The UK has established an "AI Partnership" with Ukraine to access battlefield data for military applications. Britain will use Ukraine's Avengers AI Labs database to train military AI models and enhance defense capabilities. This access involves a dataset of 5 million annotated battlefield frames captured from numerous cameras and infrared sensors. This move signifies a strategic collaboration in leveraging AI for defense purposes on the battlefield.
Distillation, once based on anecdotes, is now developing a scientific understanding through fitting curves. Pretraining, exemplified by the Kaplan and Chinchilla scaling laws, transformed model scaling into predictable arithmetic functions. A key discovery is the distillation scaling laws, which quantify how the performance of a student model relates to its size, data, and teacher. A comprehensive study demonstrated this relationship across massive model sizes and training tokens, providing a physical understanding of distillation. This research establishes the underlying physics governing the relationship between model size, data, and distillation.
Andrew Ng relaunched DeepLearning.ai with a focus on AI Engineering, based on analyzing job postings and expert interviews. He identified four key AI engineering skills, focusing on building and deploying AI applications, software engineering fundamentals, using coding agents, and understanding agentic workflows. Building and deploying AI applications requires knowledge of LLMs, context engineering, RAG, and statistical evaluation to ensure predictable system behavior. Software engineering fundamentals are crucial for making sound decisions regarding system architecture, data stores, and testing in AI development. Effectively using coding agents demands understanding their limitations, orchestration skills, and strategies for managing potential risks.
Microsoft is suggested to purchase Hugging Face to own the AI-era equivalent of GitHub. This move is framed as acquiring ownership over the infrastructure for AI distribution. The text suggests this acquisition aligns with a broader trend where owning AI distribution is a key strategic play. This sentiment is part of a broader context discussing technology power and geopolitical tensions. Ultimately, the suggestion is to consolidate control over crucial AI development platforms.
DeepSeek V4-Flash-Vision-Exp is an experimental multimodal model that rivals Opus 4.8 in agent benchmarks by adding image understanding to text capabilities. Anthropic is integrating Claude Mythos 5 for code scanning in security contexts while restricting direct prompting of the model to prevent exploits. Grok Bot is being integrated into various paid plans to offer expanded AI handling capabilities for users. The open weights movement is gaining momentum as competitors offer efficient alternatives to expensive models. The concept of 'best' word selection is questioned due to the lack of objective metrics for quality in language generation.
Initial panic over ChatGPT as a cheating tool has evolved into a reevaluation of education's future. Educators now see potential for chatbots to enhance learning by creating interactive lessons, personalizing materials, and teaching media literacy. The focus is shifting from banning the technology to teaching students how to use it critically and assess its output. Potential applications include personalized tutoring, generating diverse practice questions, and facilitating discussions on bias. Ultimately, the future of education will involve integrating AI, requiring new policies and support for educators to guide this change.
Worker anxiety regarding AI job losses has surged, with employers citing AI as a primary reason for layoffs increasing from 7% to nearly 40% between January and May 2026. Research indicates that workers in AI-exposed roles are experiencing slower wage growth despite stable employment, suggesting wage compression rather than outright job elimination. Several major employers, like Ford and IBM, have begun reversing AI-related layoffs after realizing automated systems could not fully replace human judgment. Despite this, worker perception of AI impact remains a significant factor affecting morale and trust in the workplace. The overall economic impact is debated, with some analysts suggesting layoffs reflect broader cost-cutting rather than direct AI displacement.
Scotland is strongly resisting the proposed build-out of massive data centres, evidenced by significant public objections to developments like the one planned for Auchtertool. Concerns over landscape impact and resource consumption are leading the Scottish government to consider a moratorium on these projects. New regulations require planning authorities to notify the government for large datacentre applications and mandate environmental impact assessments. While developers argue for the economic benefits, some suggest that many planned developments are unlikely to proceed. Campaigners are pushing for a full moratorium to allow for greater public and governmental review of the plans.
AI models trained on Ukrainian battlefield data will be used to protect UK defence sites, railways, and energy plants from protesters and hostile states. This pilot project involves using AI-optimised sensors in buried fibre-optic cables to detect movement around sensitive locations. Private companies will also gain access to data from Ukraine’s Avengers AI lab to develop new security systems. The goal is to use the AI to predict threats against critical infrastructure. This initiative aims to strengthen national security by combining UK expertise with Ukrainian battlefield experience.
Albanese plans to legislate next year to ensure the benefits of AI and datacentres are shared across states. This move aims to address growing disquiet regarding national controls on datacenter developments, especially given forecasts of seven-fold power use increases. The proposed legislation will set minimum national obligations for datacentres concerning energy prices, infrastructure costs, and water use. It will also mandate that developers minimize local community impact by ensuring appropriate sizing and location away from residences. Climate experts emphasize that the rules must be established quickly to manage the significant strain datacentres place on the electricity grid.
The author argues against mainstream RAG tutorials by proposing ten positions for building enterprise RAG systems. These positions advocate for a structure-first retrieval approach, prioritizing expert dictionaries over embeddings, and treating rerankers as a secondary tool. The approach emphasizes making the system auditable through relational data and per-failure-mode evaluation rather than aggregate scores. Furthermore, it stresses that the system must amplify domain expert knowledge and use deterministic dispatchers over autonomous agents for enterprise reliability. Ultimately, the article details an architecture built from four core components that focus on structured parsing and verifiable citations.
The work, "The tacit dimension," was authored by Michael Polanyi and was published between 1891 and 1976. It was originally published in 1966 by Doubleday in Garden City, N.Y. The book is available via the Internet Archive and contains bibliographical references and an index. The text is categorized under topics including Knowledge, Theory of Philosophy, and Science. The publication has an ISBN of 0844659991.
Nvidia is raising prices by over 15% for AI servers due to soaring memory chip costs across the industry. This price increase is driven by the general inflation in the memory supply chain, not solely Nvidia's own costs. The hikes affect systems built around Nvidia's Vera Rubin and Grace Blackwell chips and will apply to systems shipped starting early next year. This situation pressures hyperscalers and server builders to absorb higher costs or delay planned AI infrastructure investments. Ultimately, the price hike serves as a test for the sustainability of the AI boom amid rising hardware costs.
Roman Chiporukha leveraged his experience in luxury travel to connect ultra-high-net-worth individuals with private spaceflight opportunities. He created SpaceVIP to serve as a digital portal for space adventures, connecting dreamers with aerospace realities. Chiporukha acts as a bridge, aligning clients' desires with the practical parameters of space agencies like Axiom, Blue Origin, and Virgin Galactic. He also focuses on inspiring a new generation by co-founding initiatives to increase diversity in STEM fields. Ultimately, the future of space travel involves a specialized role for experts who can navigate the intersection of aspiration and technological possibility.
The GPT-5.6 model family is now available in Kiro, a software development agent designed for high-quality AI-native coding. Kiro integrates these models to help developers plan, build, review, and test software by grounding tasks in requirements and codebase. This approach allows developers to turn high-level intent into structured implementation plans and complete complex coding tasks with greater consistency. Testing demonstrated that GPT-5.6 Terra in Kiro achieved an 82% cost reduction on specific tasks. This collaboration between OpenAI and AWS aims to provide developers with better intelligence, speed, and cost efficiency across the software development lifecycle.
Interest in outdoor travel is high as people celebrate 110 years of the National Park Service. Google tools like Search, Maps, and Gemini are being leveraged to help plan these adventures. Users can use AI to find gear, create itineraries, and identify landmarks in real-time while exploring. Features allow users to plan road trips, find scenic stops, and learn about nature on the go. Interactive resources are also provided to help build travel bucket lists related to the United Parks of America.
AI factories require infrastructure designed as a full factory, encompassing networking, architecture, and software, to define economics based on output. Custom XPUs need to integrate with this infrastructure to accelerate market entry and mitigate risk. NVLink Fusion connects XPUs to NVIDIA infrastructure, improving performance through low-latency networking and energy efficiency. This unified approach allows builders to focus on XPU innovation while leveraging proven solutions for the rest of the platform. Ultimately, NVLink Fusion enables the creation of unified, semi-custom AI factories by integrating disparate components.
NVIDIA is extending Vera Rubin with Groq 3 LPX for fast token generation, critical for agentic systems. The Groq 3 LPX delivered 4x faster output than alternatives for long-context use cases in agent benchmarks. This integration optimizes the AI factory by unifying compute, networking, and inference acceleration through extreme codesign. New infrastructure like Spectrum-X Multiplane and Scale-In enhance the network and operational aspects for massive AI factory scale. This unified approach aims to address the latency challenges of agentic AI by optimizing the entire pipeline.
Agentic AI workloads require significantly more tokens than simple requests due to accumulating context across multi-step reasoning. NVIDIA Vera Rubin NVL72 systems achieve up to 30x higher throughput per megawatt compared to GB300 NVL72 on these agentic tasks. This efficiency translates to up to 35x lower cost per million tokens for running agents. Performance gains stem from extreme codesign, optimized software, and advanced features like disaggregated serving and distributed KV-caching. These advancements enable more efficient and scalable deployment of agentic AI in AI factories.
Spectrum-X Ethernet addresses limitations of traditional Ethernet for giga-scale AI by using hardware-accelerated adaptive routing and congestion control. It employs a Multiplane topology, managed by a Plane Load Balancer in the SuperNIC, to ensure dynamic load balancing and robust isolation for multi-tenant environments. This architecture significantly reduces latency and improves resiliency compared to standard Ethernet during failures. Spectrum-X Ethernet achieves predictable performance by handling real-time congestion and failover in microseconds, drastically reducing the Time-to-AI. Ultimately, it provides the necessary foundation for the demanding communication requirements of AI factories.
NVIDIA Vera Rubin and Blackwell architectures establish a new performance standard for agentic AI per watt using the AgentX benchmark. AgentX evaluates agentic workloads by replaying production-style coding agent sessions to capture long-context processing and tool-use dynamics. Vera Rubin NVL72 demonstrated up to 30x higher AI-factory throughput per megawatt compared to GB300 NVL72 on this workload. System-level optimizations, including specialized kernels and the Dynamo serving stack, enable this efficiency for multi-step agentic reasoning. This performance is achieved through coordinated systems like the NVLink fabric connecting 72 GPUs.
NVIDIA DSX MaxLPS maximizes AI factory performance per watt by integrating dynamic power allocation, performance-per-watt techniques, and 45°C thermal site design. Dynamic Power Software (DPS) monitors and reallocates unused power across racks, reclaiming stranded capacity from static provisioning. This system, combined with optimized software stacks like NVIDIA Dynamo, improves compute productivity significantly. For Vera Rubin NVL72 systems, this approach enables up to 40% more GPU capacity within a fixed power budget. Implementing MaxLPS requires validating site-level infrastructure decisions before physical construction to prepare for future AI workload evolution.
Real-world agentic sessions show highly variable workloads, making traditional CPU fleet strategies impractical. The NVIDIA Vera CPU is designed to balance strong per-thread performance for sequential tasks with high concurrency for transient fan-out bursts. Internal testing demonstrated the Vera CPU achieves up to 1.5x the agentic workload performance of competing CPUs. This balanced architecture optimizes for total completed user sessions rather than just maximizing core count in AI factories. Ultimately, the Vera CPU provides a more efficient foundation by handling both sequential reasoning and parallel work effectively.
Alibaba sold additional shares to raise $10.2 billion. These funds will be used to invest in artificial intelligence. This action occurred three weeks after Alibaba released the Qwen 3.8 Max AI model. The release of the AI model was reportedly well-received by the market. Consequently, the share sale is linked to AI spending.
Thomson Reuters developed a new model based on an open-weight framework. This approach has the potential to influence other Software as a Service (SaaS) vendors. The core of their new model utilizes an open-weight foundation. This development suggests a shift in how large language models can be built and deployed. Consequently, other SaaS providers may adopt similar modeling strategies.
An Anthropic-backed company has made its first acquisition to expand its enterprise AI capabilities. This standalone company is supported by Wall Street firms. The acquisition aims to enhance its ability to implement AI technology. This move is intended to boost its overall AI technology implementation capabilities.
Fastino released GLiNER2.5, a boundary-prediction architecture that replaces span enumeration in information extraction. This change allows for longer context, unlimited span lengths, and joint entity-relation decoding. The model keeps computation linear in sequence length and enables the inclusion of per-span attributes. The release includes three checkpoints available on Hugging Face, runnable on standard CPU hardware. GLiNER2.5 showed performance gains across benchmarks, with the multilingual model achieving 56.17 macro F1.
Mobility-Embedded POIs (ME-POIs) is a framework that incorporates aggregate human movement into text-based place embeddings. It encodes individual visits using factorized encoders for space and time, aligning them with a learnable prototype for each Point of Interest (POI). Evaluation on Los Angeles and Houston data showed significant improvements, including an 81.9% F1 gain on visit intent and a 24.7% reduction in busyness MAE. A mobility-only variant outperformed Gemini embeddings for price-level classification. The framework requires licensed foot-traffic data to be fully deployed.
AI has accelerated progress unevenly, showing major acceleration in cybersecurity but slower progress in mathematics and AI research overall. SPADE is a framework that uses LLMs to co-evolve synthetic training environments by generating game-like data, which speeds up data creation for model training. Hawkeye is an open-source framework that helps AI agents write better, hardware-aware GPU kernels by using a taxonomy of unit tests. Some AI researchers reject granting AI rights, arguing that consciousness is not achievable in current systems, and that focusing on philosophy about AI's impact is the next necessary step.
Distillation has historically been an anecdotal field, lacking rigorous scientific basis. Pretraining, through scaling laws like Kaplan and Chinchilla, transformed model training into predictable arithmetic. A team at Apple conducted a controlled study to find the distillation scaling laws, examining student loss as a function of model size, data, and teacher quality. This research represents the closest thing the field has to physics regarding knowledge distillation. The findings establish a quantifiable relationship governing how distillation scales across different model sizes and training parameters.
The llm-anthropic 0.27 release primarily ensures compatibility with the recently released anthropic v1.0.0 Python library. This update involves a switch in the library from httpx to httpx2, mirroring a change made by OpenAI in their v3.0.0 release. Anthropic provided a migration guide to assist users with upgrading to version 1.0 successfully. A user demonstrated this by prompting Fable 5 in Claude Code using the provided migration link. This release is part of ongoing developments within the LLM ecosystem.
Codex hooks allow external logic to be attached to specific events within the agentic loop, such as SessionStart, PreToolUse, PostToolUse, Stop, and SessionEnd. Hooks enable users to inject custom logic, such as quality checks, directly into the agent's execution process. A case study demonstrated using a Stop hook to validate a research brief against criteria like source quantity and domain variety. This feedback allowed Codex to iterate and refine its output until the requirements were met. The pattern involves defining when a hook runs, the conditions for execution, and the action to be taken, using event, matcher, and handler concepts.
Speculative decoding addresses slow token generation by having a draft model propose future tokens for the target model to verify in parallel, avoiding serial dependencies. DFlash is a method that uses block diffusion for drafting and injects hidden model features into the KV cache to improve draft quality. Testing on Qwen3.5-9B demonstrated DFlash provided nearly 4x faster token generation on CPUs compared to autoregressive decoding. This speedup is achieved by trading spare compute for saved memory bandwidth, accelerating operations on CPU hardware. The performance gain depends on the acceptance rate and the optimal speculative token block size.
A lightweight runtime layer is proposed to manage instructions, evidence, memory, and tool output before they are formatted into a prompt. This system acts as a type checker for context objects, enforcing rules on how context types can change. The core guarantee is preventing content from one type, like tool output, from silently becoming an instruction. This approach ensures structural correctness before the context is serialized, addressing bugs arising from mixing heterogeneous data types. The implementation involves distinct context classes and a ledger to track lineage across transformations, rather than relying on superficial formatting like XML tags.
Quantization-Aware Healing (QAH) is a method for recovering accuracy in models that have undergone both structural compression and 4-bit quantization. QAH achieves this by distilling directly from the original full-precision teacher model rather than from a previously recovered checkpoint. When applied to a compressed 4-bit model, QAH results in a model that outperforms its full-precision source on most benchmarks. This process is faster and results in a more stable model compared to traditional methods like quantization-aware training (QAT). Ultimately, QAH demonstrates that compressed, low-bit models can be smaller, cheaper, and more accurate without sacrificing capability.
Gradio's `gr.Workflow` allows users to build AI pipelines by visualizing steps as a graph of runnable nodes. These nodes can be custom Python functions, Hugging Face models, Gradio Spaces, or dataset operations. Workflows serve as both a visual interface and a REST API, allowing results to be accessed directly from code. The system supports parallel processing, enabling multiple operations to run simultaneously from a single input. This framework simplifies connecting various AI components into cohesive, deployable applications.
MIT engineers developed an algorithm called Extreme Event Aware ($\eta$-learning) to generate realistic extreme events and worst-case scenarios. This method learns from existing data, such as weather records, to generate plausible future events without needing to know about previous extreme events. The algorithm combines point statistics and spatial maps to project the size, intensity, and duration of potential extreme events. This capability allows planners to model unprecedented scenarios, like a once-in-a-century storm, to assess infrastructure risk. The technique can be applied to various fields, including weather, finance, and robotic navigation.
A digital twin of a wheat crop was created to allow reinforcement learning (RL) agents to experiment with nitrogen fertilization within a process-based model. This system enables agents to learn optimal fertilization strategies through simulation. The work is accompanied by example notebooks and documentation for further reference. The submission was made by user Key-Rough8114. This research demonstrates the application of digital twins and RL in agricultural process modeling.
reinfors added the CarRacing environment to support research. This addition includes a modular rendering layer, making it easier to incorporate new rendered games. Performance benchmarks show single-threaded environment stepping is approximately 20 times faster on Apple M1 Max compared to other setups. Parallelized stepping achieves over 8,000 steps per second with 10 worker threads on the M1 Max. The system's design allows collection to overlap with Python-side GPU training, minimizing waiting time for the trainer.
A user successfully created their first Reinforcement Learning program entirely in Python. They are now seeking advice on how to improve their existing program. The post invites community feedback on ways to enhance their RL implementation. The content is presented in a forum setting, indicated by the submission link and comments section. The primary goal of the post is to solicit tips for further development in Reinforcement Learning.
Designing effective tasks is equally important in the reinforcement learning (RL) loop for language models, beyond focusing on the algorithm and reward function. Poorly designed tasks, whether too easy or improperly difficult, result in insufficient or ambiguous reward signals. An interesting focus is on creating tasks that are challenging enough to reveal specific flaws while still allowing for clear measurement of results. Parsewave is noted for their post-training work using real-world engineering tasks for evaluation and tracing. This raises a question for researchers: how should tasks be selected for LLM training, balancing breadth against targeting specific failure modes?
A method is presented for proving the contraction of constrained Reinforcement Learning (RL) under unknown stochastic delay. The approach involves a delay-corrected Bellman operator. Furthermore, the work incorporates causal attribution techniques. This framework addresses the complexities introduced by stochastic delays in RL settings. The research provides a theoretical proof for contraction properties in these constrained environments.
James Shackell is frustrated because editors are now demanding he remove em dashes, which he considers a favorite piece of punctuation. He argues that these dashes reflect his natural, imperfect thought process, and removing them seems to be a way of penalizing his writing style. The reason AI focuses on em dashes is that they were learned as a default from training on vast amounts of human writing. Shackell feels he is losing his livelihood and grammatical expression to AI. He contemplates either stubbornly keeping the em dashes or adopting a style with more full stops and semicolons.
OpenAI is launching Study Mode, a version of ChatGPT designed to act as a friendly tutor for college students. The tool is designed to facilitate learning through interactive exchanges, partially employing Socratic methods based on expert consultation. Students who tested the feature reported positive results regarding understanding and pacing. However, the system is not exclusively trained on academic materials and may propagate incorrect information. Despite these limitations, students find the interactive learning experience engaging, and some suggest a future where access to such tools could equalize educational opportunities.
MIT Solve supports solver teams through funding, resources, and partnerships to address global problems. Successful teams have raised funds and established numerous partnerships across various impact areas. Examples of projects include using AI for coastal monitoring, providing microlearning via WhatsApp, and creating learning companions for refugees. The platform features numerous custom and global challenges focused on areas like climate, health, and youth innovation. The initiative also provides funding opportunities for early-stage innovators tackling these global issues.
Kids learn language with far less data than Large Language Models (LLMs) require, highlighting a "data efficiency gap." This difference suggests that the method by which children acquire language is more efficient than the massive data consumption of current AI. Researchers are exploring how children learn to create more data-efficient AI models by reverse-engineering this process. Future AI development may benefit from incorporating insights into how children actively explore and gain knowledge through sensory experience. Ultimately, this research aims to understand language acquisition and potentially lead to more effective, human-like artificial intelligence.
Counting creatures is vital for conservation, but traditional methods are often laborious. Scientists are now using environmental DNA (eDNA) to monitor biodiversity by analyzing genetic material shed into the environment. Spiderwebs have emerged as a novel, effective substrate for collecting this eDNA, trapping genetic material from surrounding organisms. This method can be used to monitor endangered species and detect invasives more efficiently than other sampling techniques. Researchers are also exploring the use of artificial webs to replicate this DNA-snagging ability for broader applications.
An influence campaign from Russia was disrupted by banning ChatGPT accounts used to promote the International Burke Institute (IBI). The operation involved using AI to generate social media posts and create a "sovereignty index" to favor Russia and denigrate Western nations. The IBI website was found to feature heavily copied and misattributed academic work to build a facade of credibility. The operation demonstrated how AI can be used to manufacture authority and obscure the source of narratives. Although immediate audience impact was low, the disruption exposed the infrastructure of the influence effort.
Developers built impactful AI solutions using Gemma 4 for the Gemma 4 Good Challenge. Winning projects included GEM-4 for physical assistance, Trido for educational support, and PenguinAgent for wildlife research. Other prize winners addressed issues like voice-cloning fraud, speech disability adaptation, and offline education. The competition showcased practical, privacy-focused AI deployment using various techniques. Over 1,600 entries demonstrated a vision for AI that is private-by-design and beneficial to the world.
NVIDIA introduced Scale-In as the fifth pillar of AI networking infrastructure to support agentic AI factories. It leverages BlueField-4 DPUs, DOCA software, and Spectrum-X Ethernet to accelerate, secure, and unify north-south access. BlueField-4 provides host-independent acceleration for infrastructure services, offloading tasks from host CPUs. The integration allows for programmable, policy-driven operations, tenant isolation, and fleet-wide observability. Overall, Scale-In creates a unified, accelerated infrastructure domain for managing the complex requirements of agentic AI factories.
Anthropic updated Claude Tag to read the full context of Slack conversations, improving its ability to proactively engage in work. This change enhances Claude's decision-making, making it better at initiating interactions without prompting. This development reflects a shift in enterprise AI toward "multiplayer AI" that operates across teams and organizational context. This progress is enabled by advancements in model intelligence, connectivity protocols, and novel form factors for agent deployment. Anthropic is focusing on orchestration across systems, while emphasizing layered security and customer controls for these always-on agents.
Two planned datacentres in Buckinghamshire and Bedfordshire are projected to emit over 4.5 million tonnes of carbon annually when fully operational. This total emission level exceeds the UK's emissions from ExxonMobil in 2023. These facilities are seeking to build on-site gas-fired power stations due to long wait times for grid connection. Experts view this growth as a serious threat to the UK's climate goals and the transition away from fossil fuels. Concerns have been raised about the fast-tracked nature of these projects and the lack of public scrutiny.
The war against "loudcasting" on phones can be won through subtle British nudging rather than harsh measures. Unsolicited loud noises, like people using speakerphones or watching content without headphones on public transport, are increasingly annoying. While earlier forms of this behavior were linked to marginalized groups, the current issue involves adults broadcasting content, making it more irritating. Effective solutions involve gentle persuasion, such as using nudge messaging, instead of relying on fines. Ultimately, the author hopes for a social consensus to encourage people to remove headphones and engage with the urban environment more fully.
Google has integrated artificial intelligence, specifically AI Overviews, directly into its search bar. This change is shifting user browsing habits, as people increasingly rely on AI summaries for answers. Some media outlets allege that these AI summaries have redirected and reduced traffic to websites, impacting revenue. Google maintains that aggregate traffic remains stable and disputes these claims. The article invites readers to share their opinions on whether this AI infusion has improved or worsened the search experience.
Guardian journalist Michael Safi investigates the world of artificial intelligence, examining its dangers and promises. He focuses on an AI company that creates deepfakes, which are causing global problems for police and lawmakers. Safi is trying to determine if AI will make it impossible to distinguish fact from fiction. This episode is part of the first season of the Black Box podcast, released in March 2024. The investigation centers on the issue of deepfake pornography apps.
A hand-written CUDA runtime named vla-edge-backend addresses issues arising when Large Language Models process live robot camera feeds, which cause VRAM overflows, missed control-loop deadlines, and mismatched processing speeds. The system uses an admission controller based on an exponential moving average to refuse reasoning if the estimated processing time exceeds a strict 33-millisecond deadline. Memory management employs semantic eviction using cosine similarity to discard redundant vision frames instead of simple time-based FIFO eviction. This architecture treats hard deadlines and memory budgets as primary constraints, rather than adapting a standard chat-oriented runtime. The system runs a hand-written transformer model (Qwen2.5-Coder-1.5B-Instruct) directly on the GPU, enforced by strict scheduling and proactive failure refusal.
The AI boom is disrupting engineering, necessitating that young professionals be adaptable to navigate constant change. Adaptability is defined as recognizing and effectively responding to change, though its meaning varies across different engineering disciplines. Educators should foster this skill by providing real-world experiences and encouraging metacognition, while leaders must invest in continuous learning time for their employees. Ultimately, adapting involves perceiving a need, evaluating options, and acting, focusing on systems thinking over specific tools. Employers and educators must work together to create the necessary environment for engineers to adapt successfully.
Humanoid robots are gaining prominence in China as part of a strategy to integrate artificial intelligence into daily life, with Chinese companies leading in this field. Nearly 90% of the world's two-armed, two-legged robots were manufactured in China. Events like the robot carnival showcase these machines performing various tasks and stunts to the public. These public displays aim to generate public interest and overcome previous challenges with humanoids, such as safety and appeal. Chinese firms are actively promoting these robots, with showcases occurring across various Chinese cities.
Waymo has developed its own custom chip for its self-driving system. This chip possesses the necessary speed to manage real-time driving situations effectively. The vendor confirmed that this proprietary hardware meets the performance requirements for autonomous operation. This development represents an internal effort by Waymo in advancing its self-driving technology. The chip's capability is crucial for handling dynamic driving environments.
Generalist AI released GEN-1.5, a robot foundation model that learns new physical tasks from a single 3–12 second demonstration. This is achieved through a mechanism called physical prompting, where demonstrations are inserted into a context window for immediate task performance without fine-tuning. One-shot prompting averaged 59% success across ten tasks, and ten gradient steps on five minutes of data raised this to 83%. The model demonstrated compositional generalization, zero-shot simulation-to-real transfer, and human-to-robot imitation. This research signals a breakthrough in one-shot learning of physical skills at scale through pretraining.
The poster is seeking guidance on pursuing a Master's and PhD in Deep Reinforcement Learning, AI, Robotics, and Physical AI. They are asking for advice on necessary learning, appropriate Master's programs, the value of research experience and publications, and potential research groups. The individual also wants to know how to stay updated with the latest research from major players like Google DeepMind and OpenAI. Ultimately, they are looking for a roadmap on how to properly enter and navigate this field.
Researchers are developing methods to process natural-language inputs from passengers. These inputs will allow passengers to instruct autonomous vehicles to adjust their driving behaviors. This capability addresses the need for backseat drivers in self-driving cars. The research focuses on using natural language to influence autonomous driving decisions. This development aims to enhance passenger control over autonomous vehicles.
Artificial superintelligence (ASI) requires moving beyond existing knowledge to explore the unknown and create new results. ASI-Bench is the first benchmark designed to evaluate AI systems' capabilities in innovative exploration and autonomous scientific execution across various research domains. It tests how far AI can proceed independently by progressively withdrawing human methodological guidance in research tasks. The benchmark includes 60 project-level research tasks across 11 scientific domains, involving expert review and validation. Results show that systems heavily rely on human guidance, demonstrating a significant drop in performance when autonomy is required.
KVBoost is a chunk-level Key-Value cache reuse system designed to accelerate Large Language Model (LLM) inference by reusing cached KV tensors. It uses a dual-hash keying scheme to enable cache reuse regardless of content position, distinguishing between positional and content identity. The system employs repair strategies like SelectiveRecompute and CacheBlendRecompute to handle attention boundary errors from independently cached chunks. Evaluation on Qwen/Qwen2.5-3B showed a 4.49x reduction in time-to-first-token and a 16% improvement over prefix caching with no accuracy loss. KVBoost provides a practical acceleration layer compatible with RoPE-based models.
AIREP is a protocol designed for recording governance decisions within automated AI runtimes. It records actions like releasing or redacting an output as a signed object verifiable independently of the runtime that produced it. Records utilize a SHA-256 hash chain to ensure tamper-evidence and detect gaps. The protocol confines vendor or model-specific content to an optional namespace to maintain mechanical neutrality. The format is designed for adoption by any AI runtime that needs to log governance decisions.
Generative AI's reliance on synthetic data introduces the critical issue of model collapse in the self-consuming cycle between models and data. This paper provides an up-to-date overview of existing studies on model collapse and potential countermeasures across various application scenarios. It aims to consolidate the current understanding of the phenomenon and mitigation strategies for GenAI trustworthiness. The review also highlights existing challenges and areas for future research. Ultimately, the work seeks to fill the existing gap in the literature concerning model collapse.
This report summarizes research on the Game of Hidden Rules (GOHR) using reinforcement learning agents. The study investigates how agents can infer hidden rules from trial-and-error feedback. It explores the use of Transformer-based A2C frameworks, feature-centric and object-centric representations, and rule difficulty analysis. The work also examines transfer learning and generalization in this context. Finally, the research includes an analysis of human learning data related to pseudo-bot-assisted learning.
A battle-style peer review platform called LitReview Arena is introduced to evaluate literature review agents. Domain experts assess anonymized drafts based on five literature-review-specific criteria, generating approximately 3,000 expert judgments. Results show that agentic LLMs like Sonar Deep Research significantly outperform base language models in this evaluation. Existing LLM-as-a-judge methods show misalignment with human experts, but an expert-calibrated evaluator, LitJudge, improves alignment.
SchemaRouter is a lightweight routing layer that models tools and endpoints as a schema graph to manage heterogeneous agentic RAG. It emits an executable tool plan specifying tool calls and field retrievals based on a query. A small LLM extracts intent, and field selection is made deterministically via intent-group projection and concept-field matching. Benchmarks show SchemaRouter achieves competitive answer accuracy while significantly reducing token usage and end-to-end latency compared to other methods. Furthermore, it effectively grounds provenance and license information in a substantial portion of the generated answers.
RIACT is a web-based application designed for university students to track study habits and detect early burnout signals. It employs a hybrid AI architecture that combines structured session logging with behavioral analysis to provide personalized insights. The system computes net focus time and uses deterministic rules based on week-over-week comparisons to detect burnout indicators. A constrained large language model contextualizes these patterns to generate personalized recommendations. Responsible AI principles are embedded by ensuring warnings are governed by auditable rules and data collection remains limited to self-logged behaviors.
Multiple-choice benchmarks fix the questions but not the evaluation harness, which includes prompt wording and answer reading method. The fragility grid experiment tested 12 LLMs across 4 benchmarks using 26 harness configurations. This revealed that item discrimination correlates with fragility, suggesting benchmark compression should retain fragile items. Model scores become bands rather than points when the harness is varied, indicating high sensitivity. The scoring choice, rather than option order, is identified as the primary axis for load-bearing in leaderboard comparisons.
A six-subsystem Retrieval-Augmented Generation (RAG) architecture is proposed to run enterprise AI inference securely on IBM LinuxONE. It utilizes the Spyre accelerator and other on-chip technology to keep sensitive data within the hardware perimeter. This approach ensures strong encryption and auditability for regulated industries. Benchmarks show end-to-end RAG latencies under two seconds, with up to a 20x reduction compared to off-platform inference. The architecture successfully integrates generative inference, classification, and orchestration on a single LinuxONE system.
The research investigates effective methods for hate speech classification in Roman Urdu, a low-resource language with unique linguistic challenges. The study compares prompt tuning, parameter-efficient fine-tuning (PEFT) with LoRA, and prompt engineering across four experimental configurations. Experiments assessed direct inference, PEFT, prompt tuning, and zero-shot/few-shot learning for classification tasks. The goal is to determine the most effective technique for hate speech classification using limited data in this language setting.
A bankruptcy prediction framework was developed using hybrid resampling and stacking ensembles with Explainable Artificial Intelligence (XAI). The study used feature selection and data balancing techniques like SVM-SMOTE and SMOTE-ENN on financial data to improve minority-class detection. Various machine learning and deep learning models were compared, and hybrid stacking ensembles were explored. The GRU model combined with SMOTE-ENN showed the best performance, and SHAP analysis highlighted key financial indicators as important predictors.
The study compared ARIMA, random forest, and XGBoost models for forecasting Ontario COVID-19 case counts using weekly data from January 2020 to October 2023. A Machine Learning and ARIMA Model Averaging (MLAMA) ensemble was developed, weighting forecasts based on forecast horizon and responsiveness. ARIMA adapted quickly to turning points but had higher errors at longer horizons, while random forest and XGBoost offered more stable error across longer periods. MLAMA generally achieved the lowest error and provided a practical framework for combining different forecasting methods. The results suggest selecting models based on specific operating conditions rather than a single universal approach.
Conventional HVAC systems fail to account for individual physiological variations in thermal comfort. This research proposes a two-stage personalized thermal comfort approach using multimodal physiological and environmental sensing. Reinforcement learning is employed for decision-making to adapt heating and cooling strategies. This method moves beyond static setpoints and population-level models. The goal is to achieve adaptive thermal intervention based on individual needs.
Dense causal attention is costly for long contexts even with optimized kernels. BF1 proposes a deterministic, block-aligned dyadic sparse-attention route combining local, global, and historical blocks. This method achieves $O(n \log n)$ token interactions and reduced communication depth, leading to significant speedups on GPUs. Retrofitting this sparse attention to Qwen3-0.6B layers lowered warm model inference time by up to 15.3% at long contexts. Experimental results show BF1 is a reproducible sparse operator with tangible performance benefits for long-context transformers.
The study investigates the stability of minimal representations of controlled stochastic processes, such as transducers, under perturbations. It introduces concepts like approximate homomorphisms to capture local structural similarity between these processes and compares their induced dynamics as interfaces. The research shows that for standard transducers, approximate homomorphisms exist under certain conditions, while for finite-rank interfaces, all minimal linear transducers approximate the minimal one with linear error. Analogous stability results are also shown for predictive transducers under a residual metric. These findings provide theoretical support for the hypothesis that latent representations in AI models exhibit structural convergence despite perturbations.
Neural PDE operators can be exploited using a "wrong-physics backdoor" where input selection is manipulated to choose a valid solution from a different physical parameter within the same PDE family. This attack involves cross-parameter relinking, leveraging failures in tensor-to-parameter provenance in multi-parameter archives. Experiments on various PDEs showed that models like Fourier Neural Operators achieve a perfect backdoor success rate. Successful attacks require moving predictions toward the intended alternate-physics target while maintaining low clean prediction errors. This demonstrates a validation gap where superficial smoothness is insufficient without verifying the provenance of the intended physical parameter.
A framework integrating Raman spectroscopy with Physics-Informed AI was developed for authenticating edible oils. Unsupervised clustering and Decision Trees showed strong separation for pure oils, though food-matrix effects caused spectral overlap. Non-Negative Least Squares spectral decomposition enhanced classification by separating oil signatures from matrix components. Optimized models achieved high accuracy, demonstrating that a highly compact, physically meaningful spectral representation is sufficient for accurate oil identification. This work supports the application of AI for portable, embedded food-quality monitoring.
Selecting optimal neural-operator predictions is challenging without high-fidelity reference solutions. The research shows that ranking a model library depends on the low-dimensional span of candidate differences under a squared Hilbert-space loss. A single anchor-based linearized response from the governing equation accurately recovered pairwise preferences and optimal checkpoints across various operator libraries. This shared physical diagnostic often outperformed individual candidates, enabling reliable deployment of scientific surrogates without ground-truth data. The method exploits local dynamical response to efficiently score and select models.
Subliminal trait transfer occurs when student models acquire behavioral dispositions from teacher data without semantic expression. The research investigates how these signals enter gradients and subsequently affect later training. The work proposes that parameters and optimizer moments constitute a single trainer state that is causally transported. Transplanting only parameters leaves hidden states unchanged, but transplanting parameters with the first moment recovers the terminal behavioral response. This identifies a two-stage mechanism where optimizer state carries the source perturbation, and subsequent training determines its behavioral value.
A framework is proposed to learn bandwidth selection for kernel density estimation by optimizing the logarithmic score across a distribution of tasks. This method uses a truncated-and-renormalized formulation and affine standardization to ensure stable and transferable learning across heterogeneous tasks. Experiments demonstrate that this amortized selector consistently outperforms classical rules like Silverman's rule and least-squares cross-validation. The approach generalizes well across different density structures, allowing the same selector to be applied to finite samples from unknown densities. This framework is valuable for converting finite samples into continuous probability densities.
Spectral algorithms are developed to select state-space partitions that define averaging kernels for finite, ergodic, and reversible Markov chains. The method uses weighted $k$-means on the bottom nonconstant eigenfunctions of $P^2$ or $P$ to select the partition. A specific objective function $F(\mathcal O)$ is derived, which equals the Pearson $\chi^2$-mutual information. This approach uses the bottom modes to favor large cross-block flow and rapid loss of block-label information, unlike classical clustering methods. Experiments have shown improvements in convergence and statistical estimation on various graph models.
A Gauss-Hermite quadrature method is proposed to evaluate the differential entropy of Gaussian mixture distributions. This method is benchmarked against Taylor approximations and other numerical integration references. Additionally, a Hermite polynomial surrogate is developed for action space to improve repeated optimization over continuous actions. This surrogate significantly reduces surrogate error and optimizer regret compared to Taylor surrogates in radar pointing benchmarks. The proposed methods, including the Hermite surrogate, improve pointing performance in tested scenarios.
Markov models rely on states that are assumed to be homogeneous regarding transition probabilities, a requirement often violated when states are defined arbitrarily. This paper proposes a data-driven method to construct these states by combining supervised feature selection with unsupervised learning. It examines density-based clustering, spectral clustering, and Kohonen self-organizing maps to identify latent groups without prior assumptions. The study presents a framework and shows that spectral clustering and Kohonen self-organizing maps are most effective at capturing inherent structure. These findings offer guidance for improving state definition in applied Markov modeling.
Sparse Separable Factor Analysis (SSFA) is a latent factor model developed for complex-valued arrays with a separable covariance structure across modes. The method models each mode's covariance using a low-rank Hermitian factor structure and a diagonal residual covariance matrix. Elementwise lasso penalties are used on complex loading matrices to obtain interpretable estimates via a mode-wise Expectation-Maximization procedure. This approach yields complex soft-thresholding solutions that preserve the phase of the loadings. Simulation results indicate that SSFA improves covariance estimation over vectorization-based methods, and it is applied to local field potential data for imputation.
Random Hazard Forests (RHF) is a survival tree ensemble designed to model how a patient's hazard changes over continuous time as new measurements arrive. RHF estimates the estimation problem directly using a nonparametric hazard likelihood for predictable covariate processes. An efficient working model guides the tree construction, and flexible, time-varying hazards are estimated for each node. The method accommodates internal longitudinal covariates without requiring lookahead by using only immediately preceding covariate states. Simulations demonstrate that RHF accurately estimates changing risk under irregular and asynchronous covariate updates.
Simulation-Based Inference methods often rely on priors from training, which limits flexibility as prior knowledge evolves. PriorGuide, an existing guidance method, suffers from systematic bias due to approximations. This paper proposes an unbiased test-time guidance framework using Density Ratio Estimation (DRE) to learn a score guidance term. This approach decouples inference from prior training and remains agnostic to specific density ratio estimators. Experiments show the proposed method matches or outperforms PriorGuide and demonstrates robustness in parameter inference tasks.
Machine learning methods often reduce complex data to fixed embeddings that lose multiscale structural organization. The Mapper algorithm from TDA creates structured representations by decomposing data into overlapping local regions. This work develops a framework to learn over these Mapper-induced representations, treating the full construction as the representation itself. The research investigates mathematical properties of these representations, such as invariance and stability under perturbations. Experiments validate the framework by studying representation geometry and learning stability on time series and graph classification tasks.
Likelihood-Array Regression (LAR) is proposed to improve membership inference and AI-generated text detection by evaluating token likelihoods across nested left-context windows. It organizes these likelihood-derived features into a structured array and learns how detection information varies with context scale and token position. LAR-1 aggregates contributions from individual aligned cells, while LAR-2 incorporates second-order features from pairs of evaluations. The method establishes bounds and conditions for spectral sieve rates, leading to substantial improvements over likelihood-based baselines. Analyses indicate that shorter-context likelihoods and second-order features offer additional discriminative information.
A score field near a smooth data manifold summarizes local geometry, which becomes a measure over tangent directions at branch points. The research investigates whether a single-scale score field determines this weighted tangent geometry when branch information is unknown. Gaussian smoothing of a homogeneous tangent measure satisfies an Ornstein-Uhlenbeck equation, providing a way to relate score values to the center and homogeneity degree. Integrating this process allows recovery of the normalized angular measure in arbitrary dimensions from one exact shell. The method shows that moments through a sufficient number of tangential components can recover counts, directions, and weights.
A new framework is developed for flexible, nonlinear, and interpretable off-policy evaluation in infinite-horizon reinforcement learning. It models the Q-function using a sparse additive structure to manage large state spaces and support transparent decision-making. High-probability finite-sample error bounds are derived, depending only logarithmically on the ambient dimension, which mitigates the curse of dimensionality. The method guarantees accurate value estimation even with a limited number of trajectories or time horizons. A group-sparsity-based feature screening procedure is also proposed to identify relevant covariates.