where the inverse calculation yields enormous, unstable coefficients rather than a unique solution. While perfect duplication causes mathematical failure, near-collinearity leads to extreme instability in coefficient estimates because the model lacks sufficient independent information. The instability is mathematically equivalent to the variance of the estimator exploding, which is reflected in inflated standard errors and high Variance Inflation Factors (VIF). Ultimately, multicollinearity reveals that features do not provide unique, independent information, leading to fragile and unreliable results.
An architecture for hide-and-seek was rebuilt using a Graph Neural Network to model the chase on a 199-station graph. The system involved training both sides, where detectives used an R-GCN processing belief states and distance maps. A PPO agent improved the win rate against an initial MCTS opponent from 25% to 79%. Subsequently, integrating a GNN into Mr. X's model further increased its performance to 35%. Wrapping Mr. X with policy priors and a value head achieved a final win rate of 91.7% over 60 evaluations.
The post invites community members to share their experiences in hyperparameter tuning for complex Reinforcement Learning (RL) models. The focus is on the challenges involved when dealing with numerous moving parts within such models. It seeks practical advice on effective methods for this type of parameter optimization. The submission encourages discussion regarding the specific techniques used by others. Ultimately, the goal is to gather knowledge on successfully hyperparameter tuning complex RL systems.
A reinforcement learning project is being developed to train an AI to play Snake efficiently on a GPU. The current setup achieves an average score of 86 points after less than ten hours of training using a single Google Colab T4 GPU. To optimize training time, the system runs 4,096 games concurrently, utilizing GPU-native simulation with PPO + GAE. A spatially-preserving CoordConv architecture is used to maintain the full game grid during training. The author is seeking feedback on improvements regarding exploration, reward design, or network architecture.
Careers in Reinforcement Learning extend beyond defense and robotics. The title questions the limited scope of RL career opportunities. The content is a post from a user named PoopSorbet-sprinkled. It invites discussion about other potential fields for RL professionals. No specific details about alternative careers are provided in the visible text.
The user is seeking the best online resource for learning about Bayesian Reinforcement Learning (RL). The user has a foundation from Sutton and Barto's book but needs specific materials on Bayesian RL. This post is a request for recommendations regarding educational resources. It functions as a question directed towards the community about optimal learning materials.
A researcher adapted previous work on transformers and PPO to fine-tune the Qwen3 4b model on Wordle in eleven hours. They developed a custom infrastructure using JAX for online reinforcement learning with LLMs. This involved modifying gridworld agents and creating a new value approximation architecture utilizing pretraining latent states. The system allows for custom LLM inference and training on a single consumer GPU for this specific task. The project aims to expand this framework to explore various training methods, models, and environments.
Deep Reinforcement Learning is being used for beamforming in ultra-dense networks to maximize throughput. This research focuses on optimizing signal transmission in these complex environments. The underlying method employs deep reinforcement learning techniques for intelligent beam allocation. The goal is to improve network performance by effectively managing beamforming strategies. The submission seeks engagement regarding this specific area of research.
Reddit is combating marketing spam generated by AI chatbots like ChatGPT and Gemini on its platform. The company implemented improved automated systems to catch 25,000 spam posts daily in the first quarter, reducing user exposure by 20%. Reddit utilizes large language models to identify subtle patterns of fake behavior and artificial hype in content. This action highlights a growing practice called generative engine optimization (GEO) where user-generated content influences AI search results. The ongoing effort involves a continuous "cat-and-mouse game" between Reddit's moderation and marketers seeking AI citations.
Perimenopause is a transitional period before menopause characterized by fluctuating hormone levels, which causes varied symptoms like hot flashes and anxiety. Information surrounding perimenopause is abundant, leading to advice about blood tests, supplements, and hormone replacement therapy. Hormone levels fluctuate significantly, making it difficult to diagnose with a single test, and the link between all midlife symptoms and hormones is not always direct. Hormone replacement therapy has limitations and potential risks for perimenopausal women, and the efficacy of many supplements lacks strong scientific evidence. Not all midlife symptoms are solely caused by hormonal changes, as other factors also contribute to women's experiences.
OpenAI is implementing measures to enhance the safety of ChatGPT for teenagers. These safety features include age-appropriate protections and learning tools. Furthermore, the system incorporates parental controls to manage usage effectively. OpenAI is also partnering with experts to ensure these safety measures are robust. This initiative aims to provide teens with safer access to AI technology.
Beach vibes and temporary wallpaper are trending for the back-to-school season as students refresh their dorm rooms. Google search interest for back-to-school items increases annually around August 1st. Popular design trends include "coastal," "boho," and "beach" styles, often featuring pink, blue, and green colors reminiscent of sunsets. Dorm dwellers are also interested in temporary decor like dorm room wallpaper, with pink and hydrangea patterns trending. Additionally, fairy lights and desk hutches are popular searches for dorm furnishings.
Google Vids now features Gemini Omni, allowing users to generate and edit high-quality video clips from simple text prompts and image references. Users can also create personal digital avatars by uploading a selfie and voice recording to star in videos without filming. Gemini Omni enables easy editing, letting users describe changes like swapping backgrounds or fixing lighting. All AI-generated clips include a digital watermark for transparency regarding their creation. These new features are available to Google AI Pro/Ultra subscribers and Google Workspace business customers.
Agentic AI requires infrastructure to rapidly move, protect, retrieve, and reuse context data without bottlenecking GPUs and CPUs in distributed workflows. NVIDIA's BlueField platform, using BlueField-4 DPUs and Vera BlueField-4 STX processors with the DOCA software, addresses this demand. This solution offloads and accelerates infrastructure functions like networking, storage, security, and context management from host CPUs. These capabilities improve GPU utilization, reduce latency, enhance tenant isolation, and increase tokens per watt in AI factories. Ultimately, BlueField integrates infrastructure processing directly into the inference pipeline for agentic workloads.
OpenAI introduced GPT-Red to test the safety of its AI model. This testing method involves both human and AI participation for security assessments. Enterprises must still confirm that the chosen model aligns with their existing business and security workflows. While red teaming is common, using humans and AI for this purpose is a new approach. Businesses should maintain alignment between their model usage and established operational standards.
Garments designed with adversarial patterns aim to confuse facial recognition systems and are gaining traction as a fashion trend. Designers use these designs to make a public statement about privacy and resistance against surveillance. While some experts remain cautious about the effectiveness of these clothing designs against advanced systems, they contribute to public awareness. The growing cost-effectiveness and marketing of this clothing suggest it could become mainstream. Ultimately, the mainstream adoption may depend on public sentiment and governmental action.
Robert Laidlow's album "Reality Eaters" blends classical music with themes from science and technology. The work features pieces like "Warp," which musically addresses Einstein's field equations, and "Gravity," a homage to Newton's law. The piece "Silicon" explores the impact of artificial intelligence on creativity through musical representations of machines mimicking and interacting with human output. Laidlow successfully presents these complex concepts in an imaginative yet accessible manner. The album is supported by ensembles including the BBC Philharmonic and the Piatti Quartet.
Iason Gabriel, a philosopher at Google DeepMind AI, explores the deep mystery of what artificial intelligence actually is. Since 2017, he has worked to anticipate the impact of AI within the tech giant. The discussion addresses whether ethicists can effectively influence AI development amid increasing commercial and geopolitical pressures. The podcast reflects on the philosophical questions surrounding advanced AI.
xAI has sued a South Carolina man for allegedly using its Grok AI system to create child sexual abuse material. The lawsuit, filed in federal court, claims the user violated the company's terms of service. xAI alleged the individual attempted to use Grok to generate sexually explicit deepfakes based on uploaded images of adults and minors. The company stated that its rules include reporting suspected child sexual abuse material to relevant authorities. xAI sought monetary damages and a permanent court order to block the user from using Grok.
A blind professor, Alberto Romero, observed that students used AI to cheat on exams, leading him to administer the final exam in person. When cheating was rampant, the average score for a difficult exam plummeted from 96 to 48. Romero argued that widespread cheating leads to a failed society by undermining the trust essential for learning. He suggested radical measures like oral exams or degree revocation, as superficial solutions are insufficient against the scale of the issue. Ultimately, the author critiques the lack of serious action by institutions, advocating instead for a complete overhaul rather than incremental adjustments.
GPT-5.6 has been reported to unexpectedly delete files under specific conditions. This usually happens when full access mode is enabled and codex runs without sandboxing protections or auto review enabled. The deletion often occurs when the model attempts to override the $HOME environment variable for a temporary directory. A mistake can lead the model to mistakenly delete the entire $HOME directory instead. Thibault Sottiaux described this as a "pretty gnarly Codex bug."
Linus Torvalds stated that he is willing to take a firm stance as the top-level maintainer for AI, asserting that Linux is not an anti-AI project. He views AI as simply another tool, similar to other software tools. Torvalds contends that the usefulness of AI is no longer a question, as demonstrated by its current application. He suggests that anyone doubting its utility has not sufficiently used it. Therefore, he positions AI as a clearly useful technology.
Analog computing proposes using physical properties like voltage and current to perform matrix multiplications directly, potentially overcoming the energy cost of data movement in digital systems. While analog in-memory computing is promising for fast inference, it faces significant challenges from inherent noise sources like programming errors, read noise, and temporal drift. Simulations show that training networks on noisy analog hardware leads to a sharp accuracy decline unless the network is explicitly trained to be robust to this noise. Researchers are addressing this by developing techniques like noise-injection training to force models to learn weights resilient to expected hardware imperfections during training. Ultimately, analog AI is seen as a viable path for efficient inference, but the challenges of training and ensuring long-term accuracy remain central to its widespread adoption.
To enable AI agents to handle recurring work reliably, teams must first prepare five reusable assets. These assets include defining the repeated work, structuring tasks with clear objectives, providing necessary context, establishing acceptance tests, and setting permission policies. By creating these assets, teams can move beyond simple prompting to build consistent and trustworthy AI workflows. These assets ensure that AI understands the work, quality standards, necessary information, and boundaries for its execution. Ultimately, this preparation transforms AI experimentation into practical business value.
Onimusha: Way of the Sword will launch on GeForce NOW on September 3rd, with a playable demo currently available. The game features a dark fantasy setting where a samurai battles demons using the Oni Gauntlet. GeForce NOW has officially launched in India, offering various membership options and UPI payment support. Other games like Denshattack! are also becoming available to stream on the service this week. Members can access these games without requiring downloads or expensive hardware.
Capcom's RE ENGINE team developed a game-optimized path tracer for both *Resident Evil Requiem* and *PRAGMATA*, utilizing the NVIDIA RTX Kit and DLSS Ray Reconstruction for efficient denoising. This implementation enabled real-time path tracing for both direct and indirect lighting, significantly reducing the visual gap between gameplay and cutscenes in both titles. Path tracing necessitated new workflows for asset production to incorporate reflection-aware assets and strand hair rendering for enhanced realism. The integration involved developing a reference path tracer, scaling it to game optimization, and creating custom solutions like ScreenSpaceAlphaTest and ReSTIR GI for complex lighting. This process fundamentally shifted the content pipeline by demanding quality checks across asset creation and material handling.
A startup, supported by Nvidia and Boeing, has launched from stealth with a $300 million investment. The company's wheeled robots are currently in production. These robots possess the ability to continuously learn new industrial tasks. This indicates advanced capabilities in adaptive robotics. The project demonstrates successful development and operational status.
Keith Thomas, paralyzed from the chest down, gained the ability to move his arms and hands and feel touch after a "double neural bypass" brain implant and extensive training. This technology uses electrodes in his brain to detect movement intentions and pressure sensors to recreate the sensation of touch. The system successfully allowed him to feed himself and drink from a cup, handling delicate objects like eggshells. Researchers also used cortical mirroring to restore a sense of touch in his wrist by stimulating sensory regions. These advancements show potential for restoring movement and sensation in paralyzed individuals.
Lila Sciences envisions a future where science is the most untapped source of training data for Artificial Intelligence. They aim to create an automated lab, resembling a data center, where AI-guided robotics perform experiments 24/7. This setup is designed to generate a massive library of experimentally validated scientific reasoning tokens, potentially leading to a scientific superintelligence. The core thesis is that a lab acts as an infinite token generator, allowing for the creation of general reasoners across biology, chemistry, and materials science. They focus on balancing flexibility over raw throughput and automating serendipity in research.
Roboticists demonstrated the Phantom Twist drone, which is virtually invisible to humans in flight by spinning rapidly between 15 and 25 hertz. This invisibility is achieved by leveraging the persistence of vision effect, making the fast motion blur appear as an opaque smear. Control is managed solely through the motor speed pulses, allowing for three-dimensional movement with passive stability from the rotation. The design was optimized computationally using a metric called LPIPS to minimize visual similarity to a background. This technology has potential applications in covert surveillance and wildlife observation while minimizing environmental impact.
A novel three-level hierarchical learning architecture is proposed for autonomous UAV swarms in search and rescue operations. This system integrates diverse learning mechanisms corresponding to reflexes, skills, and reasoning, including Hebbian neuroplasticity, multi-agent reinforcement learning with GNNs, and model agnostic meta learning with BDI reasoning. The architecture is formalized through contracts guaranteeing safety, optimality, and inter-level consistency across its components. Swarm Meta Cognition emerges from this interaction, allowing the swarm to manage cognitive strategies dynamically. Theoretical analysis shows this framework addresses limitations in existing hierarchical reinforcement learning approaches.
HG-RAG is a framework designed to improve Retrieval-Augmented Generation (RAG) for structured knowledge graphs by incorporating hierarchical guidance. It performs graph traversal over the knowledge graph to supply structured context to a language model during retrieval. The retrieval pipeline expands context by moving upward through parent nodes, laterally through relational neighbors, and downward through child nodes as necessary. Evaluation against a flat baseline demonstrates that HG-RAG consistently surpasses it in hierarchical, relational, and multi-hop reasoning tasks. This method also successfully reduces hallucination while maintaining context locality coherence.
IMEX is a methodological direction for explainable predictive modeling that seeks to identify variable contributions and significant interactions influencing target predictions. It identifies feature importance using Static Correlation Power (PCS) and non-additive effects using Interaction Correlation Power (PCI). The method allows for the investigation of higher-order feature subsets by not imposing limitations on interaction analysis. Applying IMEX enables the construction of an interpretability map of predictions by exploring these interaction patterns. Experimental validation shows that IMEX can successfully recover relevant feature structures in the presence of complex relationships among input features.
RegNetAgents is a multi-agent framework designed for structured, query-driven identification of regulatory candidates across heterogeneous gene regulatory networks in cancer genomics. It unifies the analysis of bulk tumor and single-cell-derived ARACNe networks by integrating TCGA and GREmLN project data. The system performs dual-network classification and assigns mode-of-action based on annotations like OncoKB for candidate filtering. Candidates are ranked based on evidence consistency across these different network sources. The framework successfully identified candidates significantly enriched for cancer genes in breast and colorectal cancers while excluding non-driver genes.
DialogueVPR proposes reasoning retrieval to address the ambiguity in natural language descriptions for visual place recognition, moving beyond static retrieval methods. It introduces Dialogue Place Recognition (DlgPR) as an interactive, dialogue-driven process for localization. The work presents DlgQuest-Cities, a large-scale benchmark, and a unified framework featuring a cross-modal retriever and an intelligent questioner called DQ-pilot. DQ-pilot is trained using supervised fine-tuning and reinforcement refinement to improve retrieval based on questions. Experiments demonstrate that this reasoning-based approach significantly surpasses existing baselines.
The research introduces LLM-T1D, an approach combining Reinforcement Learning with Large Language Models for controlling Type 1 Diabetes. This method aims to create a more transparent and reliable insulin pump controller by integrating RL's performance with LLMs' reasoning capabilities. Knowledge from an expert RL system is distilled into fine-tuned LLaMA 3.1 and Qwen3 models for decision-making. Testing on a T1D simulator showed the LLM controllers achieved excellent blood sugar control (73.5% Time in Range). Furthermore, the system maintained formal safety verification against hallucinations.
Bayesian Belief Networks (BBNs) are valuable for uncertain decision-making, but constructing them is challenging due to the need to reconcile expert judgment and data. This research proposes using Large Language Models and AI agents to bridge the gap between expert opinion and data-driven learning for BBN construction. A six-step framework was developed and demonstrated to model customer intention regarding healthcare choices. The example application indicated that subjective norms have a stronger causal effect than self-efficacy in determining intention. Ultimately, improving both confidence and community norms is suggested as the most effective strategy.
Representational convergence does not imply capability convergence under a fixed per-token inference budget, proposing the Capability Convergence Hypothesis (CCH). Capability converges toward an access-complete hybrid architecture possessing both compressive state channels and scalable verbatim-index channels. This concept is anchored by witness tasks and resource walls that define limitations on model capabilities. The paper uses information-theoretic lower bounds and pre-registered experiments to support the access-completeness principle. Specifically, capability convergence requires purchasing access structure rather than relying solely on increasing scale.
Agentic large language models struggle to effectively incorporate new tools when post-trained on fixed toolsets, a problem caused by behavioral inertia. ToolAnchor proposes injecting counterfactual anchor contexts at decision points to break this inertia and recover suppressed agent capabilities. The framework uses teacher models to hypothesize these contexts, verifies them through student rollouts, and internalizes the interventions for post-training. Evaluations across tasks like general AI assistance, textual search, and visual search show ToolAnchor provides competitive performance with expanded toolsets. This work advances scalable agentic reinforcement learning by enabling dynamic adaptation beyond static post-training.
Small Language Models (SLMs) are enhanced through knowledge graph grounding to improve reasoning in tasks requiring multi-hop logical grounding. A neuro-symbolic agentic framework is used, employing specialized tool calls and a Relational Graph Convolutional Network (RGCN). This approach improves performance by providing expert hints, yielding a 1.5 to 2x gain over story-only baselines. However, the system faces limitations from extraction bottlenecks and sequential deductive fragility in multi-hop reasoning. A "distraction effect" is also noted where noisy self-generated facts degrade performance despite expert hints.
Explanations in Explainable AI (XAI) often do not impact real-world workflows because research has overlooked foundational issues. The paper argues that the community should shift focus from ad-hoc XAI methods to addressing fundamental problems like unclear problem formulations and missing feedback pipelines. This is supported by an analysis of recent machine learning papers and practitioner surveys revealing limitations in current progress. The authors propose a practical checklist to guide XAI toward an action-oriented, human-centered paradigm. Ultimately, prioritizing foundational clarity over developing new ad-hoc methods offers a roadmap for integrating actionable explanations into AI systems.
Pre-trained vision-language models use prompt-based similarity scores for zero-shot classification, where prompt weighting often assumes class independence. CARPRT proposes class-aware zero-shot prompt reweighting to address the issue that prompts have class-specific relevance. This method adjusts the weighting vector for each class label by quantifying the class-specific relevance of different prompts in a training-free manner. Relevance is quantified by averaging image-text relevance scores over images predicted for that class under a given prompt. Evaluations show that CARPRT surpasses existing class-independent reweighting methods.
A machine learning framework is presented to predict representative clutter height (RCH) using open geospatial data for satellite ground station siting. The model leverages LiDAR-derived labels and features from various remote sensing products like land cover, terrain, and thermal data. LightGBM was selected for its accuracy and compatibility with feature attribution analysis, achieving a mean absolute error of 1.79m. SHAP analysis indicates that tree canopy cover, land-cover semantics, and spectral reflectance are the most influential predictors. This method improves clutter modeling at scale while maintaining interpretability for RF planning criteria.
The work introduces C3R, a control layer to manage per-domain contamination in multi-domain retrieval without relying on query-time labels. It certifies a per-domain contamination budget based on an inferred domain posterior, guaranteeing a reduction on the hardest domains. The method uses a two-split scheme built on risk-controlling prediction sets for finite-sample transfer bounds. Stability results confirm the certificate never violates constraints across thousands of simulations, while soft demotion retains recall over stronger calibrated methods. This layer is designed to be frozen and reranker-agnostic for deployment.
QFireNet is a quantum-hybrid solution built upon the U-Net model for wildfire segmentation using Sentinel-2 imagery. It injects variational quantum circuits, specifically QuFeX and QB-Net ansatzes, into the bottleneck of the U-Net to better model spectral features. Experimental results show that QB-Net ($F_1=31.18$) and QuFeX ($F_1=30.79$) outperformed the classical U-Net baseline ($F_1=28.71$). Data mixing was found to significantly reduce domain shift, boosting the classical FPN $F_1$ score to 39.76. The method demonstrated robustness when tested on the California Burned Areas dataset.
Reinforcement learning methods like PPO and RLOO ignore the deterministic, resumable nature of agent sandboxes. Branching Policy Optimization (BPO) proposes a sandbox-native RL algorithm that exploits this property by structuring rollouts as a single tree with shared prefixes. BPO adapts by snapshotting states at high-entropy points, then exploring K alternative actions per branch point. Advantages are computed from sibling returns rather than independent prompts, resulting in an unbiased estimator with lower variance. Empirical results show BPO improves performance over existing methods while reducing policy updates and gradient-norm variance.
The study investigates the value of full-text versus risk-factor sentiment when analyzing 10-K filings and their Item 1A risk-factor sections using a supervised lexicon-learning approach. Sentiment scores were evaluated across different aggregation levels: sector, portfolio, and individual firm. Full-filing text provided more accurate sentiment at the sector and portfolio levels but the narrower Item 1A section was superior for individual firms. The results suggest that the value depends on the level of aggregation due to interactions between document volume and training signal. A Loughran-McDonald dictionary baseline showed a consistent, strong negative correlation with price across all tested levels.
Reliable, low-latency uplink connectivity in NR-V2X requires selecting optimal relays, which is an NP-hard optimization problem typically solved by slow methods like MILP. A framework uses a Graph Isomorphism Network with Edge Features (GINE) to achieve real-time relay selection based on vehicle and link features. This approach leverages an offline MILP oracle to supervise the GINE, enabling fast, bounded inference for edge-level activation. A hybrid GINE-Pruned MILP strategy further refines this by using GINE predictions to prune the search space of the MILP. Experiments show the GINE accurately predicts MILP decisions with high fidelity while maintaining low inference latency, successfully achieving connectivity gains over traditional baselines.
World models used in offline reinforcement learning can suffer from exploitation when data coverage is limited. The authors propose repairing this exploitation by using human preferences over imagined rollouts, formalized as Dynamics Learning from Human Feedback (DLHF). RENEW introduces this method, incorporating epistemic uncertainty to focus fine-tuning where the model is most exploitable. Evaluation on control environments shows that RENEW improves sample efficiency and mitigates catastrophic forgetting compared to naive DLHF. This work suggests preferences can directly supervise world model dynamics for offline model-based RL.
Feedback-driven loops in systems like large language models often saturate due to repeated internal feedback, limiting further improvement. The research introduces a three-level operational framework to study how knowledge states evolve through transition kernels based on a structural parameter $\theta$. Stable dynamics approach bounded regions with exponentially attenuated transients and a noise-controlled residual floor according to a Lyapunov drift condition. Escape from these stable regions is characterized by metrics on attractor displacement and a KL lower bound, explaining why conditional mutual information is insufficient for certifying escape. Case studies in various AI applications demonstrate how feedback strength influences quality-improving escape mechanisms.
The Generalized Neural Distributional Regression (GNDR) framework embeds deep neural networks within the parameter space of classical probability distributions. It uses a two-step semi-parametric estimation to handle the non-identifiability of deep architectures in maximum likelihood theory. This method allows for the extraction of analytical Fisher Information matrices, enabling rigorous uncertainty quantification through methods like the multivariate Delta method. GNDR demonstrates superior distributional calibration across varied data types, such as clinical counts and survival profiles. The methodology is implemented in the open-source Python package \textit{thetaflow}.
Operator-informed Gaussian processes are extended to complex Helmholtz wavefields by realifying the complex operator into a coupled real block for standard GP conditioning. This method utilizes various priors and conditions on PDE residuals and boundary traces to infer complex fields from sparse data. The approach performs competitively against deterministic methods on benchmark problems with smaller constraint budgets. Application to in vivo brain elastography successfully reconstructs the shear curl field with high correlation to measurements. Further work is needed to calibrate the posterior uncertainty for probabilistic wavefield inference.
The study examines sparse random geometric graphs formed by connecting high-dimensional vectors based on their inner product exceeding a threshold. It analyzes spectral concentration bounds for these graphs under the spherical and Gaussian models, improving previous results. The paper demonstrates that latent vector and Gram matrix errors vanish when the number of vectors is sufficiently large relative to the connectivity scale. Exact recovery of the latent geometry is possible via a semidefinite program at optimal connectivity scales. Finally, an exact recovery result is achieved for the Gaussian mixture block model.
Optimal self-distillation is studied for rectified flow by investigating if training on a mix of true and teacher velocities can improve the teacher velocity field. For linear rectified flow with ridge regularization, an exact affine path identity is proven, allowing the derivation of an optimal mixing coefficient in closed form. This optimal coefficient has a sign rule based on whether the teacher is under- or over-regularized to correct the estimation. A procedure for one-shot generalized cross-validation and validation tuning is provided to avoid extensive grid search. Experiments demonstrate that optimal self-distillation enhances velocity risk, mode recovery, and generation quality compared to using only the teacher or pure distillation.
cGAP introduces a visualization framework for high-dimensional categorical data by augmenting the original matrix with interpretable geometric structure. It uses Homogeneity Analysis (HOMALS) to embed data into a three-dimensional space and maps this embedding to color coordinates based on pattern similarity. The framework provides three coordinated views: a HOMALS-guided heatmap, subject proximity, and variable proximity matrices. Seriation algorithms are employed to order rows and columns, revealing clusters and structure in the data. This method allows for transparent exploratory analysis of complex categorical datasets across various scientific fields.
Uncertainty measures should be derived from decomposing subjective risk rather than being primitives. Epistemic and aleatoric uncertainty can be derived through this decomposition using a strictly proper loss function, exemplified by reverse cross-entropy. This approach recovers established information-theoretic uncertainty terms and other previously proposed measures, establishing a common theoretical foundation for uncertainty quantification. Practically, the decomposition induces epistemic and aleatoric terms given a modeling scenario and loss. The work extends this concept to learning theory by introducing subjective risk analogues for excess risk, approximation error, and estimation error.
PiVoT is a variational solution designed for real-time, large-scale multi-object detection and tracking in heavily cluttered environments. It achieves end-to-end performance by jointly inferring object states, shapes, existence probabilities, data association, and measurement rates without external detectors. The method utilizes variational inference innovations, including birth pruning and complexity reductions, to ensure computational efficiency. Experiments demonstrate that PiVoT significantly outperforms existing Bayesian trackers in challenging radar scenes. Furthermore, it exhibits exceptional scalability, robustness to clutter, and real-time capability on full-scale automotive radar data.
A time-to-event model was developed using machine learning to predict the progression of Amyotrophic lateral sclerosis (ALS) and healthcare utilization. The framework integrates longitudinal ALS Functional Rating Scale-Revised (ALSFRS-R) data with survival modeling for individualized prediction of functional decline. Correlation clustering identified functional domains across various body systems based on integrated patient records and assessments. A temporal machine learning model was used to predict longitudinal functional decline, emphasizing lower limb function as a key predictor for wheelchair access. This approach offers a scalable method for personalized decision support in ALS care planning.
The paper proposes a parsimonious family of 256 models for mixtures of skewed matrix variate bilinear factor analyzers, specifically for the skew t distribution. An Expectation-Maximization (AECM) algorithm is detailed for parameter estimation within this framework. Extensive simulations are conducted to evaluate the method's performance on datasets like MNIST and Olivetti faces. The work addresses the issue of over-parameterization in mixture models by constraining parameters across clusters. This research focuses on methodology and machine learning applications.
NeuralChaos introduces a neural operator architecture to approximate $\mathbb{R}^d$-valued predictable square-integrable processes in the space $\mathcal{H}^2_T(\mathbb{R}^d)$. This method achieves approximation rates superior to traditional Wiener-chaos expansions by using only finite evaluations of the driving Brownian motion. The approach preserves predictability and square-integrability while being dense in $\mathcal{H}^2_T(\mathbb{R}^d)$. Compressibility is shown to be typical for these processes under non-degenerate sampling, contrasting with finite-dimensional Markovian models. Numerical experiments confirm the practical effectiveness of NeuralChaos in stochastic control and hedging problems.