llm-agents

Paper proposes Skill++, which rewrites only the name and description of incoming agent skills to prevent routing interference in growing skill libraries, raising full-skill selection from 36.4% to 76.3% across 731 skills, three benchmarks, five routers, and the Codex CLI harness.

A new preprint (arXiv 2610.40961) pits frontier LLMs against each other in the classic hold-up game, finding that renegotiable options that boosted human investment to 67% in a 2011 lab study produced zero seller investment across 1,440 AI-agent games.

A developer building an independent verification prototype for coding agents shares two reproduced cases where pytest reported passing despite failing tests (exit status 0, config-based deselection) and asks the community how they verify agent-generated changes before accepting them.

Anthropic released a 10-page PDF outlining an 8-layer framework for building a personal skill library that lets Claude agents automatically select the right procedure, cutting token usage from ~36K to ~3.9K per request.

The HarnessMM paper, a collaboration between Tsinghua University and Microsoft Research Asia, proposes improving agent harness token efficiency through protected decision reuse and recursive self-improvement. Experiments show DeepSeek Harness (DSH) benefits significantly: HumanEval success rate rises from 71.43% to 85.71% with 87.93% fewer tokens per problem, while Codex only saves 29.54%.

Lin Yuxiang (GitHub: Lum1104), a graduate student, saw his side project Understand-Anything jump from 655 stars to 56,400, and it was taken over by Egonex and officially released. It's becoming the default code-understanding plugin for mainstream AI coding tools like Claude Code, Cursor, and Copilot, turning any codebase into an interactive knowledge graph using Tree-sitter and LLM agents.

A curated list of tiny-to-medium models (mostly under 35B) suited for running LLM agents on a normal computer without a high-end GPU, ordered by capability and highlighting models with vision support.

Operon-S1 introduces a System 1-style fast decision controller that separates judging from doing in long-horizon biomedical LLM agents, posing 13 recurring judgements as closed questions answered by a local model in 0.6s median latency while cutting decision-related cost from 8.0%–35.2% with no measurable accuracy loss on five of six benchmarks.

The paper introduces CivHarness, a replay-grounded system for generating long-horizon feedback in Freeciv, and CivTelescope, an LLM judge that predicts which game position leads to a better outcome, addressing 'scoreboard traps' where in-game scores mislead. Using CivTelescope as a dense reward (CivMarshmallow), agents achieve 8.94-point mid-game gains, 77% above the best baseline.

The paper introduces MemIG, a self-evolving memory framework for LLM agents that stores experience as indexable, quantitative, structured records (instead of free-form text) to improve in silico antibody optimization, including epistasis modeling of co-applied mutations.

The paper introduces BABA-Bench, a benchmark of 47 interactive patient-advocacy worlds that checks each agent action in real time for permissions, evidence, and prohibited steps. Evaluating 14 frontier models across 1,974 runs, it finds agents often reach correct outcomes via invalid routes—e.g., Claude Opus 5 hits all outcomes in 81.6% of runs but passes all procedural checks only 52.5%—and that supplying a verified route boosts pass rates to over 91%.

This paper compares centralized, auction-based consensus, and independent LLM agent teams in a perimeter-defense simulation, finding that a consensus team with an external conflict resolution protocol achieves the lowest breach rate (15%) and that prompt-level forewarnings mitigate disguised-intruder confusion.

The paper presents CGAP, a neuro-symbolic framework that compiles tool catalogs into a PDDL model and uses a classical planner with LLM-based goal parsing to guarantee executable multi-tool plans, outperforming standard tool-calling agents by up to 28.9 points on four benchmarks.

The paper introduces Noura, a framework that turns executable personal clinical specifications into supervision for LLM-based diet planning, enabling compact models like Qwen3-14B to dramatically improve multi-day meal plan compliance and reduce rule violations compared to direct generation by larger models.

The paper introduces FABench, a scalable backtesting environment for forecasting agents with an automated pipeline that discovers events, generates questions, and resolves outcomes to avoid training-cutoff leakage. Evaluating agents on eight recent models, it finds retrieval consistently improves Brier scores and lifts leading agents to Polymarket market consensus, while revealing recurring evidence-weighting failures.

论文提出 Agentic World Modeling（AWM），通过只读接口与真实环境交互式获取证据来模拟动作结果，实现无需环境副本的并行 agent 测试时扩展，在 τ-bench、AppWorld 和 OfficeBench 上显著优于单次模拟与训练式世界模型基线。

The paper introduces ProClaim, a scientific agent that persists a structured evidence state outside the model context, and ProClaim-eval, a 718-claim consensus verification benchmark, showing that both the evidence state and a trained stopping judge improve consensus claim verification over baselines and general-purpose agent harnesses.

The paper proposes AgenticTuner, an agentic pipeline using an LLM as the optimizer to calibrate cosmological simulations by leveraging human-readable priors like codebases and documentation, achieving competitive calibration performance at a fraction of the CPU cost of Bayesian optimization.

Flide is an efficient and exact inference engine for LLM decision models that decouples logical tree structures from a single flattened token stream via ForkAttention, answering queries up to 9.5x faster than vLLM and SGLang while staying closest to a float32 reference.

The paper introduces KEEPER, a comparative study of four auxiliary objectives for building control-sufficient textual memory in long-horizon LLM agents, finding that reward-related prediction yields the best task performance and introducing self-consistency bias as a measure of error persistence in recursive memory updates.