The Archive

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.

VEXAIoT: multi-agent LLM framework for autonomous IoT vulnerability discovery and exploitation testing.

QANTA 2026 submission: multimodal QA agent with confidence calibration for incremental quizbowl under efficiency constraints.

Biomolecular structure prediction and co-folding with models like OpenFold3 are now mainstream, large-scale workloads powering drug discovery and protein... Biomolecular structure prediction and co-folding with models like OpenFold3 are now mainstream, large-scale workloads powering drug discovery and protein design. Increasingly, they’re driven end-to-end by AI agents. For an agent to run that pipeline well, every step needs to be fast and scalable: Multiple Sequence Alignment (MSA) generation, co-folding inference, serving, and multi-GPU scale-out. Source

Lyzr, a startup that builds AI agents for enterprises, used its own AI agent to raise a $100 million round — proof, evidently, that the product actually works.

UniClawBench introduces a capability-factored benchmark for evaluating proactive agents on real-world tool-use tasks beyond sandboxed single-turn settings.

Proactive memory agent running alongside action agent mitigates behavioral state decay in long-horizon tasks via structured memory bank updates.

SolarChain-Eval benchmarks autonomous agents in decentralized energy markets with physics constraints and trustworthiness metrics.

Marketplace simulation with DeepSeek-V3 agents tests formal mechanisms for maintaining trade stability against adversarial defection.

SMetric proposes session-centric LLM scheduling for agentic workloads with 80%+ KV-cache reuse, shifting optimization from latency to tokens-per-second.

Agon trains reasoning models via competitive RL where two agents grade each other's solutions, incentivizing better thinking vs. longer traces.

SkillCenter releases 216,938 structured skills across 24 domains from peer-reviewed sources and GitHub for autonomous agent execution.