AI·Stitch-skills is a library of agent skills that follow the Agent Skills open standard for compatibility with coding agents such as Antigravity, Gemini CLI, Claude Code, and Cursor.
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AI·Superpowers is an agentic skills framework and software development methodology that works.
AI·This repository contains a curated collection of Codex plugin examples for OpenAI, each including a required plugin.json manifest and optional supporting files for skills, agents, and marketplaces.
AI·GitHub trending page for the Ansible repository describes Ansible as a simple IT automation platform for deploying and maintaining applications and systems.
AI·OpenAI integrates Codex capabilities into its new ChatGPT Work agent for general office tasks, rebrands models as Sol/Terra/Luna tiers, and makes GPT-5.6 the default for Microsoft 365 Copilot.
AI·OpenAI released GPT-5.6 in three tiers—Sol (flagship), Terra, and Luna—boosting benchmarks in Agent's Last Exam (56.3 score), math, coding, security, and research while slashing token costs and adding Max/Ultra reasoning modes.
AI·OpenAI folds Codex into the new ChatGPT desktop app as Work mode, shifting focus to usage-based billing and water-meter-style pricing for AI agents, effectively 'killing' the old standalone Codex product.
AI·Zhipu AI founder Tang Jie releases internal letter after 'GLM Moment' success, announcing focus on long-horizon tasks, autonomous agents, and self-evolving AI beyond coding and reasoning.
AI·Nubia is set to launch the second-generation 'Doubao Phone' at WAIC 2026 as a mass-market flagship with improved AI agent OS, memory, and execution capabilities.
AI·OpenAI attributes a complete proof of the 50-year-old Cycle Double Cover Conjecture (every bridgeless graph has a cycle double cover) to GPT-5.6 Sol Ultra, which completed the 3-page PDF in under an hour using 64 parallel sub-agents guided by a 700-character public prompt.
AI·This position paper reviews LLM-driven formal theorem provers and argues that current systems function mainly as solvers for well-defined problems, not as research agents capable of discovering new theorems or resolving open conjectures at the frontier. It identifies key limitations in datasets, exploration, tools, and collaboration and proposes a roadmap for AI4Math systems to support genuine mathematical research.
AI·MASTE: A Multi-Agent Pipeline for Zero-Shot Aspect Sentiment Triplet Extraction
AI·DeepSearch-World is a self-distillation framework for web search agents that uses self-generated experience for training in verifiable environments.
AI·The paper empirically assesses the reliability of Gemini models as audio judges for full-duplex voice agents by scoring stereo waveforms, validated against human calibration.
AI·The paper proposes an agentic tool-making pipeline that compiles repeated SOP steps into validated versioned tools to reduce latency and improve reliability in production LLM agents.
AI·Baidu's Baidu Dazhi proactive agent sees daily queries surge 20x since launch; personal and enterprise versions upgrade with intelligent routing, multi-device memory, and browser/PPT tools.
AI·ChatGPT Work is now an agent that takes action across apps and files, stays with projects for hours, and turns goals into finished work using GPT-5.6.
AI·UniClawBench is the first capability-driven benchmark for proactive agents, using live Docker containers and closed-loop multi-agent evaluation to assess five foundational skills across 400 real-world tasks.
AI·Proactive memory agent introduced to combat behavioral state decay in long-horizon agents by actively surfacing scattered decision states.
AI·CausalDS benchmark evaluates causal reasoning in LLM data-science agents bridging symbolic and data-analysis approaches.
Modern LLMs are increasingly deployed in long-context applications such as retrieval-augmented generation, repository-level coding, and agentic workflows whose accumulated reasoning and tool traces routinely push the input an order of magnitude past the pretraining window, making zero-shot context extension the dominant deployment path for open-weight checkpoints. Most existing zero-shot methods fix a single rescaling factor up front, so an aggressive factor sacrifices short-context fidelity wh…
Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs). Previous RL pipelines for LLMs were mostly synchronous and batch-interleaved, which is inefficient for long-horizon agentic tasks. Recently, asynchronous RL has emerged as a more efficient alternative by updating the model as rollouts arrive. However, existing asynchronous RL systems often emphasize throughput, while leaving training stability and task effectiveness largely underexplor…
We present AgentLens, a production-assessed benchmark for interactive code agents. Most code-agent benchmarks reduce a run to a single bit -- did the task pass? -- but the people who actually use these agents experience the entire trajectory: how the agent follows instructions, uses its tools, verifies its own work, recovers from mistakes, and talks to them along the way. AgentLens evaluates that whole trajectory. It pairs formal verification, where an objective check exists, with LLM-written t…
Coding agents increasingly generate pull requests (PRs) for real-world software issues, yet one-shot PR generation remains open-loop: the PR is proposed without systematic review, diagnosis, or revision. We introduce SWE-Review, a framework for closing this loop with agentic code review. Given an issue and an AI-generated PR, a reviewer agent explores the repository, decides whether the PR should be accepted, and provides structured feedback for revision. We evaluate this setting with our propo…
Most safety alignment work treats "detect the attack" as a text classification problem — does the prompt contain language the model's safety guardrails should catch. That assumption breaks down for LLM agents with real tool access. Here's a concrete case: take a known, public security vulnerability (a CVE), work out the sequence of tool calls that would exploit it, then have an LLM rewrite that a…
A new OpenAI research paper shows how AI agents are transforming work, enabling longer, more complex tasks and expanding productivity across roles.
OpenAI introduces three Academy courses that help people build practical AI skills, create repeatable workflows, and apply agents in everyday work.
OpenAI plans to acquire Ona to expand Codex with secure, persistent cloud environments, enabling long-running AI agents across enterprise workflows.
Securing internal systems with an AI Control Roadmap, combining traditional safeguards and real-time monitoring.
Google DeepMind and partners announce a $10M funding call for multi-agent safety research.
Gemini 3.5 is built to help you execute complex, agentic workflows.
Introducing Co-Scientist, a collaborative AI partner built with Gemini to help researchers accelerate scientific breakthroughs.
Explore how AlphaEvolve's Gemini-powered algorithms are driving impact across business, infrastructure, and science.
Gemma 4: Our most intelligent open models to date, purpose-built for advanced reasoning and agentic workflows.
Generative AI
Google Research Blog·research.google··0.2generative aimachine intelligencenatural language processing
Embodied agents are typically built as hand-designed compositions of perception, memory, planning, and action modules. This modularity exposes a large architectural design space, but current systems still rely on researcher intuition to choose where information is stored, how observations are processed, and how model calls are connected. Agent Architecture Search (AAS) automates such design for text-domain agents, but has not been systematically evaluated on perceptual embodied agents through s…