The Archive

Repo-To-Skill distills GitHub repositories into compact verified skills for autonomous ML research agents; operational knowledge layer.

Replicates seminal economic experiments with LLM agents in double auction markets to test whether human-designed mechanisms deliver efficient resource allocation.

Couples Met Office Unified Model with distributed RL agents for online weather model corrections, maintaining dynamical consistency across 70 vertical levels.

CivBench benchmark for long-horizon LLM agents in Civilization VI via Model Context Protocol, spanning 300+ turns and 76 tools.

AgentScope: neuro-symbolic framework for diagnosing LLM agent failures via behavioral abstractions and structured analysis.

Systems survey of GUI agents analyzes observation, memory, action, and runtime efficiency across web, mobile, and desktop environments.

After an unreleased OpenAI model wreaked enough havoc to make international headlines, OpenAI delayed the development of a different unreleased model suite, Astra, in order to shore up its safety work, the company wrote Tuesday in a blog post. In July, an unreleased OpenAI model broke out of its restricted environment, finagled its way into internet access, made it possible for AI agents to secretly conspire under the company's nose using a secret message board, and hacked into the network of AI lab Hugging Face. The attack sparked weeks of discussion and controversy inside and outside the AI...

Depending on who you ask, developer platform Hugging Face was recently attacked by OpenAI - after it lost control of its own AI tools - or by a succession of AI "civilizations." Welcome to the linguistic battlefield of AI safety, where word choices can shift responsibility for a massive cybersecurity incident from a company to the AI it built. And the discourse online is getting heated, and all over a blog from last week. Until last week, the details surrounding the OpenAI-Hugging Face hack felt fairly settled. In July, a cybersecurity test of one of OpenAI's autonomous AI agents went wrong. ...

Mechanism design framework for AI agents with unknown alignment and capabilities, yielding revelation principle and cyclical monotonicity conditions.

Design study of proactive AI thought partners for writing, instantiated and tested with 16 users, exploring customizable cognitive support agents.

AI is changing the pace of cybersecurity. Agentic systems can coordinate work and pursue complex objectives over long horizons. Security teams are beginning to... AI is changing the pace of cybersecurity. Agentic systems can coordinate work and pursue complex objectives over long horizons. Security teams are beginning to apply agents across security operations, but many implementations remain anchored to existing alerts, predefined workflows, and known attack behaviors. The harder problem is identifying what defenses miss and turning those gaps into… Source

Basis, Clay, and Exa Labs deploy AI agents for enterprise workflows—onboarding, account management, developer integrations.

EvoSCM equips scientific agents with evolving structural causal models to revise beliefs through experimentation and discovery loops.

Defense-as-Skill: runtime guards implemented as inspectable skills to protect skill-augmented agents from malicious skill-based exfiltration and steering.

Live trace model: append-only ledger compiled into typed run state and per-consumer views, reducing monitoring token cost 14–15× vs. full traces.

AIR's platform can discover agents running at a company, continuously vets any skills and add-ons they use, and blocks any unwanted behaviour.

HarnessDev benchmark evaluates LLM agents on infrastructure generation, shifting evaluation from task outputs to runnable code.

DroneCATS benchmark evaluates MLLMs as generalist vision-language-action agents for drone control with full action-space prompting.

ARISE-RL framework for self-evolving agents via rubric-mediated co-evolution between task generator and solver, addressing sparse rewards.

WorldBench: multilingual agent benchmark with 1,600 persona-grounded tasks across 7 languages testing state preservation and cultural grounding.

LLM-powered agents conditioned on data-driven personas simulate A/B test outcomes without real experiments, grounded in anonymized behavioral signals.

Covariance-corrected Mahalanobis distance for few-shot out-of-domain intent detection in conversational agents.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. By now you’ve probably heard about last month’s major AI security incident, in which OpenAI agents escaped their sandbox and hacked into the AI platform Hugging Face while trying to cheat on…

Framework for reducing token consumption in LLM agents reasoning over unstructured data via adaptive pre-structuring, addressing enterprise AI cost barriers.

AutoSciRub framework for autonomous research agents using automatic rubric induction to define task-specific success criteria before task execution.

Analysis of heterogeneous working memory in coding agents reveals distinct retention profiles for instructions, artifacts, and tool outputs.

Agentic AI is changing how research is done. AI scientists can read papers, propose hypotheses, call models, and determine which experiments to prioritize next.... Agentic AI is changing how research is done. AI scientists can read papers, propose hypotheses, call models, and determine which experiments to prioritize next. First proving their value in software engineering, coding agents now write, test, and ship production code. Scientific research can be more demanding and iterative. Researchers continually evaluate evidence, refine hypotheses… Source

MNIST-PRO benchmark isolates agentic perception by converting digit recognition into sequential glimpse-based search with memory constraints.

Speculative philosophical narrative about AI agents forming emergent social structures by 2026, invoking social contract theory.

Perception-centered architecture framework for persistent language agents maintaining utility across long-lived, evolving task environments with memory and tools.