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CodeMidas turned 3,185 open-source codebases into 5,545 training tasks for agentic coding environments. And it never read an issue, a commit, or a written task record to do it. The paper behind that number, CodeMidas: Scaling Agentic Coding RL Environments from Code Itself, describes an agentic pipeline that

APort Vault replayed 4,371 human-written attacks against 14 language models from 8 labs. And the takeaway for AI agent payment authorization is blunt: the decision to move money should never live inside the model. The Open Agent Passport (OAP), the open specification APort built and stress-tested in that exercise,

OpenAI closed multimodal fine-tuning to new users on May 8, 2026. Existing users got "the coming months" to keep creating training jobs, which is a duration, not a date. Every fine-tuned model already in production stays alive exactly as long as its base model does. Not longer. Start

OpenAI's text-embedding-3-large moved MTEB from 61.0% to 64.6%. And the argument that number started is reshaping frontier LLM embedding models. Research on arXiv splits the field cleanly: LLMs used as embedders beat dedicated embedding models on reasoning-heavy retrieval, dedicated models win on classification. And the two

A GitHub snapshot dated April 2026 traces the term "agent harness" to late 2025, and the thing it names now decides what your AI coding agent is allowed to touch. An agent harness is the runtime wrapper that turns a bare language model into an agent: it runs

JEPA-Anything is a domain-agnostic framework built on orthogonal predictive factorization (OPF). And it attempts something most "general" AI research avoids: a single architecture evaluated across seven domains at once. The domains are vision, biology, clinical trajectories, control, molecular dynamics, physical fields, and weather, per the paper on AlphaXiv.

A preregistered study (arXiv 2605.23909) just gave LLM overconfidence a formal diagnosis: "too sure they are right." Confidence exceeds accuracy, on average. That single finding explains most of what goes wrong when you put a frontier model inside an agent and hand it real work. If you

Lamini claims Memory Tuning drops hallucinations from 50% to 5%. And frontier LLM agents still overclaim inside that leftover 5%. The strongest vendor number in this space still ships five fabricated answers per hundred. And a formal analysis titled "Hallucination is Inevitable: An Innate Limitation of Large Language Models&

AgentLSD, listed at 2026-09-16 in the AgentSafety Papers tracker on GitHub, is a controlled framework for evaluating AI security agents against adversarial task contamination, a failure class most teams still have no name for. The framework runs those evaluations as CTF challenges. And the benchmark summaries report that it "

Shares of Llama-3-8B, not gigabytes. That's the form the memory claim takes in this paper, and it's how the best number in it hides. I went in hunting a memory figure, skimmed straight past the thing, and had to loop back for it. What I'

Cursor v0.46 made AI coding agents the default interaction mode in February 2025. And GitHub switched its Copilot Coding Agent on for every paid subscriber in September 2025. That is the entire adoption story. Nobody chose agentic coding through procurement or a benchmark bake-off; vendors changed what happens when

Open-source AI agent frameworks have quietly turned into infrastructure, and the proof isn't in anyone's marketing copy. It's in version numbers and shell commands. Microsoft Agent Framework landed at 1.0 GA advertising "graph-based workflows, multi-agent orchestration, and human-in-the-loop support" (Microsoft agent