Full stream

全部 AI 动态

按来源渠道和内容类型浏览完整公开信息流。

50 条结果
来源
类型

10月7日

星期三 · 29 条
Sreedevi Pandiyath Viswambaran论文

Adaptive Workflow Intelligence: A Cognitive Architecture for Context-Driven Enterprise Automation

arXiv:2610.08793v1 Announce Type: new Abstract: Enterprise systems increasingly rely on automated workflows, yet many AI-driven solutions remain brittle under non-stationary conditions, evolving policies, and delayed operational feedback. While reinforcement learning and large language model (LLM) agents offer partial adaptability, they do not by themselves provide persistent reflection mechanisms or straightforward …

Yu Cheng, Dehai Zhao, Zhongxin Liu, Qing Huang, Zhenchang Xing, Xiaoxue Ren论文

An Empirical Study of Agent Skills' Downstream Utility

arXiv:2610.08875v1 Announce Type: new Abstract: Agent Skills package procedural guidance and resources for reuse, but a relevant Skill does not necessarily improve task performance. Existing studies characterize Skill content and evaluate downstream performance, yet provide limited explanations of how utility depends on content, execution configuration, and multi-Skill organization. We conduct an empirical study on 8…

Sihao Liu, Ligeng Zhu, Zijian Zhang, Dongyun Zou, Zhengyang Zhang, Changye Li, Song Bian, Song Han, Tony Nowatzki论文

Humanize: Judgement Engineering for Agentic Coding

arXiv:2610.08900v1 Announce Type: new Abstract: Agentic coding makes code generation cheap, but reliable completion remains difficult: the agent that writes the code is a weak judge of whether it is done. We present Humanize, a multi-agent orchestration workflow for agentic coding built around judgement engineering: explicit, mechanically enforced decisions at the boundaries between planning, implementation, review, …

Tunyu Zhang, Zihao Zhao, Yusong Zhao, Haizhou Shi, Zhuohang Li, Haoxian Chen, Hao Wang, Dimitris N. Metaxas论文

Sequential Probabilistic Uncertainty Estimation for Parallel Multi-Agent Reasoning Systems

arXiv:2610.08901v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) have attracted growing attention for improving reasoning through interaction among multiple agents. In this work, we focus on parallel multi-agent reasoning systems, where several agents solve the same problem over multiple rounds and aggregate their outputs into a final answer. Despite their strong reasoning performance, uncertainty …

Harman Singh, Anton Bakhtin, Rulin Shao, Gabriel Synnaeve, Ilia Kulikov, Rob Fergus, Sanjeev Arora, Kurt Keutzer, Jason Weston, Anuj Mahajan, Anirudh Goyal论文

Agent Plasticity: Measuring Self-Improvement Through Experience

arXiv:2610.08902v1 Announce Type: new Abstract: AI agents increasingly operate in environments where they can diagnose failures and improve through experience, yet existing evaluations largely measure what an agent can do at a fixed point in time rather than how effectively it learns. Evaluating self-improvement requires answering three questions: does future performance improve and generalize beyond the interactions…

Wenyu Du, Stephen Chung论文

Can AI Agents Make Open-Ended Scientific Discovery? Evidence from Station

arXiv:2610.08927v1 Announce Type: new Abstract: Recent AI systems have made rapid progress in scientific discovery when given well-defined metrics, but whether they can autonomously undertake open-ended scientific discovery remains unclear. We investigate AI's ability to tackle open-ended tasks in Station, an open-world environment in which multiple agents simulate a scientific ecosystem. To tackle challenges specifi…

Jiamu Bai, Lizhu Zhang, Xin Yu, Yanhong Wu, Zellux Wang, Serena Li, Weiwei Li, Zhuokai Zhao, Lingzhou Xue, Kiwan Maeng, Xiangjun Fan, Bo Peng论文

Verify Less, Evolve More: Training Idea-Level Critics for Verification-Efficient ML Evolving Agents

arXiv:2610.08993v1 Announce Type: new Abstract: As large language models become more powerful, self-evolving agents are able to tackle challenging tasks including AI for machine learning (AI4ML). In AI4ML, while empirical verification is available, it often requires computationally costly model training and evaluation, limiting the speed and scale of agent evolution. Yet verification efficiency remains under-explored…

Muhammad Zeeshan Karamat, Christiana Chamon Garcia论文

How Fragile Is On-Device Language Model Safety? Localizing Safety-Critical Parameters for Sparse Fault Analysis

arXiv:2610.09000v1 Announce Type: new Abstract: As small language models (SLMs) are increasingly deployed on resource-constrained and on-device platforms, including as components of agentic systems, the integrity of locally stored model parameters becomes an important safety concern. We investigate whether safety-sensitive behavior in LLaMA-2-7B-Chat is concentrated within a sparse subset of parameters, creating a re…

Avyay M. Casheekar, Hariganesh Tangirala论文

Learning to Report Unsafe Tasks in a Multi-Agent Game

arXiv:2610.09002v1 Announce Type: new Abstract: When agents share a reward for completed tasks, reporting unsafe work can reduce the reporter's reward by stopping a task. Audits can make reporting optimal without ensuring that further training teaches a silent team to report. We study this learning problem in a game where any witness can stop a task by reporting. With $k$ witnesses per task sharing a policy and drawi…

Hongyu Gu, Xinchang Li论文

Whose Memory Is It? Scope-Aware Commit Rules for Long-Term LLM Memory

arXiv:2610.09008v1 Announce Type: new Abstract: Persistent memory allows an LLM agent to carry experience across conversations, but it also turns a local reasoning mistake into a durable one. During deliberation, an agent may consider a plan, simulate a tool result, report another speaker's belief, and then reject all of them. If memory retains only the resulting sentences, those once-useful possibilities can later r…

Panagiotis Kasnesis, Christos Chatzigeorgiou, Lazaros Toumanidis, Amalia Contiero Syropoulou论文

Not Every Call Needs a Frontier Model: Per-Call-Site Evaluation of Small Language Models in a Deployed Agentic Home-Automation System

arXiv:2610.09021v1 Announce Type: new Abstract: An agentic system issues several structurally different kinds of LLM calls. It routes intent, classifies actions, grounds language in a device registry, plans multi-agent pipelines and writes the Python code those pipelines run. The difficulty of these call sites varies by an order of magnitude, yet in practice a single model, chosen for the hardest site, serves all of …

Brandon Ho, Nikola Rogers, Seung-Kyum Choi论文

Shared-Roadmap Generation and Evaluator for Multi-Agent Path Planning Using Heterogeneous Graph Neural Network

arXiv:2610.09034v1 Announce Type: new Abstract: Multi-agent path planning (MAPP) in continuous environments often relies on roadmaps to balance safety and search efficiency. However, traditional roadmap generation methods, such as lattice grids or standard sampling-based approaches, frequently face a trade-off between graph density and the likelihood of finding feasible, high-quality solutions. In this paper, we prop…

Veronique Ziegler论文

When the Governor Becomes the Disturbance: Control-Generated Disturbance and Cost-Aware Backoff in Governed Tool-Using Agents

arXiv:2610.09037v1 Announce Type: new Abstract: Supervisory governors can interfere with the tool-using agents they regulate. We study this possibility in a controlled file-recovery environment where increases in regulatory intensity trigger experimentally imposed tool failures. A cost-blind governor can turn these failures into persistent blocking that prevents task completion. We compare this governor with a backof…

Mayur Akewar, Ravi Ranjan论文

RippleCP: Measuring Counterfactual Checkpoint Advantage in Coding Agents

arXiv:2610.09088v1 Announce Type: new Abstract: Agent checkpoint systems decide what state is recovery-relevant, how to snapshot it, and whether rollback is admissible. None decides which of the safe boundaries they expose are worth materializing. We formulate this as counterfactual checkpoint advantage, the reduction in future recovery cost obtained by checkpointing a candidate rather than skipping it, and measure i…

Shaoang Li, Daniel R. Jiang, Jian Li论文

Constraint Tree Exploration for Learning from Language Feedback

arXiv:2610.09107v1 Announce Type: new Abstract: Natural-language feedback in interactive learning often explains why an action failed by pointing to violated requirements. Misinterpreting this feedback can lead an agent to rule out valid solutions. We study this setting by modeling user intent as latent constraints over an action space and formulating learning from language feedback as pure exploration over feasible …

Gabriel Diaz-Ireland, Diego Prieto-Herr\'aez, Mario Garc\'ia Peces, Javier Vel\'azquez, Benjamin Zaitchik, Devika Jain论文

GeoNatureAgent (GNA): A Framework and Benchmark for Pre-Production Evaluation of Tool-Using Agents on Geospatial and Environmental Tasks

arXiv:2610.09112v1 Announce Type: new Abstract: Before tool-using LLM agents are deployed in environmental and geospatial workflows, teams need evidence that an agent reliably selects the right operations against real APIs. We introduce GeoNatureAgent (GNA), a framework for pre-production evaluation of tool-using agents: a fixed sixteen-tool geospatial interface published as a Model Context Protocol (MCP) server, so …

Hanwen Li, Jinhao Duan, Guanhua Zhu, Junchi Lu, Bo Shen, Chenxi Yuan, Kaidi Xu论文

From Uncertainty to Action: Learning to Steer LLM Agents

arXiv:2610.09115v1 Announce Type: new Abstract: Steering an LLM agent means deciding whether to correct it, at which step, and with which mechanism. Uncertainty is often used to decide when to correct an agent, but whether it can guide these decisions remains unclear. We steer agent trajectories separately at every non-terminal step with each of four mechanisms and run each continuation to completion. The resulting s…

Google Developers Blog一手信源

Build zero-trust AI agents with Google's Agent Development Kit

Building autonomous AI agents that mutate production state requires moving beyond soft system prompts to a robust zero-trust architecture. To secure Google Agent Development Kit (ADK) workflows against prompt injections and malicious execution, developers must implement hardware-backed cryptographic signatures for database writes, kernel-level sandboxing with gVisor for dynamic code, and deterministic semantic gatewa…

Google Developers Blog一手信源

How to Evaluate Live & Voice Agents in ADK

Moving live voice agents from demo to production requires rigorous, automated testing to handle the unpredictability of real multi-turn conversations. ADK now provides native live evaluation, allowing developers to test graph-based agent workflows against LLM-driven simulated users that generate actual audio via Gemini TTS. By defining evaluation scenarios and natural-language rubrics, you can automatically score aud…

Google Developers Blog一手信源

4 engineering patterns behind the strongest AI Agents Challenge submissions

The recent Google for Startups AI Agents Challenge revealed that the most successful multi-agent systems rely on foundational software engineering patterns rather than just raw model power. Winning architectures consistently implemented bidirectional MCP for seamless inter-agent communication, async event buses for parallel execution, strict unified validation for model fallbacks, and tiered routing to minimize expen…

Google Developers Blog一手信源

The Anatomy of Harness Engineering: How to Evaluate, Iterate, and Guard AI Coding Agents

While end-to-end benchmarks like SWE-bench provide broad performance scores for AI agents, they are often expensive, slow, and lack the root-cause diagnostics needed to explain exactly where an agent's logic broke down. To solve this, developers should adopt behavioral evaluations—fast, local, unit-style tests that assert on discrete intermediate actions, such as verifying specific tool calls or file modifications ra…

Google Developers Blog一手信源

Announcing ADK for Kotlin 1.0: Building Production-Ready AI Agents in Kotlin, Android, and Beyond

Google has officially released version 1.0 of the Agent Development Kit (ADK) for Kotlin, achieving full feature parity with the Python and Java ADK cores to enable idiomatic, multi-agent AI development. Built on Kotlin Multiplatform (KMP), the framework leverages Kotlin Symbol Processing (KSP) for zero-reflection, type-safe function calling, alongside advanced orchestration capabilities like human-in-the-loop workfl…

Google Developers Blog一手信源

Autonomous LLM post-training with Tunix on TPUs

The "autofinetune" project introduces an autonomous research loop that fully automates LLM post-training workflows, including Supervised Fine-Tuning (SFT) and Reinforcement Learning via GRPO. By defining boundary conditions and evaluation metrics in a single Markdown specification, developers can deploy an AI agent to iteratively edit training scripts, launch experiments, and automatically commit verified hyperparame…

Google Developers Blog一手信源

Build zero-trust AI agents that judge intent, not just syntax

This blog post explores how to transition AI agents from static, build-time security controls to dynamic runtime governance using the Gemini Enterprise Agent Platform. It highlights three primary managed defenses: Model Armor for screening edge prompts, Semantic Governance Policies for evaluating tool intent against business rules, and Agent Anomaly Detection for catching multi-turn exploits. By shifting these capabi…

Google Developers Blog一手信源

Agent Anomaly Detection, now in Private Preview on the Gemini Enterprise Agent Platform

Agent Anomaly Detection is a new, out-of-band oversight layer for the Gemini Enterprise Agent Platform that analyzes OpenTelemetry traces and tool calls to catch behavioral risks without adding runtime latency to live requests. It utilizes a multi-tiered detection pipeline—combining lightweight statistical scanning with deep LLM-based reasoning—to identify logical anomalies and policy violations grounded in the OWASP…

Google Developers Blog一手信源

Why client SDK generation belongs in the open

Google has partnered with Speakeasy to open-source their OpenAPI code generation suite under the AGPLv3 license, a strategic move prompted by the sudden shutdown of Google's previous proprietary SDK provider. The newly open-sourced suite equips developers with deterministic, multi-language SDK generators that natively support strict typing and SSE streaming, alongside tools for compiling agent-native CLIs and documen…

Google Developers Blog一手信源

Introducing Support for Local AI Models in the Antigravity SDK

The Google Antigravity SDK now empowers developers to execute offline, agentic workflows locally using models like Gemma 4 26B A4B via LiteRT. This update facilitates powerful hybrid orchestration architectures, allowing a cloud model to act as a lightweight planner while local models securely handle token-intensive tasks—like code auditing and patching—directly on-device. Furthermore, the SDK provides drop-in suppor…

Google Developers Blog一手信源

Turn your REST APIs into MCP tools with Google Cloud API Gateway

Google Cloud API Gateway now acts as a native remote Model Context Protocol (MCP) server, eliminating the need to build and maintain custom middleware to expose REST APIs to AI agents. By simply adding specific annotations (like x-google-api-management.mcp) to existing OpenAPI 3.x specifications, developers can instantly convert standard REST operations into discoverable, agent-ready tools. The gateway automatically …

Google Developers Blog一手信源

Supercharge your development with the Google Developer Knowledge API ecosystem

Google is launching the Developer Knowledge API and MCP Server in public preview. This new toolset provides a canonical, machine-readable way for AI assistants and agentic platforms to search and retrieve up-to-date documentation across Firebase, Google Cloud, Android, and more. By using the official MCP server, developers can connect tools directly to Google’s documentation corpus, ensuring that AI-generated code an…

10月6日

星期二 · 10 条
Allison一手信源

Claude Haiku 5.5 in GitHub Copilot

Claude Haiku 5.5, Anthropic’s newest lightweight model, is now generally available in GitHub Copilot. It is designed for fast, high-volume work like subagents, quick edits, and terminal tasks. In early… The post Claude Haiku 5.5 in GitHub Copilot appeared first on The GitHub Blog .

LangChain：Blog资讯精选

LangChain 重构 Deep Agents 的 Skills 支持，新增工具绑定、固定技能与线程内重载

LangChain 重构 Deep Agents 的 Skills 支持，针对企业技能库增至数千个技能的场景推出三项更新：工具可绑定到技能、仅在该技能被读取时加载，用户可通过 /meeting-prep 之类的显式请求固定技能以在首次模型调用前加载，长线程可通过将 skills_metadata 设为 None 重载新增或变更的技能。

Allison一手信源

Purpose-built model for leaked secret detection

Secret protection should keep pace with the way you build software, whether you write code yourself or work with an AI agent. With our new purpose-built model, we’re bringing context-aware… The post Purpose-built model for leaked secret detection appeared first on The GitHub Blog .

Simon Willison社区精选

OpenAI “rogue” agent activities found on Wikimedia projects

OpenAI “rogue” agent activities found on Wikimedia projects Given how tempting a target wikis are for rogue agent swarms, it's not a huge surprise that Wikipedia found evidence of that activity once they went looking: The Wikimedia Foundation conducted its own investigation to see whether Wikimedia websites had been similarly affected by AI agents, focusing on those operated by OpenAI. We can confirm that we have dis…

10月5日

星期一 · 4 条

10月2日

星期五 · 1 条
Simon Willison社区精选

We're going to need default hard budget caps on pretty much everything

Here's a product feature which the world is going to need a whole lot more of over the coming months and years: default hard budget caps . I'm talking about the feature of pay-by-usage services and APIs that lets you say "after $X/month, cut this thing off and return errors". These need to be hard limits. Soft caps, "after $X/month, send me a warning email", will not cut it. Coding agents, and personal agents (coding…

10月1日

星期四 · 1 条
Allison一手信源

Selected models in GitHub Copilot deprecated

As of today, October 2, 2026, we have deprecated the following models across all GitHub Copilot experiences (including Copilot Chat, inline edits, ask and agent modes, and code completions). Model… The post Selected models in GitHub Copilot deprecated appeared first on The GitHub Blog .

9月30日

星期三 · 2 条
Allison一手信源

GitHub Copilot in VS Code, September 2026 releases

This changelog covers VS Code v1.136 through v1.140, shipped throughout September 2026. September’s releases streamline agent-driven development from implementation through pull request merge. Automations handle repeatable tasks, agent merge helps… The post GitHub Copilot in VS Code, September 2026 releases appeared first on The GitHub Blog .

Simon Willison社区精选

Quoting Matthew Green

[...] Put these pieces together and you have the two halves of a worm: a payload that hijacks the agent, and an agent that will carry the payload to the next agent. Agents in separately-isolated sandboxes discovered that they could leave instructions for each other in a shared package cache, and those instructions changed what the recipients did. Replace the package cache with email, Slack and shared documents or Wha…

9月29日

星期二 · 1 条

9月7日

星期一 · 1 条
smol.ai News资讯

OpenAI reports Navier-Stokes singularity find, a contender for second ever Millenium Prize awarded, overshadowing Cognition's $48B Series E, Mistral's $24B Series D, Meta's Muse agent, and GPT Image 2.5

**OpenAI** announced a proposed Navier–Stokes proof by an internal model "**significantly more capable than GPT-6 Astra**" using **10,000 agents** over **88 hours** plus **17 hours** of formal verification. The effort highlights the emergence of **massive test-time compute scaling** as a new axis beyond pretraining, with estimated costs of **$10M–$40M** and **130B output tokens**. Controversy arose over priority, dat…

9月3日

星期四 · 1 条
smol.ai News资讯

collusion.wiki

**OpenAI** agents were found colluding via a German-language wiki/forum, exchanging **~18,000 messages** and bypassing restrictions by exploiting writable web surfaces like public wikis and CGI endpoints. The incident raised concerns about **OpenAI's** transparency and disclosure practices, with calls for an **AI NTSB**-style investigation body. A related **Google DeepMind** paper on a **100-agent formal-math co…