The Archive

LongStraw: GPU-efficient execution stack for million-token RL post-training with GRPO, bridging inference context length vs. post-training gap for agents.

StructureClaw: artifact-centered benchmark for evaluating LLM agents on complete structural engineering workflows with verifiable evidence chains.

Proof-or-Stop: lifecycle control framework for autonomous coding agents using mechanically verifiable evidence gates for transitions between states.

Cars24 deploys OpenAI voice and chat agents handling 1M+ monthly conversation minutes, recovering 12% lost leads via agentic workflows.

Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on reliable multi-step execution. But the ambition runs well ahead of the reality: most deployed “agents” are still chatbot wrappers, the control plane enterprises expect is deliberately hybrid to avoid lock-in, and real-time fiscal control over token burn remains the exception. This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what dr...

OpenUSD is an open, extensible framework that provides a common scene description language for physical AI. It enables teams to bring CAD data, simulation... OpenUSD is an open, extensible framework that provides a common scene description language for physical AI. It enables teams to bring CAD data, simulation assets, and real-world telemetry into a shared, physically accurate view of the world. Until now, building a USD implementation has typically required adapting a large existing codebase— even for teams that need a specific memory footprint… Source

Study shows agent-optimization gains may not compound over time; proposes Terminal-Bench 2.0 to test continual learning on deployed agents.

TRACE assigns per-turn rewards for long-horizon multi-tool agents to improve credit assignment beyond sparse outcome rewards.

OpenAI is finally releasing some hardware. No, it isn't the mysterious AI-powered device the company is developing with former Apple designer Jony Ive, a project already tangled up in a messy lawsuit. Instead, it's a product designed to be used with its coding platform, Codex. The device, a square-shaped block of buttons called Codex Micro, is a collaboration between the AI company and keyboard maker Work Louder. OpenAI said it is a limited-run collaboration that will give users more ways to monitor and manage their agents. The pad closely resembles Work Louder's Creator Micro 2, and marketin...

DeepStress: stress-testing framework for search agents' robustness to degraded retrieval quality and evidence reliability.

Experience Memory Graph enables one-shot error correction for LLM agents via structured trajectory memory, reducing API costs and improving generalization.

SPyCE co-evolves reusable skills and policies for multimodal agents, distilling visual reasoning trajectories to improve generalization across tasks.

The guy behind TCP/IP is working on a standard for identifying AI agents in the wild.

User permission frameworks for AI agents address prompt injection and unauthorized actions; bridges product-level design with enforcement mechanisms.

Willison/Ronacher reflect on how AI agents may erode institutional knowledge and shared understanding embedded in code review friction.

LLM agents often waste computational budget through redundant context re-reading; proposes task-complexity estimation to optimize execution scope.

PalmClaw framework enables native on-device LLM agents on mobile phones via direct API calls rather than GUI automation.

Coding AI agents are becoming practical operators for long-running machine learning (ML) workflows. They can inspect repositories, set up runtimes, resolve... Coding AI agents are becoming practical operators for long-running machine learning (ML) workflows. They can inspect repositories, set up runtimes, resolve build issues, launch experiments, monitor execution, analyze metrics, and summarize results. For reinforcement learning (RL) research, this matters because meaningful metrics often appear only after the essential experiment infrastructure… Source

What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning... What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning models to production video tasks, developers often lose days to data formatting, container setup, training scripts, baseline evaluation, and hyperparameter sweeps before they even know whether post-training improves accuracy. Source

In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic action and numerical parameters. In such settings Reinforcement Learning algorithms typically determine parameters with one-shot estimators, which makes their training sample inefficient. Though in most PAMDP environments explicit but incomplete knowledge (e.g., rules, safety constraints, or expert heuristics) is available, it is rarely directly used to increase the sample-efficiency of training Reinforcement Learning agents. We step into this gap...

Framework evolves evaluation metrics alongside agent skills using evolutionary search over drawback detectors, enabling self-improving systems without pre-defined oracles.

Internet of Agentic Things (IoAT) framework integrates autonomous AI agents with IoT, cyber-physical systems, and edge computing for closed-loop orchestration.

Controlled study finds GRPO RL fails to improve 4B–8B scale web agents over supervised baselines across learning rate and hyperparameter grids, questioning RL value at small scale.

MM-ToolSandBox: benchmark with 500+ tools across 16 domains for evaluating visually grounded multi-turn tool-calling agents.

Safety vulnerability: distributed backdoors in multi-agent LLM systems bypass local monitors by splitting harmful payloads across agents.

Automated red-teaming system discovers reusable vulnerability patterns in production LLM agents (Claude Code, Codex) operating on untrusted content.

"Context bombing" tricks hacking agents into shutting down before they can do harm.

Empirical audit reveals trained distributional RL agents' risk estimates often violate first-order stochastic dominance.

Simon Willison examines DRI (Directly Responsible Individual) concept from Apple/GitLab in context of LLM agents, arguing humans must retain accountability.