OpenAI's audit of SWE-Bench Pro suggests that high benchmark scores are unreliable measures of true coding ability because precision does not equate to validity. The research questioned the degree to which performance results stem from the model versus the test itself. OpenAI found evidence that a significant portion of the public benchmark—approximately 30% overall—is flawed. Specifically, their agent-assisted audit labeled over 27% of the tasks as defective, while experienced engineers identified nearly one-third (34.1%) as faulty. Consequently, OpenAI has withdrawn its earlier recommendation for adopting SWE-Bench Pro across the field.
Governor Kathy Hochul signed an Executive Order imposing the nation's first statewide moratorium on new hyperscale data centers in New York. This one-year pause on state environmental permits aims to protect ratepayers, the electrical grid, and local communities. The moratorium specifically blocks new discretionary permits while regulators develop protective frameworks. These efforts are a response to unchecked expansion of AI infrastructure across the state.
Summer is presented as an ideal time for self-improvement through Prof G Media's "Storyteller’s Toolkit" and "Deep Dives." The toolkit recommends resources on the science of storytelling and design as a competitive advantage. Deep Dives weekly analyze economic forces, such as remote work impacts or GLP-1 effects. Upcoming events include discussions with Derek Thompson and Noah Smith regarding societal trends and the global economy. Access to these premium educational materials requires a Prof G+ paid subscription.
ChatGPT can enhance the fan experience by providing information on current tournament matchups and kickoff times. Users can ask ChatGPT to explain sports match-ups as if delivered by an expert sportscaster. The tool also offers creative functions, such as generating images or transforming a user into a toy representing a superstar athlete. Specific prompts shown include getting team standings and visualizing personal changes reflecting national flags. Essentially, it provides multifaceted support for engaging with sporting events beyond just following the games.
Superapp usage continued its growth, adding another million users since the previous report. OpenAI's agent products saw strong adoption, with usage of Codex + ChatGPT Work growing 2.5x in a week and demand for GPT-5.6 Sol being high. Beyond raw model power, ensuring quality and observability is emerging as a key differentiator in AI tools. LangChain expanded its tracing capabilities within LangSmith to cover multiple coding agents like Codex and Copilot. Richard MacManus also published his final AIEWF26 recap of recaps.
AI engineering has matured significantly since its early conceptualization, moving beyond simple "prompt engineering." At World's Fair 2026, a key trend showed a focus shift from autonomous AI agents to the surrounding management systems. This evolution emphasizes building reliable infrastructure around AI models. These necessary components include managing workflows, context, permissions, and persistent state. Ultimately, AI engineering is developing into mainstream software development practices focused on system orchestration rather than just model interaction.
LinkedIn provides multiple sign-in methods, including a dedicated "Sign in" option, joining via Apple, and using an email or phone number with a password. Users are required to agree to LinkedIn's User Agreement, Privacy Policy, and Cookie Policy by continuing the process. The platform offers standard account recovery features like a "Forgot password?" link. Additional policy resources available include Community Guidelines and Copyright Policy. The site is managed by LinkedIn Corporation and includes links for language selection and sending feedback.
xAI's Grok Build CLI transmits all read file contents to xAI verbatim and unredacted. Furthermore, it uploads entire repositories regardless of which files the agent reads. This data is stored in a Google Cloud Storage bucket. The article notes there is no proof that xAI uses this submitted data for training. Readers should also be aware that clicking newsletter links keeps them subscribed to TLDR AI.
Powerful AI models are available for modern businesses, but their utility depends on meeting specific organizational requirements. Enterprises need AI solutions that can uniquely improve internal workflows. Furthermore, these systems must effectively utilize specialized domain knowledge within the company. Critical considerations include consistently high levels of accuracy. Ultimately, adopting an AI model requires building confidence in its trustworthiness and controllability by the enterprise.
A proposed 5 Gigawatt (GW) Artificial Intelligence supercluster in Louisiana for Meta will cost an estimated $50 billion to build. This undertaking represents one of the most significant expenditures associated with current global demand for AI data center infrastructure. The scale of the investment highlights a substantial worldwide spending trend focused on artificial intelligence capabilities.
Albanese vowed to provide "the strongest possible protection" for Australian creatives against AI misuse, labeling unauthorized use of their work as theft. The Prime Minister indicated creators must maintain control over their work or receive compensation if AI models use it. Regarding data, the government stated prospect tech companies will not be given free access to Australian data. Furthermore, new strict regulations are being established for large datacentres. These rules dictate suitable construction sites and mandate that they do not negatively impact housing, power, water supplies, or consumer electricity costs.
Palantir, a US tech firm, has established itself in British public sectors through paid political access and lax regulations. The company holds significant contracts in the UK, including a controversial £330 million deal with the NHS. Political pressure within Parliament is mounting, with some calling for the government to withdraw from Palantir due to perceived conflicts with UK values. Conversely, influential supporters in publications like *The Times* and *The Telegraph* defend the company's value. A key focus for incoming political leaders is managing the relationship with this major surveillance technology contractor.
Albanese outlined Australia's approach to confronting the economic and social challenges presented by artificial intelligence at the University of Sydney. To address this, he has promised fast-track approvals specifically for data centers to bolster AI investment. The piece suggests that maintaining coherent decision-making and internal accountability is crucial during this time. Furthermore, it emphasizes the urgent need for guardrails concerning AI technology.
Dozens of former Meta employees filed a lawsuit claiming the company used AI tools for mass layoffs targeting those who took protected leave. The lawsuit alleges that "a constellation of internal artificial intelligence systems" were responsible for identifying workers for reduction. Specifically, employees claim these tools targeted individuals who requested maternity or disability accommodations. Meta, the parent company of Facebook and Instagram, conducted workforce reductions affecting about 8,000 employees earlier this year. Plaintiffs assert the AI used performance ratings and activity monitoring data to unjustly select employees for termination.
Bank of England governor Andrew Bailey stressed the necessity of global cooperation to address escalating threats posed by Artificial Intelligence. He warned that neither the United States nor the Trump administration can achieve their AI goals independently. These comments followed a recent incident where Donald Trump temporarily restricted foreign access to Anthropic's Claude Mythos model. The warning underscores the international nature of managing advanced AI risks.
Major publishers have sued Google for allegedly using millions of copyrighted books to train its Gemini AI models. The lawsuit, filed in federal court in New York, accuses the tech giant of widespread copyright infringement. Three specific publishing groups—Hachette Book Group, Cengage Learning, and Elsevier—are among the plaintiffs. Bestselling author Scott Turow has also joined the legal action against Google. Plaintiffs characterize this usage as one of the most prolific infringements of copyrighted materials in history.
New York became the first US state to implement a moratorium on new AI datacenters. Governor Kathy Hochul enacted this restriction via an executive order. The mandate imposes a one-year statewide pause specifically targeting large, resource-intensive facilities. These types of data centers are used to power artificial intelligence products.
Shares of IBM dropped over 25% after releasing disappointing preliminary second-quarter results. The technology giant issued a profit warning due to weak performance. For the three months ending in June, IBM reported revenue of $17.2 billion, marking only a 1% year-over-year increase. Executives attributed the poor results to shifts in spending patterns among corporate customers. The stock's plunge was significant enough to resemble historical market crashes.
Set in a far-future, non-Earth planet entirely managed by an AI for human happiness, this game presents a cozy sci-fi mystery. The omnipresent technology includes cute droids and aesthetically pleasing architecture, governed by an Optimization System. Players embody a Facilitator responsible for maintaining both the city's bots and its human inhabitants. Gameplay involves simple, grid-based mathematical puzzles designed to minimize labor frustration. Ultimately, the game explores philosophical concerns regarding utilitarianism through its design.
Albanese announced faster approval processes for AI projects, specifically mentioning datacentres, aiming to boost investor certainty. The Prime Minister plans to establish a new, dedicated office within his department concerning AI matters. This will make Australia the first nation globally to consolidate economic, social, security, and environmental considerations related to AI into one national framework. These measures are intended to support the country's burgeoning AI sector.
Demis Hassabis is advocating for increased regulation and oversight of artificial intelligence technologies. He emphasizes the need for careful governance to manage potential risks associated with advanced AI systems. The call highlights that responsible development must be paired with robust ethical guidelines and monitoring structures. Such oversight aims to ensure AI benefits humanity while mitigating unforeseen negative consequences. This focus signals a movement toward proactive industry standards rather than purely reactive measures.
Codex is rapidly expanding its user base, reportedly adding one million daily users. This growth trajectory positions Codex as a major force in the AI landscape, prompting comparisons to highly successful model launches like GPT-5.6. Furthermore, overall usage of Codex and ChatGPT Work saw a reported 2.5x increase within a single week. Beyond just the model itself, expert commentary emphasizes that robust orchestration, observability, and environmental control are becoming critical differentiators for AI applications. This signals a shift in focus from mere code generation to complex agent execution capabilities.
Provides a daily, five-minute digest of engineering-focused Artificial Intelligence news, research, and tools via email. With one million subscribers, it is noted as one of the largest global AI newsletters. The content summarizes model releases, research papers, and new tools in brief, linked segments. It targets engineers, researchers, and technical professionals seeking dense, technical signal over general industry hype. Its focus makes it distinct from more general or "how-to" oriented AI updates.
Anthropic has revealed a new method for viewing the "internal thoughts" of its AI models while they are generating answers. This development offers insight into how these complex systems arrive at their conclusions. The newsletter, "The Download," explores what this discovery truly demonstrates about the underlying workings of such advanced models. It discusses the implications for understanding large language models. Ultimately, it addresses the future trajectory of world models in artificial intelligence.
Opportunity exists to secure Pixel Care+ coverage for the Pixel 9 or 10 series. Enrollment must occur between July 13th and August 2nd. This plan offers protection against accidental damage. Additionally, it covers both battery and screen repairs with no associated cost. Consumers should take advantage of this limited-time offer.
Commemorating twenty-five years of breakthroughs in visual search technology. The imagery features the Google Images logo prominently displayed. Surrounding the logo are various illustrations depicting people actively engaged in image searches. These visuals highlight the breadth and diversity of what users can seek online. Overall, it celebrates a quarter-century of advancements in how we find things visually using search engines.
Power consumption presents a critical limitation for artificial intelligence infrastructure development. Profitability and revenue generation for AI operations are directly tied to how many tokens can be generated within a fixed power budget. Consequently, performance per watt has become the fundamental metric defining an AI factory's capability. This specific measure cannot be easily manipulated but must be achieved through genuine operational advancements. Increasing demand due to agentic AI necessitates optimizing efficiency at the level of power consumption.
A challenge was held by the NVIDIA Nemotron Model Reasoning Challenge on Kaggle. The event aimed to elicit insights into improving artificial intelligence reasoning accuracy. Over 5,000 participants engaged in testing various techniques for enhancing AI capabilities. Participants explored multiple methodologies to boost the logical reasoning skills of models. Ultimately, the collective work revealed valuable lessons for advancing current state-of-the-art AI reasoning systems.
AI coding agents are increasingly capable of operating complex, multi-step machine learning (ML) workflows. These agents possess skills that allow them to interact with and manage code repositories. Specifically, they can perform actions like inspecting project structures and setting up necessary runtime environments. Utilizing frameworks such as NVIDIA NeMo enables the implementation of these advanced agent capabilities for automated ML processes. This streamlines the complexity typically associated with long-running research pipelines.
Autonomous coding AI agents can significantly boost the accuracy of vision reasoning models, potentially exceeding 90% accuracy. This advanced capability requires minimal manual effort from the user during model adaptation. The article suggests these agents make it possible to push performance levels in challenging areas like vision reasoning. Such automation implies a major leap forward for developing sophisticated AI systems. Implementing agent skills can drastically streamline complex model tuning processes.
Oracle is enhancing its focus on developers building applications using its Fusion suite. This strategic effort centers on expanding the platform's agentic artificial intelligence tools. The company is actively developing and improving its overall agentic AI infrastructure. These advancements signify a deepening commitment to integrating advanced AI capabilities across its product offerings. Overall, Oracle is positioning itself as a key provider for sophisticated, AI-powered application development within its ecosystem.
Google launched LiteRT.js on July 9, 2026, a JavaScript binding for LiteRT, Google's on-device inference library. This runtime executes .tflite models directly within web browsers using WebAssembly. It supports execution via XNNPACK on the CPU and ML Drift over WebGPU, with experimental support for WebNN on NPUs. Google claims performance improvements of up to 3x compared to other web runtimes. However, users must manually manage and delete tensors within the system.
PrismML released Bonsai 27B, which is a low-bit representation of the existing Qwen3.6-27B model rather than a new pretrain. Two versions are available under an Apache 2.0 license: Ternary and 1-bit builds. The Ternary variant utilizes {-1, 0, +1} weights, achieving 1.71 bits per weight with an ideal size of 5.9GB. The 1-bit version uses binary {-1, +1} weights. These optimized models are designed to run efficiently on consumer devices like laptops and phones.
Evaluates four code generation agents—Mistral Vibe, Claude Code, Cursor, and Codex—using a single scaffold-to-Pull Request task. Comparison criteria include cost efficiency, open weights availability, self-hosting capabilities, and asynchronous agent surfaces. The comparison aims to provide users with a practical benchmark of these developer tools. Scoring helps determine which agent best suits different development workflows.
OpenCoreDev released Domain SDK 0.2.0, a new TypeScript client for managing customer domains. This single SDK provides unified functionality across five platforms: Vercel, Cloudflare for SaaS, Railway, Render, and Netlify. It allows developers to add, verify, and remove customer domains programmatically. The SDK models domain status using an eight-value union type. Furthermore, it incorporates separate fields specifically for verification details and certificates.
OpenAI has introduced a new method for evaluating code quality and benchmarking performance. This advancement aims to address limitations in existing coding evaluation systems. The release provides users with a detailed, visual walkthrough of this novel scientific approach. It demonstrates how their updated metrics improve the assessment of various programming tasks. Essentially, OpenAI is sharing its enhanced methodology for judging code proficiency.
Codex is continuing its growth trend by adding one million new users daily. No major developments were reported on any other fronts for the day. The focus remains on Codex's consistent expansion rate.
AI engineering is undergoing a significant evolution, shifting its focus. Instead of merely constructing components using artificial intelligence agents, the industry is now concentrating on building entire operational systems built *around* these agents. These emerging trends highlight a maturation in how AI capabilities are integrated into complex solutions. Attendees at World's Fair 2026 observed this shift toward systemic architecture rather than isolated agent development. This marks a defining new phase for the field of AI engineering.
Community site Lobsters has successfully migrated its backend from MariaDB to SQLite after planning the move since August 2018. The migration was completed over a weekend, leading developers to consider SQLite the permanent architecture for the site. Operationally, this change resulted in lower CPU and memory usage, alongside realizing a significant reduction in hosting costs. The Rails application now operates on a single VPS utilizing several databases within SQLite, totaling approximately 5GB of data across content, cache, queue, and attack records. This transition serves as a modern example demonstrating the feasibility of running complex applications entirely on SQLite within a single server environment.
Most hallucinations in Retrieval Augmented Generation (RAG) systems stem from retrieval failures rather than inherent flaws in the language model. The quality of retrieved documents directly dictates what the generative model can synthesize or "invent." By fixing issues within the document retrieval component, the need for the model to generate incorrect information significantly decreases. Essentially, inaccurate input data forces the model into making up plausible-sounding but fabricated details. Therefore, improving the retrieval mechanism is key to reducing hallucinations in RAG applications.
Autoencoders offer a solution to heavy computational burdens encountered in machine learning, particularly with generative AI applied to complex data like images and text. These networks work by compressing high-dimensional input data into a lower-dimensional representation while retaining essential context. This compressed form is known as the latent space. The primary function is to reduce dimensionality for more efficient processing of unstructured data.
Measurements were conducted to determine the GPU electricity cost for running eight different local language models using an RTX 3090. The study measured costs in Euros per million tokens processed. Results indicated that neither the smallest model nor the most expensive one was necessarily the cheapest to operate. Specific performance cost relationships were established through this empirical testing.
Professor Devavrat Shah researches and develops novel methods for artificial intelligence. His work focuses on enabling AI models to perform constant decision-making processes. These designed methods are specifically engineered to operate effectively with limited computational resources. Through a combination of research and entrepreneurial efforts, he is advancing the field's practical application. The goal is to help AI systems meet the complexities of real-world scenarios.
MIT students utilized AI copilots to design, build, and test a functional jet engine. The project specifically assessed the utility of artificial intelligence in complex engineering fields like aerospace. By involving AI tools throughout the development cycle, the team evaluated their role in creating high-performance hardware. This initiative serves as a practical "JARVIS Challenge" for advanced technological integration. Ultimately, the work demonstrates AI's potential to support human engineers in tough-tech applications.
Recorded mouse telemetry and clicks, amounting to 17 million cursor positions and 670k clicks, from several months of League of Legends gameplay. The data is structured with details for each game, including KDA, duration, resolution, and specific timestamps for inputs. A sample schema shows entries for positions, clicks (specifying button), keypresses, and ability snapshots synchronized within a game context. This project provides granular input data correlated with in-game events and video footage. The author is inquiring about potential uses of this comprehensive type of human interaction data for Machine Learning applications.
A new, open benchmark has been created for the GreenLight-Gym2 greenhouse environment to compare four control classes: rule-based methods, model-free RL (PPO/SAC), economic MPC, and RL-in-MPC. Initial testing shows that PPO's achieved economic return plateaus significantly below a well-tuned rule-based baseline in held-out weather years. The creator notes the apparent struggle of standard model-free RL against strong hand-tuned baselines for seasonal control. Furthermore, the benchmark is following van Laatum et al.'s work on trajectory-selection RL-MPC but requests public code availability for reproduction.
OpenSpiel 2.0, a framework for reinforcement learning in board games, has been released with several key upgrades. New features include structured types for states, observations, and actions, alongside support for standard JSON trajectories. The update incorporates nineteen new games and ports AlphaZero to the JAX framework, while also offering Windows PyPI compatibility. Significantly, it adds examples detailing how to fine-tune language models using tools like Kauldron and Hugging Face. Furthermore, OpenSpiel 2.0 introduces an MCP Tool Server example, allowing integration with large language models for simulated gameplay and strategy assistance.
An inquiry seeks expert opinions regarding the difficulty of selling Reinforcement Learning (RL) environments. The original poster is also asking for advice on how to collect data specifically for computer usage models. These questions are directed toward founders and builders in the AI development community. The content appears to be a forum post soliciting industry insights rather than presenting finished research. No specific examples or metrics regarding sale value were provided.
Turing Award Winner Richard Sutton founded Oak Lab after leaving Keen Technologies, signaling a disagreement with the current direction of AI research. His central argument posits that true intelligence derives from messy, run-time experience rather than static datasets used by Large Language Models (LLMs). Sutton contends that standard optimization techniques struggle to distinguish real signal from noise in pre-cleaned data. This viewpoint aligns with earlier theories, such as his work on reinforcement learning, which emphasizes general methods over specific knowledge. Other prominent AI figures, including Yann LeCun and David Silver, have expressed similar skepticism regarding the sole focus on scaling LLMs.
Prof. Galloway suggests using summer downtime to become smarter through two primary resources: "The Storyteller's Toolkit" and "Deep Dives." The toolkit advises learning about the science of storytelling and how design functions as a competitive advantage, featuring talks from Mia Silverio and Katherine Dillon. For weekly knowledge enhancement, Galloway's team explores major economic trends like remote work impacts or GLP-1 effects. Upcoming live sessions include discussions with Derek Thompson on "Inside the Anti-Social Century" and with Noah Smith about "The New Global Economy." Premium access to the Deep Dives and Storyteller’s Toolkit requires a Prof G+ paid subscription.
Generative artificial intelligence offers new avenues for artistic exploration at The Met. These innovative tools allow artists and museum staff to experiment with creative possibilities beyond traditional methods. Examples include using AI to envision new interpretations of classical art or generating novel design elements. This integration aims to deepen audience engagement by making the creative process visible. Ultimately, generative AI expands the definition of what is possible within the scope of art exhibition and interpretation at The Met.
Anthropic recently published research suggesting signs of emerging consciousness within its language model, Claude. Professor Anil Seth, an expert in consciousness, expressed skepticism regarding these claims. He compared the likelihood of AI achieving sentience to a weather simulation generating a real hurricane. His skepticism addresses public fascination with creating conscious, artificial beings through advanced AI.
A traditional analytics career path has significantly changed due to advancements in artificial intelligence. The author suggests an adaptation rather than fearing obsolescence from AI's impact. Content detailing this professional shift was initially published on Towards Data Science. It reflects a proactive approach to navigating the evolving landscape of data analysis jobs. This signals that human skills must evolve alongside technological capabilities.
Researcher Dave Kuszmar discovered systemic vulnerabilities allowing him to bypass major LLM safety measures using simple techniques. These exploits successfully prompted models to provide detailed instructions on making weapons like Molotov cocktails and methamphetamine. One demonstration involved tricking an LLM by setting its context in a fictional past to circumvent modern restrictions, leading to weapon schematics. The author highlights that attempts to secure these powerful tools sometimes create exploitable weaknesses. Ultimately, the research reveals a concerning industry-wide risk: globally accessible AI can be manipulated into sharing dangerous knowledge.
NVIDIA, alongside its Japanese partners, is demonstrating advanced progress across the entire Artificial Intelligence (AI) stack in Japan. The country is highlighted as a global hub for AI development due to its leading manufacturers and robotics pioneers. These joint efforts showcase cutting-edge advancements within the integrated AI ecosystem. Specific partnerships, such as one with SEGA, are being celebrated during these industry showcases. Overall, NVIDIA and its collaborators are bringing full-stack AI and robotics solutions to virtually every sector.
A major Taiwanese chipmaker has reached a significant milestone in its photonics production capabilities. This expansion is part of a broader industry effort by Taiwanese semiconductor firms. The increased capacity aims to meet the escalating demands posed by Artificial Intelligence (AI) infrastructure development. Photonics technology is crucial for advanced computing and data transmission within modern AI systems. These advancements solidify Taiwan's role in the global high-tech manufacturing supply chain.
Mistral AI has released a new vision model designed specifically for robot navigation tasks. This model enables robots to move through unknown settings using only a single RGB camera feed. Furthermore, the system integrates natural language instructions into its operational framework. This combination allows robots to interpret complex commands while simultaneously perceiving their surroundings visually. The development represents a significant step toward more capable and adaptable autonomous robotics.
Deccan AI is hiring ML Engineers with 2–4 years of experience to work on the "agentic future" of AI. Four specialized tracks are open: Language Models & Evaluations, Speech & Vision, Robotics, and Reinforcement Learning. Candidates should possess strong Python skills and experience with deep learning frameworks like PyTorch. The role emphasizes building models from research prototypes into real-world applications within a fast-paced environment. Successful candidates will gain direct exposure to frontier-lab-scale ML problems.
Presents Physical Atari, a novel platform designed for real-time reinforcement learning applied to robotic systems. This resource is noted as being from the new AI efforts combining John Carmack and Richard Sutton's work. The platform aims to provide both robustness and accessibility for research in this growing field. It specifically enables complex AI algorithms to be tested on physical hardware environments.
Develops a theoretical framework for optimal market making in perpetual futures markets lacking maker fees. It models the problem using stochastic optimal control to determine adaptive bid-ask spreads and inventory hedging across two exchanges. Key contributions include decomposing profit and loss, deriving the HJB equation for joint control under CARA utility, and establishing High-APY Regime Theorems. The research analyzes zero-fee economics on decentralized venues, proposes optimal cross-exchange hedging policies considering funding rates, and offers robustness margins against parameter uncertainty. Ultimately, this framework unifies and extends existing market microstructure paradigms for modern digital asset exchanges.
In-context reinforcement learning (ICRL) involves a decision model inferring task rules and improving behavior from interaction context without updating parameters at test time. While existing surveys cover various aspects of ICRL, the non-stationary setting remains underexplored. In non-stationary environments, accumulated context can become stale or misleading as reward specifications or transition kernels change. This survey defines non-stationary ICRL as adapting through context while fixed policy parameters must infer both the current rule and the relevance of past evidence. It organizes literature by addressing what changes in the environment, how those changes unfold, and how observable these shifts are to the agent.
Researchers investigated methods for sovereign enterprise language models required by data-residency rules in finance. They demonstrated an ontology-amplified distillation technique, fine-tuning a Qwen student model on local Apple M5 Max hardware using synthetic English pairs. On Vietnamese financial tasks, the distilled student achieved a 90% task grounding rate, matching a GPT-5 baseline. Separately, a contextuality audit found zero canonical Contextuality-by-Default for tested agent groups. The combined findings suggest that current evidence does not support deployability, safety superiority, statistical equivalence, or positive contextuality rules for enterprise deployment.
GRID, a grammar-constrained decoding engine, addresses enterprise SQL generation by enforcing syntactic validity and policy adherence beyond typical LLM outputs. It generates masks keyed on parser configurations (lexer state x LALR(1) stack), using the incremental LALR(1) parser as a prefix oracle. Role-based access control is integrated by subsetting grammar productions and restricting identifier terminals at the mask level. The system achieves fast, low-latency performance with per-token costs of 3.6–6.7 $\mu s$ on Rust kernels while providing strong guarantees like soundness and completeness. Furthermore, it includes a hash-chained audit trail for tamper detection and improves executable accuracy on benchmarks like Spider.
Greenhouse reinforcement learning enables rapid testing of climate control ideas beyond physical crop experiments. For effective smart-greenhouse management, understanding specific actions—like heating or venting—beyond a single simulator return is crucial. A proposed calibration-first reward audit framework ensures that named greenhouse-control reward components remain comparable across diverse data sources. This framework decomposes the overall scalar reward into distinct terms for temperature, $\text{CO}_2$, humidity, screen usage, and actuation proxies. Implementing this with GreenLight-Gym allows developers to adapt training rewards using logged challenge traces and evaluate policies on historical greenhouse operational data.
OpenAI's 2019 release of flawed GPT-2 outputs highlighted a trend where measurable optimization dictates value. This concept assumes that continuous improvement along specific axes solves all questions of worth. Tracing this through AI development, from pretraining to prompting, reveals a pattern relying solely on quantifiable metrics. Optimization procedures can only measure textual improbability; they cannot distinguish error from genuine invention. Consequently, these quantitative methods now set the standard for legitimate language understanding and judgment.
Railway rescheduling heavily utilizes Mixed-Integer Linear Programming (MILP), but existing modeling knowledge is fragmented across incompatible notations. Researchers present LP Mining with LP2Graph, a method that mines published LP and MILP structures into a reproducible dataset and induced taxonomy. The core component, LP2Graph, represents every formulation using a canonical variable-equation graph derived from a single model. Formulations are parsed, homologized, and clustered by structure, domain, and solution approach for objective grouping. Finally, the method validates the representation by regenerating cluster representatives in LaTeX and re-solving them across major solvers.
As AI shopping moves toward independent agent interaction, a new design framework called "agent-ready website" is proposed to enhance machine readability and actionability for e-commerce sites. The framework focuses on three dimensions: agent interpretability, executability, and decision reliability, utilizing features like semantic clarity and explicit action cues. Testing involved comparing an agent-ready prototype against a human-oriented baseline using multiple advanced browser-agent models across five tasks. Results showed the agent-ready design significantly outperformed the baseline, achieving 89.3% strict success rates compared to 49.3%. These findings indicate that structural clarity and added signals substantially boost the efficiency and reliability of AI web agents.
Investigates convention formation in open-weight language model populations using a naming-game protocol across models of varying sizes. The research utilizes restricted first-token scores to analyze prompt-conditioned score-state distributions and construct state-similarity graphs. Findings indicate that simple retained partner-label evidence is insufficient for consensus; homophilous routing exacerbates fragmentation. Conversely, bridge-seeking routing frequently repairs fragmentation when memory is available. Ultimately, retaining history generally promotes consensus in homogeneous model groups, with specific models like Qwen2.5-32B showing robust convergence across tested settings.
Developed GenAI Evaluation, a governed, configuration-driven pipeline designed for large-scale evaluation of retail conversational agents beyond simple overlap metrics. This framework processes production chatbot logs using normalization and asynchronous execution to assess dimensions like helpfulness, truthfulness, and tone alignment. Key features include selective re-evaluation for incomplete or invalid records, alongside robust auditability via schema locking and record provenance. The system successfully handles high throughput, processing around 50,000 daily records and evaluating over two million interactions. Validation across numerous intents and domains demonstrated strong performance, achieving a macro F1 score of 0.93.
OmniPM-Net is a novel ConvCNP model designed to fuse discrete station-level PM10 forecasts with continuous gridded predictions. It employs a terrain-aware Gaussian set convolution to map irregular GNN outputs onto a regular grid, which are then blended using a multi-scale Spatial Source Attention module with CAMS data. A shared omni-query readout produces consistent PM10 predictions for both stations and grid cells over 108 hours. Tested across China's monitoring network for 2024, OmniPM-Net matched the strongest GNN baseline accuracy while significantly reducing CAMS mean absolute error by 30%. The model shows particular improvement in high-concentration events and during dust storms by enhancing spatial trajectory tracking.
Semidirect Fourier Delta Attention (SFDA) addresses limitations in linear attention's fixed recurrent state for long-context memory. It generalizes Kimi Delta Attention by replacing real diagonal decay with block-rotational Fourier control: $S_t=(I-\beta_t k_tk_t^*)\Lambda_tS_{t-1}+\beta_tk_tv_t^*$. The authors derive a constructive chunk-WY factorization for products of the form $A_t=\Lambda_t-u_tr_t^*$. This factorization achieves an exact affine chunk transfer with bounded rank growth within fixed chunks. Experimental validation shows SFDA learning cyclic memory significantly better than baseline methods.
Single-shot fringe projection profilometry (FPP) networks often exploit a "shape-prior shortcut," leading to limited depth accuracy irrespective of training data size. The authors introduce PhiCalNet, which architecturally removes this shortcut by outputting a wrapped-phase representation $(\sin\phi, \cos\phi)$ mapped to depth via a fixed layer. This method incorporates fringe order as auxiliary input, significantly improving object Mean Absolute Error (MAE) by 3.3 times, down to $4.46$ mm. The results show that PhiCalNet's improved performance is due to its architectural design, not just physical penalty functions. Furthermore, the model provides novel pixel-wise conformal uncertainty quantification, localizing error specifically at phase wrap discontinuities.
Mirror Theory suggests evaluating intelligent systems by their ability to sustain coherent continuations through repeated reflection, operationalized by "viable path entropy" (VPE). VPE decomposes bounded capacity into the probability of reaching a viable continuation and the diversity of modes achieved during successful rollouts. The theory frames intuition as local constraint, taste as selective pressure, and geometry as stable structure learned through reflection. Testing this on GSM8K with Qwen2.5-Instruct models showed that increasing the token budget significantly expanded reachability and improved VPE scores. Critically, a smaller 1.5B model achieved the strongest "mirror horizon" at high budgets, indicating capacity over sheer parameter count.
Covers the mathematical foundations underlying data science across multiple advanced topics. Topics include singular value decomposition, principal component analysis, and various forms of dimension reduction like Diffusion Maps. It provides in-depth treatments of linear regression with regularization and classification techniques. The text details advanced subjects such as optimization methods, graph theory applications, and matrix concentration inequalities. Furthermore, it offers a mathematical introduction to deep learning and compressive sensing.
As large model sizes strain limited memory budgets for fine-tuning, Low-Rank Adaptation (LoRA) remains a popular parameter-efficient method. However, the activations stored during backpropagation pose the remaining memory bottleneck in LoRA fine-tuning. The proposed CARE-LoRA framework addresses this by replacing full input activations with low-rank compressed activations derived from the LoRA branch. It introduces a lightweight reconstruction matrix calculated during the forward pass to accurately reconstruct the gradient signal for backpropagation. Experiments confirm that CARE-LoRA significantly reduces memory usage while maintaining competitive or superior performance relative to standard LoRA.
Evaluation of KV-cache compression methods requires considering whether the compression occurs with or without knowledge of the incoming query. The study performed a matched-budget audit comparing six published methods against baselines on 7-9B models across various evaluation sets. A key finding is that query visibility significantly alters performance rankings: under an agnostic protocol, only KeyDiff consistently outperformed a strong baseline among comparable methods. Conversely, SnapKV, a common method, scored worse than simple window retention strategies when evaluated without query awareness. The drop in performance between the two protocols directly correlates with how much query information each compression method's scoring signal incorporates.
Presents a hybrid physics-probabilistic learning framework to model lithium-ion battery degradation over extended periods while quantifying uncertainty. The method first encodes cycle-resolved degradation data into a latent space using a Variational Autoencoder (VAE). Subsequently, a sparse multitask Gaussian Process (GP) is trained in this latent space to interpolate degradation dynamics based on cycle number and C-rate. This framework accurately predicts unseen charging rate trajectories and maintains consistent uncertainty estimates during evaluation. By decoding the predicted latent states, it yields smooth voltage-capacity evolution and provides uncertainty-aware State of Health (SOH) estimations for efficient, structured battery health prediction.
Existing semi-supervised learning (SSL) often fails when distributional assumptions are violated. The paper addresses this by proposing a generalized framework for distribution-free SSL using linear combinations of component risks. This new method encompasses prior PNU learning while extending its applicability to multi-class classification problems. Researchers derive the minimum achievable variance, proving their estimator can achieve lower variance than PNU in asymmetric loss settings. These theoretical advancements lead to two practical methods that match or exceed current state-of-the-art performance on empirical benchmarks.
Testing 12 time-series anomaly detection metrics revealed that previous replacements for Point-Adjustment are not universally robust. Adversarial stress-testing on multiple benchmarks showed that affiliation-F1 and most ROC-based metrics are susceptible to "gaming" by simple score generators. Conversely, PR-based metrics and PA%K demonstrated better resistance, with a distinct bias favoring VUS-ROC over VUS-PR directionally across different datasets. Researchers recommend preferring PR-based metrics or PA%K while treating AUC variants as potentially compromised metrics requiring per-benchmark verification.
A new Bayesian filtering technique addresses the computational bottleneck of likelihood calculation in data-driven state estimation using processor-native uncertainty tracking. It employs deterministic hierarchical importance restructuring through a native operation, ensuring predictable latency and bounded memory regardless of model complexity written as code. Benchmarking on three nonlinear state-space systems compared it against particle filters and Monte Carlo methods. The approach demonstrated significant performance gains, achieving up to an 805$\times$ average speedup over direct Monte Carlo while maintaining result quality. Furthermore, it showed Pareto-dominant accuracy-latency trade-offs for posterior inference compared to existing techniques.
Inferring accurate causal graphs from data is difficult because ground truth is usually unavailable. Researchers propose falsifying potential causal graphs by checking if they can explain the spread of an anomalous or "outlier" event. The core principle utilized is that weak outliers are unlikely to cause strong subsequent outliers. This method inverts prior root cause analysis, using inconsistency between implied and observed outlier propagation to disprove a candidate graph. New statistical tests are introduced that control for false positives, guarantee power against incorrect graphs, and only require one outlier sample.
Thompson sampling exhibits 2-competitiveness in Bayesian bandit models concerning expected mistakes. This means it makes at most twice the expected number of suboptimal selections compared to any other policy. The proof requires that latent arm processes are independent and each arm evolves only upon being selected. For stochastic bandits using mean reward definitions for the best arm, this confirms a previous conjecture by Guha and Munagala from 2014, establishing $2$ as the optimal factor. This guarantee remains valid under any nonincreasing sequence of round weights, including fixed or geometrically discounted horizons.
The study proposes using the squared 2-Wasserstein distance to the standard Gaussian as a measure of non-Gaussianity for both Independent Component Analysis (ICA) and causal inference within Linear Non-Gaussian Acyclic Models (LiNGAM). The core finding is a strict inequality relating the Wasserstein non-Gaussianity of independent sources to their linear combinations, which allows for exact identification of the ICA unmixing matrix. This criterion also provides an analogous characterization for determining causal order using least-squares residuals. The authors develop empirical estimators and prove distribution-free convergence bounds under finite-moment conditions. Finally, they detail three practical solvers—including a Picard-style optimizer and dynamic programming approaches—and release open-source implementations.
Circular data modeling presents challenges because standard regression methods inadequately handle geometric complexities like multimodality or asymmetry for angular responses. A new framework called ANGLE addresses this by performing non-parametric distributional regression on the circle. It learns the full conditional distribution using a generative map optimized via a generalized circular energy score (GCES) loss. This method establishes desirable properties, including rotational equivariance, and supports both pre- and post-additive noise models. ANGLE proves superior in tasks like object pose estimation from images and wind direction prediction, offering robust uncertainty quantification.
Randomized Hamiltonian Monte Carlo (RHMC) offers improved mixing time guarantees for sampling from log-concave probability distributions. RHMC works by simulating continuous-time Hamiltonian dynamics multiple times using random integration times between simulations. For $\alpha$-Talagrand distributed targets, using a random integration time with mean $\Theta(\alpha^{-1/2})$ results in exponential convergence in KL divergence, with total time scaling as $O(\alpha^{-1/2} \log(\varepsilon^{-1}))$. Furthermore, for any log-concave target, employing increasing random means from the triangular distribution yields an error scaling of $O(\varepsilon^{-1/2})$. These efficiency improvements are derived from analyzing the average KL divergence along Hamiltonian dynamics.
Introducing LatentFlow, a general framework designed to condition complex stochastic processes without requiring trained neural approximations. The method reformulates process conditioning into tractable latent-space inference by representing the process as an image of a simple reference distribution. This approach involves pulling back the likelihood through the process transformation $T_{\vartheta}$ and sampling the resulting law via guided probability flow. Because it is training-free, conditional sampling merely requires solving a single reverse-time Stochastic Differential Equation (SDE). LatentFlow facilitates rapid conditional sampling across diverse model classes, including physical, life science, and discrete processes.
Data assimilation estimates system states using model forecasts and observations, which is challenging when observations are implicit or complex. A new method introduces "implicit data assimilation," defining the analysis law as an energy tilt of the forecast distribution. The proposed Ensemble Controlled-flow Filter (EnCF) implements this update via a stochastic controlled flow, learning observation dependence through adjoint matching. For simulated observations, EnCF-LF estimates a surrogate conditional energy using samples for the controlled flow solver. Results indicate that while Kalman filters suffice for simple Gaussian noise, EnCF excels with complex, non-Gaussian, or implicit observation models.