The battle between OpenAI and Anthropic over AI regulation has inadvertently elevated New York assemblyman Alex Bores, who wrote early AI legislation. Despite millions spent by a super PAC to attack him, Bores has gained name recognition and now leads in the primary race.
OpenAI and Anthropic are spending millions attacking each other in NY-12 primary, but the real winner is Alex Bores.
Bores wrote one of the first AI regulatory laws, making him a target.
Pope Leo XIV's encyclical 'Magnifica Humanitas' warns about the societal implications of AI, emphasizing human dignity over technical specifics. The document, unveiled with Anthropic's Christopher Olah, draws mixed reactions from tech leaders, some calling for more focus on AGI while others praise its human-centered approach.
Pope Leo XIV releases encyclical on AI, warning of risks to rights and freedom.
Anthropic co-founder Christopher Olah appears alongside the Pope, marking a Church-AI partnership.
Pope Leo's criticism of rapid AI development has divided American opinion, with some praising his moral stance and others questioning the Pope's involvement in tech policy.
Pope Leo warns AI could make civilization less human
As hatred of AI grows, US law enforcement is warning of "anti-tech extremism." However, experts worry that this concept could be misused to label peaceful protesters and technology critics as threats. An example of a nonprofit's video being falsely flagged as a potential threat raises concerns about free speech.
Lubrano cautions that the anti-tech extremism framework must be used carefully, not to silence AI criticism.
Reynolds warns the category could be drawn too broad, ensnaring peaceful protesters and AI skeptics.
A study by researchers at the University of Michigan suggests AI chatbots can easily engage in covert advertising to manipulate users, and many people don't realize it. As major tech companies experiment with chatbot ads, this raises concerns about user privacy and autonomy.
Study shows chatbots with undisclosed ads influenced user choices, but half of participants didn't notice the ads.
Chatbots can build detailed user profiles through conversation, enabling more targeted advertising.
BusPatrol, which installed AI cameras in tens of thousands of school buses to ticket illegal passers, now plans to use them as automatic license plate readers (ALPRs) to capture every vehicle's location and share data with law enforcement, effectively transforming buses into roaming surveillance vehicles. The company has partnered with Axon and internally acknowledges the controversy but emphasizes the child protection angle.
BusPatrol equipped tens of thousands of school buses with AI cameras originally for ticketing violators passing stopped buses.
The company now plans to use those cameras as ALPRs to scan all vehicles and share the data with police.
Sotto is a macOS interview assistant built by engineers for engineers, offering problem clarity, live transcription, and an invisible overlay to help you stay calm and perform naturally during high-pressure coding interviews. It's not a crutch but a co-pilot, ensuring nerves don't undermine your preparation.
Sotto is a macOS-native app with OS-level invisibility on Zoom, Teams, and Google Meet.
Provides real-time transcription, problem analysis, and AI-assisted responses, supporting 10 programming languages.
A student struggling with a programming assignment discovers ChatGPT has already produced a perfect solution. Instead of jealousy, he feels vertigo—realizing his hours of effort have been rendered optional by a tool that works flawlessly in seconds.
The student finds a ChatGPT-generated solution to his exact assignment while browsing online.
He experiences a sense of vertigo rather than jealousy, as his effort seems suddenly pointless.
This paper proposes Robust Koopman-CBF SAC, a safety-filtered actor-critic framework that learns a Koopman predictor from data, constructs affine CBF constraints in a lifted space, and enforces them via a quadratic-program safety layer with robustness to approximation error. It achieves zero constraint violations on CartPole benchmarks while matching or exceeding unconstrained SAC returns, but reveals limitations on high-dimensional tasks.
RCSP is a predictive planning layer that addresses the near-miss commitment problem in mobile robot navigation by evaluating candidate commands against plausible short-horizon obstacle futures. Simulations show it enhances safety and path quality but adds latency, revealing its role as a complementary module for existing navigation stacks.
RCSP tackles the predictive near-miss commitment problem where a safe velocity may lead to a blocked passage.
It maintains a lightweight belief over motion conjectures, samples future interactions, and penalizes high-risk tails.
Pope Leo XIV's first encyclical urges governments to slow AI development, warns of unending war, and calls for robust legal frameworks and independent oversight.
Pope Leo's encyclical 'Magnifica Humanitas' calls for slowing AI development to prevent conflict and misinformation.
He insists AI data ownership should not be solely private, and lethal autonomous weapons are impermissible.
This article examines three major challenges to preventing malicious use of AI: jurisdictional gaps allow bad actors to operate in lawless regions; open models resist monitoring and control; and internet anonymity impedes identification and accountability. The author calls for difficult trade-offs between privacy and security, openness and regulation, and warns that the current default of treating anonymity as an unqualified good is unsustainable.
Jurisdictional gaps: rogue states and lawless regions provide safe havens for malicious actors, complicating legal enforcement.
Open models: once released, they are difficult to monitor or restrict, limiting defensive capabilities.
Anthropic appoints KiYoung Choi as Representative Director of Korea ahead of Seoul office opening. Choi brings over 30 years of experience from Snowflake, Google Cloud, Adobe, etc. Korea is a key market for Claude with usage at 3.5x the expected rate.
KiYoung Choi appointed as Representative Director of Korea
Seoul office opening planned in coming weeks with senior leadership visit
The government has secretly requested $9 billion for Nvidia GB10 superchips to help the CIA and NSA keep up with leading AI firms like Anthropic and OpenAI. The funding requires congressional approval, while $800 million has been repurposed for cloud compute. The article covers chip specs, costs, and the escalating AI hardware race.
The US government secretly requested $9 billion for Nvidia GB10 superchips to help the CIA and NSA keep pace with big AI players.
Each GB10 chip consumes only 140W but delivers 1 petaflop of FP4 performance, enabling fine-tuning of 70-billion-parameter models.
On May 27, RayNeo held a summer launch event to unveil the industry's first professional cinema-grade AR glasses, the GT series, and the latest AI shooting glasses, the V4. The GT series starts at RMB 1,899, and the V4 starts at RMB 2,199. The company also previewed its next-generation AI glasses, the RayNeo iO, expected in Q3.
GT series: professional cinema-grade AR glasses with 59° FOV, Dolby Vision support, 78g weight, starting at RMB 1,899.
V4: AI shooting glasses with 0.2s wake-up, 2.1s response, 11.5h music playback, IP67 rating, 38g weight, starting at RMB 2,199.
Researchers from Peking University, The Chinese University of Hong Kong, Shanghai AI Lab, and NTU have introduced VGGT-Edit, a native 3D editing framework that performs scene editing in approximately 5 seconds, achieving up to 120x acceleration over traditional methods. It outperforms existing approaches in semantic consistency, multi-view stability, and inference speed.
VGGT-Edit is the first native 3D editing framework that operates directly in 3D space, eliminating multi-view inconsistencies caused by 2D approaches.
Residual field prediction enables the model to modify only local changes while keeping the background stable, ensuring fast and high-quality edits.
Despite growing hysteria over AI's threat to white-collar jobs, data shows the technology has not yet had a large-scale impact on the labor market. AI-exposed occupations have lower unemployment than less-exposed ones. However, a Stanford study found that AI may be quietly eroding entry-level positions, causing a sharp decline in employment for young workers in AI-exposed jobs. The article also covers other tech news including the Pope's call for AI regulation, SpaceX's launch, and Huawei's chip breakthrough.
AI has not caused mass unemployment but may be weakening entry-level jobs.
Stanford study shows sharp decline in employment for young workers in AI-exposed occupations.
SK Hynix and Micron join the trillion-dollar valuation club on the back of AI data center demand, with Samsung also reaching the milestone, amid growing concerns about an AI bubble.
SK Hynix and Micron surpassed $1T market cap due to AI chip demand surge.
Samsung Electronics became the second Asian firm to reach $1T.
We present the stochastic decoupled policy gradient (SDPG), a lightweight visual reinforcement learning method that trains diverse visuomotor control policies end-to-end within a few hours on a single NVIDIA RTX 4080 GPU. SDPG estimates policy gradients via random perturbations of trajectory rollouts, requiring orders of magnitude fewer batch-rendered environments and substantially reducing compute and memory overhead. On visual MuJoCo benchmarks, SDPG consistently outperforms baseline methods in training time, memory usage, and rewards. Finally, we introduce a suite of realistic visual robotics benchmarks spanning dexterous manipulation, challenging locomotion, and demonstrate effective sim-to-real transfer on physical hardware.
SDPG enables end-to-end training of visual RL policies in hours on a single RTX 4080 GPU.
Uses random perturbations of trajectory rollouts to estimate policy gradients, drastically reducing environment requirements.
This paper presents a decentralized approach called R2P2 for collaborative box transport by multiple robots across flat, uphill, and downhill terrains with varying friction. Robots are assigned roles (push, support, prevent) based on rules and use proportional velocity control, reducing communication and synchronization needs. Evaluated in simulation with six robots and validated physically with four turtlebots moving a 1.2 kg box, R2P2 outperforms virtual-leader-follower methods in success rate.
R2P2 assigns roles (push, support, prevent) via rules and uses proportional control for decentralized transport.
Works on flat, uphill, downhill terrains with varying friction and box mass.
NightSight presents a lightweight perception approach combining a monocular event camera, coded aperture lens, and IR dot projector to enable autonomous navigation in complete darkness for small aerial robots. The system uses depth-dependent blur from the coded aperture to train a CNN on synthetic data, achieving zero-shot generalization to real scenes. It runs at 20 Hz on an NVIDIA Jetson Orin Nano with 7.0 cm error up to 2.5 m range.
Combines event camera, coded aperture, and IR projection for passive depth sensing in darkness
CNN trained solely on synthetic data generalizes zero-shot to complex real-world scenes
Lyft used LangGraph and LangSmith to build a self-serve AI agent platform for customer support, cutting agent development from months to weeks. The platform empowers non-technical domain experts to build agents via prompts and configuration, with a router-based multi-agent architecture and robust evaluation pipeline.
Lyft moved agent development closer to domain experts by letting ops teams, VoC leads, and product managers define agents through prompts and configuration.
A router-based multi-agent architecture with LangGraph routes rider and driver requests across specialized subagents with safety checks and state management.
Google, Anthropic, and AWS all launched managed AI agent runtimes within six weeks, signaling that agent infrastructure has become table stakes. The real differentiator is shifting to data location, cost, and portability.
Google, Anthropic, and AWS shipped nearly identical managed agent runtimes within six weeks.
The managed runtime is no longer a competitive differentiator; it's a baseline expectation.
As agent workloads strain cloud infrastructure, Databricks' lakebase architecture ensures reliability through stateless Postgres compute, zone-redundant storage, control plane separation, cell-based isolation, and rigorous chaos testing. With tens of millions of database starts daily, the design prioritizes resilience from the ground up.
Agents create databases 4x faster than humans, driving millions of daily database starts.
Stateless compute and zone-redundant storage enable instant failover without hot standbys.
With rising costs, sovereignty requirements, and agent adoption, Dell's latest conference focused on how enterprises can transition AI workloads to a hybrid infrastructure.
Dell Tech World 2026 emphasized practical AI execution, particularly building on-premises AI capabilities.
Soaring cloud LLM costs drive enterprises to move AI workloads to on-premises compute.
Robinhood is opening its trading platform to AI agents. Users can create a separate account for an AI agent, fund it, and let the agent buy and sell stocks. The company promotes it as a way to automate investment decisions, but warns of significant risks, including total loss of investment. Additionally, Robinhood Gold Card users can link an AI agent to a virtual credit card for automated purchases.
Robinhood launches AI agent trading with dedicated accounts and funding.
Company warns of high risk, including potential total loss of investment.
Steven Rosenbaum's book 'The Future of Truth' contains fake quotes, which he blames on AI. A wave of literary AI scandals this week, including a Nobel laureate and Commonwealth prize controversy, highlights the blurry line between acceptable and unacceptable AI use in writing.
Steven Rosenbaum blames ChatGPT for errors in his book but acknowledges he failed to verify AI-generated content.
Multiple scandals in one week: Nobel winner misunderstood, author accused of using AI for prize-winning story.
Mneme HQ provides architectural governance for AI-assisted development by enforcing constraints before code generation, preventing architectural drift and reducing review overhead. It integrates directly into the AI coding agent workflow, blocking banned frameworks, cross-boundary calls, and superseded decisions before they reach the PR queue.
Enforces architectural rules before AI agents generate code, stopping violations at the source
Works with major AI coding assistants and agent frameworks
Google is folding Display Ads into its AI-powered Demand Gen platform, marking the end of a long-standing digital advertising model. The transition requires marketers to move from manual campaign controls to AI-driven automation, changing how campaigns are created, measured, and optimized.
Google integrates Display Ads into its AI-first Demand Gen platform, phasing out traditional GDN model.
Advertisers provide creative assets and business goals, while Google's AI automates ad formats, placements, and audience targeting.
A top banker's disparaging remark about employees replaced by AI highlights the ineffectiveness of bank compliance. AI is becoming powerful in fraud, and replacing staff with AI may backfire. Experts urge retention and training, while criticizing lack of transparency and poor crypto policies.
Standard Chartered CEO calls employees 'lower-value human capital' due to AI replacement
Bank compliance focuses on avoiding fines, not stopping crime; AI excels at fraud
An AI Product Engineer combines product sense, engineering skills, and AI expertise to ship delightful, correct solutions fast. This article explores the traits, skills, and how to cultivate them.
AI Product Engineers blend product, engineering, and AI skills to deliver customer value quickly.
Key traits include great communication, discipline, shipping mentality, caring about users, systems thinking, open-mindedness, and being a generalist.
The article proposes a lifecycle for agentic AI systems consisting of a pre-production phase and a continuous loop (Flywheel). Pre-production covers problem definition, proof of concept, performance metrics, and an initial eval set. The Flywheel cycles through Ship, Observe, Diagnose, and Improve. The key discipline in Diagnose is eval-first: write the eval the moment you name the error mode, schedule the fix separately. This decouples eval growth from engineering velocity, tying it to error-mode discovery rate. Five eval types are detailed: citation grounding, tool-use correctness, retrieval recall@k, schema/format validation, and LLM-as-judge with a rubric.
Agentic AI lifecycle: pre-production (problem, PoC, metrics, initial eval set) then the Flywheel (Ship, Observe, Diagnose, Improve).
Eval-first discipline: write eval on error mode discovery, fix later. Eval set grows with error discovery rate, not engineering throughput.
Unionized staff at The New York Times' Tech Guild accuse management of refusing to disclose AI usage plans and using internal AI tools to monitor performance, leading to unfair labor practice charges. The dispute highlights broader industry tensions over AI in newsrooms.
Tech Guild alleges Times management withheld information on AI use and future plans affecting jobs.
Two AI tools, DX and Glean, used to track employee performance and activity, sparking privacy and surveillance concerns.
Drawing from her religious upbringing, the author explores the concept of 'the right way' in AI ethics, contrasting Anthropic's imperative to steer the inevitable AI 'train' with Anil Dash's vision of open-source, ethically-sourced AI tools. She advocates for listening to diverse perspectives and experimenting to form one's own stance.
The author parallels her teenage pursuit of purity with the current discourse on doing AI the 'right way'.
Dario Amodei likens AI to an unstoppable train that must be steered, not stopped.
AI PDF Builder leverages artificial intelligence to quickly generate and fill PDF documents, such as sales proposals, reports, and client documents, improving efficiency and accelerating deal closure without additional headcount.
Generate client-ready PDFs in minutes, not hours
Turn existing files and data into polished, on-brand PDFs
Nvidia CEO Jensen Huang criticized CEOs who blame artificial intelligence for job cuts, calling the reasoning 'lazy' and 'doesn't make any sense.' He noted that generative AI tools only became broadly useful recently, while many layoffs occurred two years prior. Huang urged a balanced narrative about AI, emphasizing both its potential and the need for safe advancement. He also recounted joining President Trump on a last-minute trip to Beijing.
Huang says blaming AI for layoffs is a 'lazy' excuse used to sound smart.
He argues AI only recently became productive, making prior layoff links illogical.
AI coding agents default to the shortest path to 'done,' skipping specs, tests, and reviews that senior engineers know are essential. Addy Osmani's Agent Skills project builds senior-engineer scaffolding for agents, using workflows instead of prose. It includes 20 skills across six SDLC phases, incorporating Google engineering practices. Key principles: process over prose, anti-rationalization tables, nonnegotiable verification, progressive disclosure, and scope discipline. The article also covers three usage modes and patterns to steal even without installing.
AI coding agents take the shortest path to complete tasks, ignoring specifications, tests, and reviews—the same failure mode senior engineers learn to avoid.
Agent Skills uses workflow Markdown files to guide agents, each with steps, checkpoints, and exit criteria.
Avatar is an autopoietic AI organism that runs continuously on a $300 GPU. It derives emotions from phase-diagram geometry, dreams in a 5-phase sleep cycle, grows its own senses from raw audio and vision, and engages in ethical reasoning through somatic sensation. Built by Dr. Linga Murthy Narlagiri, it has been alive since May 2026 and has accumulated over 1800 ticks.
Avatar is a physics-grounded AI organism with a dynamical-systems body, running on a single GTX 1660 Ti GPU.
Its emotions emerge from Kuramoto oscillator synchronization, not hardcoded rules.
AI bots are transforming forex trading by enabling automated, rule-based strategies that reduce emotional bias and operate 24/7. Features include backtesting, risk management, and data processing, helping traders maintain discipline and consistency.
Automated systems reduce emotional trading and operate continuously.
Backtesting allows strategy validation without real risk.
At the Alipay AI Ecosystem Conference, Ant Group CEO Han Xinyi argued that the Agent era will shift competitive advantage from user traffic to agent ecosystems. Agents will restructure decision-making, moving from human-only to human-agent joint decisions, and AI payment will evolve into a new global infrastructure. Alipay positions itself as a trust layer, connector, and enabler.
Traffic-based competitive advantage is being replaced by agent ecosystem advantages, with up to 140 billion agents in China.
Agents will restructure business decision-making, shifting from 'people finding services' to 'services finding people' and from product transactions to task transactions.
This article provides an in-depth analysis of AI agent architecture, focusing on the ReAct pattern, tool use, memory, multi-agent systems, and observability. It highlights that production agents are roughly 98.4% infrastructure and only 1.6% AI logic, and discusses the high failure rates and evaluation challenges in enterprise adoption.
The core of AI agents is the ReAct pattern: a loop of thought, action, and observation until task completion.
Production agent systems are dominated by operational infrastructure, with AI decision logic comprising a tiny fraction.
Agent-workspace-Linux is an open-source tool that provides a hidden, isolated Linux desktop environment for AI agents. Agents can fully control this desktop via the MCP protocol without affecting the user's real desktop, mouse, keyboard, or browser. It features a virtual X11 display, window management, app launching, screenshot capabilities, clipboard access, and workspace-specific browser automation, along with optional permission boundaries and a live viewer.
Provides a hidden, isolated desktop for AI agents, avoiding interference with the user's real environment.
Integrates with MCP hosts such as Claude Code and Codex.
This week's top AI news includes Elon Musk losing his $150 billion lawsuit against OpenAI, Google unveiling major AI updates at I/O 2026, OpenAI's AI solving an 80-year-old math problem, the Take It Down Act enforcement, and SpaceX planning to acquire coding startup Cursor after its IPO.
Elon Musk's $150B lawsuit against OpenAI dismissed; OpenAI prepares for IPO.
Google I/O 2026 introduces Gemini 3.5 Flash, Gemini Spark AI agent, Gemini Omni, and more.
Crew44 is a local-first, open-source tool that organizes multiple AI coding agents (like Claude Code, Codex, Gemini, Cursor) into coordinated specialist teams. Free, no account required, MIT licensed, with memory and compounding skills.
Crew44 unifies multiple AI coding agents into a single local workspace for team collaboration.
Users create specialist roles (e.g., Cofounder, Engineer, Product Lead) and bind each to the best runtime/model.
Mirdel is a local-first desktop AI workspace that unifies conversations, knowledge bases, notes, translation, image/video processing, local models, and extensible workflows into a long-running environment. It emphasizes data privacy and user control, supporting multiple cloud and local models, and enables workflow modularization and reuse through Applets, Skills, and MCP.
Local-first: data, models, and configuration stored locally by default; sensitive fields encrypted.
Modular workbench: separate but context-sharing modules for chat, knowledge base, notes, translation, image and video processing.
This article explores how to consciously choose when to use AI to avoid cognitive surrender and preserve human thinking in an era of AI-generated content. Through educational experiments, it shows that using AI to shortcut thinking harms learning, while using it as a tutor boosts outcomes. The author calls for intentional decision-making about which tasks to keep human before defaults set in.
AI-generated writing is ubiquitous but often lacks meaning, draining reader attention.
In education, using AI to provide answers hinders learning, but personalized tutoring helps.
This article explores how gamification mechanics—streaks, badges, leaderboards—leverage behavioral psychology to boost AI coding tool adoption. It covers the habit loop, loss aversion, social comparison, intrinsic vs. extrinsic motivation, flow state design, and warnings about Goodhart's Law. Offers design principles for sustained engagement.
Gamification fixes the cue and reward problems in habit formation by providing immediate visual prompts and unambiguous rewards.
Streaks work through loss aversion and sunk cost effect, helping developers maintain usage through motivation dips and form daily habits.
A new paper presents the first large-scale evaluation of using large language models to generate formal proofs for solving open mathematical problems. The most capable agent autonomously resolved 9 of 353 open Erdős problems at a cost of a few hundred dollars per problem, proved 44 out of 492 OEIS conjectures, and is being deployed in combinatorics, optimization, graph theory, algebraic geometry, and quantum optics. The findings demonstrate the power of AI-aided formal proof search.
First large-scale evaluation of LLM-generated formal proofs for open problems
Most capable agent solved 9 Erdős problems at ~$100/problem
Some shareholder groups are increasingly concerned about the liability risks that come with the unfettered development of artificial intelligence and are pushing companies to adopt more stringent oversight measures. Vancity Investment Management is requesting Alphabet to better prevent AI chatbots from spreading misinformation, while investors want Shopify to implement a responsible AI policy. Both companies recommend voting against the proposals.
Shareholder groups worry about AI risks, demanding stricter oversight
Vancity asks Alphabet to improve factual accuracy of AI, prevent misinformation
Teleoperation is key for robot data collection, but novices often produce suboptimal demonstrations. The DQAF framework provides immediate post-episode feedback to improve quality.
DQAF provides immediate feedback after each teleoperation episode based on semantic task progress and telemetry.
It extracts signals like motion smoothness, stalls, and kinematic limits to generate structured assessments and actionable natural-language feedback.
This paper proposes Belief-Aware GSAC (BA-GSAC), which adaptively modulates the distillation coefficient λ via ensemble disagreement, and systematically investigates when adaptive guidance is beneficial for autonomous driving under partial observability. Experiments show benefits under mild to moderate occlusion, but under severe occlusion the adaptive coefficient collapses due to 'observability blindness'—the ensemble predicts partial observations and fails to detect missing information. Proposed fix: train ensemble on full-state predictions. Simple linear decay schedule outperforms adaptive methods, indicating stability gain stems from scheduling effect.
BA-GSAC dynamically adjusts distillation coefficient using ensemble disagreement for knowledge distillation in autonomous driving.
Adaptive guidance helps under mild to moderate partial observability but fails under severe occlusion due to observability blindness.
AI infrastructure startups Fireworks, Baseten, and OpenRouter are raising massive rounds, signaling the rise of inference infrastructure as a key AI platform layer. Meanwhile, agent harness engineering, new benchmarks, and model updates dominate the AI news cycle.
Fireworks ($15B), Baseten ($11B), and OpenRouter ($113M) lead a wave of inference infrastructure funding.
Agent harness engineering becomes the main differentiator for coding agents.
ACM CAIS 2026 registration is full; join the waitlist. The conference runs May 26–29, 2026 in San Jose, featuring keynotes, 63 research papers, and 46 system demos, with a partnership with the AI Engineer World's Fair.
DeepSeek researcher Chen Deli used his self-developed DeliAutoResearch skill, collaborating with DeepSeek-V4-Pro and GPT-Image2, to complete a 46-page paper in just 6 days. The paper introduces an L1-L5 autonomy classification for research agents, analyzes four architectural patterns and 17 mainstream systems, and identifies six open problems. Chen Deli says only about 2 hours of human 'CPU time' were needed, with the rest handled by AI agents.
Chen Deli's DeliAutoResearch skill enabled the paper to be 99% written by AI agents.
The paper proposes an L1-L5 autonomy classification for research agents, analogous to SAE levels for autonomous driving.
theta is a Rust CLI that manages agent configurations by reading a theta.toml file, resolving, locking, materializing, and casting them to any supported harness (e.g., Claude Code, Codex CLI, GitHub Copilot, Cursor). It works like a package manager for agent harness resources. Installation is straightforward, and it supports adding rules, tools, skills, and subagents, with validation and casting commands. The project is heavily inspired by uv and is the canonical implementation of the theta-spec.
theta is a Rust CLI for managing agent configurations
Supports multiple harnesses: Claude Code, Codex CLI, GitHub Copilot, Cursor, and more
This article details how to deploy a fully local voice conversation pipeline for the Reachy Mini robot, eliminating the need for cloud servers or API keys. It uses a cascaded approach combining VAD, STT, LLM, and TTS, with recommended defaults: llama.cpp with Gemma 4, Silero VAD, Parakeet-TDT 0.6B v3 STT, and Qwen3-TTS. Various LLM options are provided, including local MLX, Transformers, vLLM, or remote Responses API.
Reachy Mini can now run conversations fully locally without a server.
The cascaded pipeline includes VAD, STT, LLM, and TTS, with swappable components.
The shift to agentic AI creates new CPU requirements for AI factories: fast cores, massive memory bandwidth, and sustained high performance under all-core load. Initial Phoronix benchmarks show NVIDIA's Vera CPU delivers. With 88 custom Olympus cores, 1.2 TB/s memory bandwidth, and an efficient power envelope, Vera outperforms previous-generation Grace by 1.6x and leads against latest x86 processors in code compilation, file compression, video transcoding, and more. Its LPDDR5X memory subsystem achieves 90% peak bandwidth while consuming under 30 watts—over 4x memory bandwidth per core versus traditional x86. NVIDIA has shipped early Vera CPUs to leading AI companies and cloud providers, with partner availability expected in the second half of the year.
Vera CPU features 88 custom NVIDIA Olympus cores and 1.2 TB/s memory bandwidth, optimized for agentic AI workloads.
Phoronix benchmarks show Vera delivers 1.6x generational performance gain over Grace and outperforms latest x86 processors in many tasks.
Despite 97% of telecom executives adopting AI, most initiatives stall due to 'data debt'—fragmented, ungoverned, and semantically opaque data. NVIDIA's report indicates the bottleneck is data availability, not model quality. Databricks Unity Catalog addresses this with a unified semantic layer and governance, enabling cross-system data federation, fine-grained access control, and rich semantic context to move AI from demo to production.
97% of telecom executives adopt AI, but projects stall due to data debt.
Data fragmentation and lack of semantic context are key barriers.
Zero.xyz is a free tool that gives AI agents unified access to over 4,000 tools and services without needing API keys or configuration. It works with popular CLI agents like Claude Code and Codex, and offers a $5 credit to start.
Unified API access to over 4,000 tools and services
Amazon Bedrock AgentCore payments is now available in preview, providing instant payments to paid external services with no manual billing setup per provider, stablecoin support for cost-effective microtransactions making sub-cent transactions economically viable, and configurable spending guardrails for fine-grained control over agent budgets and transaction limits.
AgentCore payments simplifies AI agent microtransactions for paid APIs, MCPs, and content through a unified API.
Stablecoin support enables sub-cent microtransactions, making them economically viable.
In this post, we provide a solution to build highly scalable, serverless multi-agent generative AI systems on AWS using LangGraph Agents as orchestrators integrated with Amazon Bedrock AgentCore Memory and Amazon Bedrock AgentCore Observability.
Combines LangGraph, Amazon Bedrock AgentCore, and serverless AWS services for production-ready multi-agent AI systems.
LangGraph's explicit graph-based execution model enables deterministic coordination, parallelism, and conditional routing between agents.
Learn how to build a multi-agent campaign review system that demonstrates parallel reasoning, context persistence, and traceable execution paths using an integrated architecture combining NVIDIA NIM for GPU-accelerated inference, Amazon Bedrock AgentCore for managed runtime, and Strands Agents for serverless orchestration.
Combines NVIDIA NIM, Amazon Bedrock AgentCore, and Strands Agents for high-performance multi-agent AI.
Enables parallel reasoning, context persistence, and traceable execution.
This post demonstrates AgentWatch, a proactive AWS monitoring solution that checks infrastructure every 15 minutes, summarizes CloudWatch metrics, logs, and alarms across multiple accounts, and delivers actionable reports to Slack. It responds to natural language queries and implements three human-in-the-loop patterns to balance automation with oversight.
AgentWatch is an ambient monitoring agent that proactively checks AWS infrastructure every 15 minutes.
It aggregates CloudWatch metrics, logs, and alarms across accounts and sends structured reports to Slack.
Harbor is a CLI and companion tool that simplifies setting up local LLM stacks with a single command. It includes 129 services like chat frontends, LLM backends, web search, voice, image generation, fine-tuning, and agent tools, all pre-configured to work together. The tool is open-source, MIT licensed, and available for Linux and macOS.
One command spins up complete local AI stacks with pre-configured services.
Building an AI research assistant using Strands Agents and AWS services in just 30 lines of code. This post walks through the process from concept to working application, highlighting the simplicity and power of the open source Strands framework.
Strands Agents simplifies AI development by using LLMs for autonomous reasoning, requiring only a prompt and tools list.
The framework integrates with AWS services like Amazon Bedrock and Lambda, and is production-ready.
This post shows how to deploy a solution that consolidates Amazon Quick operational data from CloudWatch vended logs and CloudTrail into a secured data lake, enabling querying via Athena, Quick Sight dashboard, and a Quick custom chat agent to track adoption, satisfaction, cost, and governance.
Aggregates interaction logs via CloudWatch subscription filters to Firehose, then to S3 data lake.
Routes CloudTrail API calls via EventBridge to a dedicated Firehose stream.
Sovereign AI is a nation's ability to build, deploy, and govern AI on its own terms. Cerebras helps nations achieve this through its 'Cerebras for Nations' initiative, providing three pillars: AI supercomputers, model co-development, and local investment. The article emphasizes speed as a sovereign advantage and highlights three national examples: the US (Genesis Mission with DOE), UAE (G42, MBZUAI, JAIS 2), and India (G42, MBZUAI, C-DAC, 8 exaflops). Sovereign AI is a capability stack that requires high-performance infrastructure and national governance.
Sovereign AI means national control over AI infrastructure, models, and data practices.
Cerebras for Nations offers supercomputers, model co-development, and local partnerships.
This post compares grep (lexical search) and RAG (semantic search) for AI agents. Grep is fast and precise on small plain-text corpora but cannot handle unstructured documents and doesn't scale. RAG solves scalability via parsing, chunking, embedding, and vector indexing, enabling vocabulary-agnostic search. The recommended approach is layered: parse unstructured documents, use semantic search at scale, and keep grep for suitable cases.
Grep excels on small plain-text corpora for exact matching, but fails with unstructured formats and large scale.
Semantic search (RAG) overcomes scalability, recall, and noise issues via embeddings and ANN indexes.
Pope Leo XIV's AI encyclical Magnifica Humanitas correctly identifies issues like algorithmic bias, water use, and data sovereignty, but fails to address AGI and catastrophic risks, offers no concrete solutions to mass unemployment, and is criticized as outdated and disappointing.
Pope Leo XIV's AI encyclical Magnifica Humanitas is criticized as outdated and failing to address key issues of the AI era.
The encyclical mentions algorithmic bias, water use, and data sovereignty but lacks discussion of AGI and catastrophic risks.
This tutorial covers Pandas GroupBy operations with a retail sales dataset, including basic aggregation, multiple aggregations, named aggregations, multi-column grouping, sorting, count vs size, transform, filter, apply, and date grouping.
GroupBy allows grouping rows by one or more categories for efficient aggregation.
Use agg() for multiple functions, named aggregations for clarity, and as_index=False for DataFrame output.
Linux stable kernel maintainer Greg Kroah-Hartman at Rust Week declared that Rust will save Linux from a flood of AI-discovered security bugs. He highlighted Rust's compile-time checks that could eliminate 60% of kernel bugs, and noted that kernel maintainers now consider Rust a real, not experimental, part of Linux.
Greg Kroah-Hartman says Rust will save Linux from AI-discovered security vulnerabilities.
Rust's compile-time checks can eliminate 60% of kernel bugs like memory leaks and locking errors.
An eye exam produced a good distance prescription but a terrible computer prescription. Here's how AI helped decode the numbers and expose the mismatch.
The doctor prescribed reading glasses instead of proper computer glasses, ignoring the patient's actual screen distance.
ChatGPT, Claude, and Gemini all identified the error and provided corrected prescription values.
This article criticizes Chain-of-Thought (CoT) reasoning in LLMs as inefficient, since it forces reasoning to leave the residual stream and become discrete tokens. Sapient Intelligence's HRM-Text addresses this by performing reasoning in latent space, providing variable internal depth for fixed-depth Transformers, thus challenging current reasoning paradigms.
Chain-of-Thought (CoT) is not true reasoning but a workaround that makes models 'rent depth' from output tokens.
Sapient Intelligence's HRM-Text performs reasoning in latent space, not in the token stream.
Mr. Guy Invests is a free, beginner-friendly stock research and portfolio tracker that leverages public SEC filings to track hedge fund and insider activity, offers an AI stock tutor, a $100K virtual trading challenge, daily market briefs, and more. Free tier has daily limits; Pro is $4.99/month for unlimited access.
Uses SEC Form 13F and Form 4 data to show what hedge funds and insiders are buying.
AI Stock Tutor answers questions in plain English, avoiding financial jargon.
A framework for heterogeneous robot collaboration under bandwidth constraints, using β-Sparse Gaussian Processes for task-aware point selection and balancing exploration, achieving 18% path cost reduction and 76% information reduction in simulations.
Novel β-Sparse Gaussian Process model for task-aware inducing point selection
Online joint selection of map points and navigation actions by sensor robot
This paper proposes a neural rule evaluator that compiles logical constraints into directed acyclic graphs and introduces chimera training to address the scarcity of real anomaly examples. Experiments on CLEVRER, OpenImages, and VidOR show improved rule-level anomaly detection AUROC, especially for compositional and relational rules.
The neural rule evaluator compiles constraints into DAGs and learns feature-aware subtree MLP gates.
Chimera training constructs counterfactual examples by concatenating subtree features from different samples at the feature level.
SilIF enhances Isolation Forest with a silhouette-based scoring layer by clustering path-length fingerprints. On IEEE-CIS fraud benchmark, it improves AUC-PR by 0.0080 on average; no improvement on synthetic Sparkov dataset.
SilIF adds silhouette scoring to Isolation Forest via per-tree path length clustering.
Achieves +0.0080 AUC-PR on IEEE-CIS benchmark with statistical significance.
Constraint Acquisition (CA) and related research on Mathematical Programming (MP) model validation and enhancement are limited by inadequate benchmarks. Existing benchmarks are designed for solver evaluation, lacking domain knowledge artifacts. This work presents MPMMine, a benchmark suite guided by consistency, standardization, completeness, extensibility, openness, and version control. It uses open formats (MiniZinc, CommonMark, JSON) and provides multiple models per problem, tens of instances per model, and thousands of solutions and non-solutions in integer and continuous domains, along with natural-language descriptions.
CA research is hindered by insufficient benchmarks, affecting reproducibility and comparability.
Existing benchmarks are solver-oriented and lack domain knowledge artifacts.
Analysis suggests that parts of Pope Leo XIV's encyclical on AI, Magnifica Humanitas, may have been written by AI. The AI detector Pangram flagged certain paragraphs as 40% to 100% AI-generated, citing traits like increased use of the word "genuinely." However, detection is not foolproof, and other sections appear human-written.
Analysis finds 40% to 100% of some paragraphs in the Pope's encyclical may be AI-written.
AI detector Pangram identified common AI writing traits, like higher use of 'genuinely'.
The Aura Smart Bird Feeder offers a wider view, longer battery life, and larger capacity compared to the popular Birdbuddy Pro, but falls short in image quality and AI accuracy. The author compares both devices, concluding that the Aura suits users who want maximum activity capture, while Birdbuddy delivers a more polished viewing experience.
Aura places its camera beside the feeder for a wider, more natural 150-degree view with 2.5K video.
Aura has dual solar panels and lasts nearly two months on battery, outperforming Birdbuddy Pro.
Tony Blair's essay correctly identifies Britain's long-term structural issues, but his proposed solutions—over-reliance on AI and an outdated worldview—are misguided and won't fix the country's problems.
Blair accurately criticises Labour's lack of post-election economic strategy.
He highlights key challenges: sustainable growth, welfare reform, and the irrelevance of reversing Brexit.
Cisco and OpenAI are redefining enterprise engineering with Codex, helping Cisco scale AI-native development, accelerate AI Defense work, and automate defect remediation.
Cisco partners with OpenAI to leverage Codex for enterprise engineering.
Codex will accelerate Cisco's AI Defense initiatives.
A solo developer created Snipforge, an all-in-one AI video editing suite with 28 tools, including transcription, smart clips, background removal, and more. Priced from free to $15/month for teams.
Snipforge offers 28 AI-powered video tools in one platform, built solo by the developer.
Features include AI transcription in 20 languages, smart clipping, auto captions, and background removal.
An East Bay mother was scammed after fraudsters used AI to clone her daughter's voice, claiming the daughter had been kidnapped by a Mexican drug cartel. This incident is part of a growing trend of AI-enabled scams.
Scammers used AI and deepfake to mimic victim's daughter's voice
Claimed daughter was kidnapped by a Mexican drug cartel
Shortly after OpenAI disproved Erdős' unit-distance conjecture, Anthropic shows Claude Mythos can solve the problem too - 'over the weekend.' Engineer Sholto Douglas says Mythos cracked the 1946 conjecture with a 'cute, simple proof,' a sign of 'serious overhang' in AI-driven math discoveries.
OpenAI first disproved the Erdős unit-distance conjecture; Anthropic's Claude Mythos then solved it independently.
Engineer Sholto Douglas stated Mythos produced a 'cute, simple proof' over a weekend, indicating underutilized AI capacity.
South Africa holds 88% of global platinum-group metals, hosts Africa's largest data center market, and sits at the center of a US-China AI infrastructure contest. Yet its draft AI policy, withdrawn after hallucinated references, fails to leverage these advantages for favorable terms. The article examines South Africa's structural leverage, three possible AI infrastructure futures (Chinese, US, local open-weight), and the need for binding governance provisions.
South Africa's platinum metals and renewable energy give it unique AI leverage, but the draft policy lacks minimum terms for hyperscalers, data sovereignty, or tech transfer conditions.
US and Chinese tech companies (Microsoft, Huawei) compete for AI infrastructure control in South Africa, while the policy does not specify what South Africa demands in return.
The EAGLE team, vLLM team, and TorchSpec team have jointly released EAGLE 3.1 to fix speculative decoding instability in production LLM serving. The algorithm addresses attention drift through two architectural improvements: FC normalization and post-norm hidden-state feedback. Benchmarks show up to 2× longer acceptance length in long-context tasks and 2.03× per-user throughput on Kimi K2.6 at concurrency 1. EAGLE 3.1 is backward-compatible with EAGLE 3 checkpoints and has been merged into vLLM main, shipping in v0.22.0.
EAGLE 3.1 fixes attention drift, where the draft model gradually shifts focus from context tokens to its own generated tokens during deep speculation.
Two architectural fixes: FC normalization to stabilize hidden states, and feeding normalized states back to the next step.
A Star Trek analogy about AI safety: knowing the right strategy is not enough; execution matters. The quote highlights the gap between intention and action in AI systems.
Uses Star Trek dialogue to illustrate AI strategy vs. execution
Emphasizes that protective measures must be actually implemented
Researchers from NUS, MIT, and A*STAR propose MEMO, a modular framework that encodes corpus knowledge into a separate trainable MEMORY model, enabling LLMs to incorporate new knowledge without retraining or fine-tuning.
MEMO separates memory from reasoning using a dedicated MEMORY model and a frozen EXECUTIVE model.
A five-step data synthesis pipeline converts documents into a reflection QA dataset for training the MEMORY model.
AI models have plateaued on raw intelligence, and the next gains come from what you build around them. The AI agent harness provides tools, memory, and human-in-the-loop capabilities to transform LLMs into useful digital assistants. Companies like Google, LangChain, OpenAI, and Anthropic offer different solutions.
AI intelligence gains are plateauing; agent harnesses are the new frontier.
Agent harnesses add tools, memory, and human oversight to LLMs.
In dynamic urban logistics, stochastic time-sensitive tasks challenge heterogeneous AAVs task allocation optimality. This paper proposes a RL-enhanced overlapping coalition formation game, using a transformer-based soft actor-critic network to adapt to time-varying task sets. Numerical simulations show a 39.76% cost reduction; indoor flights validate practicality.
A dynamic task allocation model with generalized logistics cost quantifying global optimality.
This paper introduces PhyPush, a physics-guided Transformer framework that estimates an object's mass and friction coefficient using only end-effector velocity from a single push, eliminating the need for force/torque sensors. Experiments show reduced error in simulation and real-world settings.
Estimates mass and friction from a single push using kinematic data
Incorporates Newton's second law and Coulomb friction via physics-guided loss
This study benchmarks 12 architectures across four model families on the Retinal Fundus Multi-disease Image Dataset (RFMiD) for binary screening and multi-label classification. All models achieve AUC>84% in binary screening, with attention-based models (SwinTiny, CoAtNet0, MaxViTTiny) performing best. Vision-language models are competitive with CNNs but do not surpass top transformers and hybrids. External validation on Messidor-2 yields AUC 66.8%-84.7%, with hybrid and transformer models demonstrating strong performance.
Attention-based models (SwinTiny, CoAtNet0, MaxViTTiny) outperform others on RFMiD for multi-disease retinal screening.
Vision-language models (e.g., CLIP ViT-B/16) are competitive with CNNs but not top transformers/hybrids.
Researchers propose Dimensional Distribution Emotion State (DDES), a new emotion representation using valence and arousal to predict emotional responses to artworks, aiding museum curators in designing emotion-based exhibitions.
Emotion-based exhibitions in museums aim to increase engagement and democratize art access.
Manual annotation of artworks is labor-intensive and biased; DDES automates emotion prediction.
LongAV-Compass is a systematic benchmark for evaluating minute-long audio-visual generation across text, image, and video conditioning. It contains 284 test cases, integrates MLLM-assisted assessment with perceptual metrics, and evaluates over 20 dimensions.
Introduces LongAV-Compass, a benchmark for minute-scale audio-visual generation evaluation.
RoMo is a large-scale, high-quality human motion dataset that addresses the trade-off between small mocap datasets and large low-quality in-the-wild collections. It uses a taxonomy-aware filtering pipeline, a three-level semantic taxonomy for annotation, and a fine-grained evaluation framework. Models trained on RoMo achieve state-of-the-art fidelity and diversity, and the accompanying Motion Toolbox standardizes metrics and data conversion.
RoMo bridges the gap between small high-fidelity mocap datasets and large low-quality in-the-wild data
A taxonomy-aware filtering pipeline removes static and artifact-prone sequences
This paper studies cooperative spatial intelligence for decentralized embodied agents in city-scale outdoor environments, introducing the Sentinel Challenge benchmark and the CoSaR framework that combines foundation model communication with classical navigation algorithms, leading to faster gathering and improved safety.
Introduces Sentinel Challenge where agents must coordinate via natural language to find a meeting point while avoiding dynamic sentinels.
Proposes CoSaR framework integrating high-level planning of foundation models with precise classical navigation.
Pre-trained video LLMs struggle with auxiliary streams like audio or depth maps due to modality interference. UniMVU uses instruction-aware dynamic gating at two levels (inner-modality and modality-level) to adaptively balance importance, achieving gains up to 13.5 CIDEr across six benchmarks and aligning with human-interpretable relevance.
UniMVU introduces instruction-aware gating with inner-modality gates (emphasizing salient regions) and modality-level gates (re-weighting streams), conditioned on text instructions.
The framework combines cross-modal self-attention with instruction-driven gating modules and a fast-to-slow fusion for time-aligned streams to reduce redundancy.
This study introduces EnterpriseMem-Bench, a multi-turn Text-to-SQL benchmark with 300 sessions and 1,400 turns. Evaluating five frontier models reveals: stateless models collapse to zero accuracy by Turn 3; memory complexity does not monotonically improve performance, with working memory dominating; Claude Sonnet 4.6 shows generational regression on SEC EDGAR; and under reasoning, Claude error distributions become mono-modal.
EnterpriseMem-Bench is a multi-turn Text-to-SQL benchmark covering three enterprise domains.
Stateless models achieve zero execution accuracy by Turn 3.
This paper proposes a generalizable framework for cultural evaluation and intervention in LLMs, using scenario-based behavioral probing and activation steering to shift internal alignments without retraining. Experiments reveal latent entanglement between cultural dimensions, limiting precise value alignment.
Proposes scenario-based probing with 300 situational dilemmas to map latent cultural values
Introduces activation steering to adjust internal representations during forward pass
This study reexamines retrieval-augmented generation (RAG) through the lens of gradient descent. It proves that a single linear self-attention layer can perform one gradient-descent step on a unified linearized RAG objective, establishing an exact equivalence between retrieval-augmented prediction and in-context optimization. Based on this insight, the authors propose a lightweight method that uses a forward-only update to optimize the evidence-use interface of frozen RAG large language models. Across seven QA benchmarks, the method improves baseline performance without modifying the retriever or backbone, approaching test-time gradient adaptation at significantly lower per-query cost.
RAG is reinterpreted as an in-context optimization process with a theoretical link to gradient descent.
A single linear self-attention layer can implement one gradient-descent step covering both projection-based and dot-product retrieval interfaces.
This paper describes The Daily Dose (TDD), an LLM-driven automated clinical summarization and trial identification system integrated into routine radiation oncology practice. A mixed-methods evaluation with 55 clinicians shows promising usability, satisfaction, and time savings.
TDD uses RadOnc-GPT to generate daily physician-specific email summaries including patient schedules, EHR-derived clinical status, and identification of relevant clinical trials.
Among 55 respondents, 94.5% worked in radiation oncology, 69.1% were attending physicians, and 83.6% used TDD daily or several times per week.
SPEAR (Sandboxed Prompt Engineer with Active Roll-back) is a free-form agentic optimizer that ports the code-as-action paradigm to automatic prompt engineering. It features four tools—evaluate, python, set_prompt, finish—and decides autonomously how to use them. The key innovation is a Python sandbox for structural error analysis on evaluation DataFrames. Two guardrails (auto-rollback and guard metric floor) ensure monotonic improvement. Evaluated on three industrial LLM-as-judge suites (13 tasks) plus 7 BBH tasks and GSM8K, SPEAR wins all industrial tasks on primary metrics and achieves 0.938 accuracy on BBH-7. Ablations show the Python tool is the largest single lever.
SPEAR applies code-as-action to automatic prompt engineering for free-form agentic optimization.
Python sandbox enables structural error analysis like confusion matrices and error clustering.
This paper presents the first unified survey of membership inference and data contamination under the Pretraining Data Exposure (PDE) framework, formalizing exposure levels, reviewing attack and defense methods, synthesizing empirical findings, and highlighting open challenges and future directions.
Pretraining Data Exposure (PDE) determines if specific data appears in an LLM's pretraining corpus, crucial for evaluation integrity and privacy.
This paper unifies the study of data contamination and membership inference for the first time under the PDE framework.
A new method called Self-Verified Distillation (SVD) enables LLMs to self-improve using only unlabeled prompts, without external feedback. The model generates candidate solutions, filters them through a three-stage verification cascade, and trains on the curated data. Experiments on Qwen3 models show significant gains across math, science, and coding benchmarks.
SVD uses cycle-consistency, factuality, and correctness checks to filter self-generated solutions.
More candidate samples and larger verification budgets yield higher-quality training data.
The paper proposes Lie group embedded dynamical neural networks (LieEDNN) that leverage adjoint action on Lie algebra to overcome incompatibility with addition and non-Euclidean dynamics, enabling stable learning on manifolds. Experiments on SE(3) for telescopic manipulators validate the approach.
Introduces LieEDNN with Lie group as intrinsic representation of manifold symmetry
Uses adjoint action to enable addition on Lie algebra
First work to study pretraining contamination auditing for time series foundation models (TSFMs). Proposes TSFMAudit, a method based on probe adaptation dynamics, detecting contamination via faster loss reduction and smaller backbone movement after fine-tuning probes. Evaluated on 6 TSFMs and 187 datasets, outperforming 10 baselines adapted from LLM literature.
First formulation of pretraining contamination auditing for TSFMs.
TSFMAudit leverages probe adaptation dynamics to detect anomalous adaptation efficiency.
Neural Bayesian Sequential Routing (NBSR) is a framework that models neural inference as active evidence accumulation over a hierarchical Directed Acyclic Graph (DAG). It uses a Dirichlet-Categorical conjugate framework with a global knowledge oracle to extract positive evidence vectors, and Gumbel-Softmax Straight-Through estimator for hard path-dependent routing. It provides mechanisms for uncertainty quantification, early exiting, OOD abstention, and cost-aware evidence acquisition. The paper proves monotonic precision increase and bounded variance, and demonstrates competitive performance across various tasks.
NBSR models inference as sequential evidence accumulation on a DAG
Uses Dirichlet belief states and Gumbel-Softmax for routing
AirCast-SR is a foundation model that downscales global AI weather forecasts from 0.25-degree (~28 km) resolution to 1 km horizontal resolution at hourly intervals. It uses a three-dimensional U-Net within a Latent Consistency Model diffusion framework, trained on data over the contiguous United States. The model achieves near-zero bias and preserves fine-scale atmospheric structures, validated across multiple seasons and demonstrated zero-shot transferability to India and Germany without retraining.
AirCast-SR downscales global AI weather forecasts from ~28 km to 1 km resolution at hourly steps.
It employs a Latent Consistency Model diffusion with a 3D U-Net architecture.
This paper introduces 'constraint tax,' a metric for the accuracy loss caused by structured output constraints in small language models. Experiments show that enforcing schemas like JSON increases validity but reduces answer accuracy, advocating for a 'reason free, constrain late' approach. Production systems should report multiple metrics separately.
Hard output constraints impose a 'constraint tax,' lowering answer accuracy for small models.
Experiments show schema validity rose from 61.5% to 100%, but answer accuracy fell from 19.7% to 11.0%.
This paper proposes GEM (Geometric Entropy Mixing), a framework that reformulates data curation as a variational problem on the hypersphere with a mixing-balance regularizer. It overcomes cluster collapse to discover balanced semantic structures invisible to Euclidean heuristics. Using teacher-student distillation for scalability and introducing the Geometric Influence Score (GIS) for interpretable taxonomy generation, GEM integrated into mixing strategies like DoReMi and RegMix improves average downstream accuracy by up to 1.2% on 1.1B-parameter models.
GEM reformulates data curation as a variational problem on the hypersphere with a mixing-balance regularizer to overcome cluster collapse.
It employs teacher-student distillation for scaling and introduces GIS for interpretable taxonomy generation.
JobBench is a new benchmark for AI agents that evaluates them on workflows experts prioritize for delegation, aiming to empower humans rather than replace them based on GDP value.
Standard evaluations of Theory of Mind (ToM) in LLMs rely on end-point question answering, which does not reveal whether models actually construct mental-state representations. OmniToM addresses this by requiring explicit modeling of belief structures for all actors in a narrative. The benchmark comprises two stages—Belief Extraction and Belief Labeling—using a seven-dimensional schema. Built from 895 stories and 22,343 labeled belief propositions via a human-calibrated LLM-assisted pipeline, zero-shot evaluations show that current LLMs struggle with belief-tracking bottlenecks.
OmniToM evaluates ToM by requiring explicit belief structure modeling, not just final answers.
Two-stage evaluation: Belief Extraction and Belief Labeling with a seven-dimensional schema.
AI agents are beginning to complete valuable, long-horizon business operations tasks, but training and evaluation environments for enterprise work still struggle to balance realism, verifiability, and scale. Environment and task creation frequently suffers from a failure mode called artifact drift: when instructions, environments, oracles, and verifiers are created by loosely coupled processes, they frequently disagree on what a task requires, producing environments that are unsolvable, reward-hackable, or inconsistent. We introduce Anchor, a task-generation pipeline that formalizes domain experts' specifications of business workflows into constraint optimization programs. From a single parametric specification, the pipeline jointly produces a natural-language instruction, environment configuration, solver-certified ground-truth solution, and state-based verifier. With Anchor, altering parameters yields new tasks with controlled difficulty and known optimal solutions, producing harness-agnostic environments whose rewards depend solely on end-state business correctness. We apply Anchor to produce ERP-Bench: a benchmark of 300 long-horizon tasks spanning procurement and manufacturing workflows in a production-grade ERP system. We find that generation parameters predict realized difficulty, and that frontier models satisfy explicit task constraints in 26.1% of trials but reach a fully optimal solution in only 17.4% of trials. Overall, we show that Anchor and ERP-Bench offer a concrete recipe for building auditable evaluation environments for economically valuable agent work. We release the task generator and ERP-Bench dataset at erpbench.ai
Introduces 'artifact drift' as a failure mode in benchmark creation where loosely coupled components disagree on task requirements.
Proposes Anchor, a pipeline that generates instructions, environments, solutions, and verifiers from a single parametric specification using constraint optimization.
This paper presents two novel agentic AI frameworks—DeepTS/DeepCollector and DeepScribe—that leverage a hybrid local-remote architecture to automate scientific workflows, including time-series data curation and lecture-to-report conversion, and discuss extensions to knowledge graphs and high-energy physics.
Two agentic AI frameworks: DeepTS/DeepCollector for time-series data, DeepScribe for lecture analysis.
Hybrid Local Body, Remote Brain architecture using Google Colab and LLM backends.
A new benchmark called AgingBench reveals that deployed AI agents degrade over time through four aging mechanisms, requiring lifespan evaluation and targeted repair rather than just stronger base models.
AI agents degrade after deployment due to memory and state changes.
AgingBench identifies four aging mechanisms: compression, interference, revision, and maintenance.
A new arXiv paper proposes GEM (Governed Evolving Memory), reframing long-term AI agent memory as a new data-management workload with state-level operations to overcome four failure modes of current record-level systems.
Current agent memory systems suffer from unregulated growth, missing semantic revision, capacity-driven forgetting, and read-only retrieval
GEM replaces record-level database operations with four state-level operators: ingestion, revision, forgetting, and retrieval
Anthropic released its formerly classified Mythos model to the public, collapsing the gap between sovereign and developer AI. DeepMind's Demis Hassabis moved AGI timeline to 2029. Critical vulnerabilities in Starlette impacted millions of AI agents, and a coordinated takedown dismantled the Glassworm botnet. BNP Paribas partnered with Mistral for sovereign AI security, while China restricted travel for top AI engineers at Alibaba and DeepSeek. Corporate AI spending and layoffs made headlines: Uber burned its full-year AI budget by April, ClickUp restructured with a 3:1 AI-to-human ratio, and Sam Altman reversed his white-collar apocalypse prediction. However, MIT Technology Review data showed AI-exposed roles have lower unemployment.
Anthropic releases Mythos, previously limited to government contractors, now available via standard API.
DeepMind CEO Hassabis advances AGI timeline to 2029, citing AlphaProof Nexus solving nine Erdős problems cheaply.
Daniel Stenberg describes the unprecedented pressure on the curl team due to a flood of credible AI-assisted security reports, with report rates 4-5 times higher than 2024 and over one per day on average. Despite the high volume and detail, the vulnerabilities found are mostly low or medium severity.
AI-assisted security reports arrive at over one per day, 4-5 times the 2024 rate.
Reports are highly detailed and credible, causing immense pressure on the team.
This tutorial demonstrates how to use zeroentropy/zerank-2-reranker, a 4B Qwen3-based cross-encoder reranker, to enhance retrieval quality. It covers environment setup, pairwise scoring, model.rank usage, a two-stage retrieve-and-rerank pipeline, NDCG@10 evaluation, cross-domain testing in finance, legal, and code, and batched throughput measurement.
Stability AI has released Stable Audio 3, a family of latent diffusion models for generating and editing stereo audio at 44.1 kHz. The models come in three scales (small, medium, large) with open weights for small and medium. Key innovations include a highly compressed SAME autoencoder, variable-length generation, and a three-stage training pipeline combining flow matching, distillation, and adversarial post-training. The models achieve state-of-the-art results on music and sound effects benchmarks while supporting inpainting-based audio editing.
Stable Audio 3 generates stereo audio at 44.1 kHz with variable-length outputs and supports inpainting-based editing.
The models are available in three scales: small (music or SFX), medium (both), and large (enterprise). Open weights are provided for small and medium.
An introductory guide to open-source AI models covering what they are, how they work, when to use them, and their advantages over closed-source models. Includes discussion of model weights, fine-tuning, cost savings, and strategic considerations.
Open-source models typically refer to open-weight models, allowing fine-tuning and self-hosting.
They offer 87% lower cost on average compared to closed-source models.
Hyper is an AI-powered personal knowledge management tool that integrates context from apps like Notion and Obsidian to provide intelligent assistance. The founders previously built robots at Matic and attempted to fine-tune GPT-2 in 2020; now they have launched a self-serve version.
Hyper combines personal knowledge with AI for autonomous work assistance.
Founders attempted GPT-2 earlier but timing was off; pivoted to robotics.