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Autonomous-Agents
AI 智能体项目,Autonomous Agents (LLMs) research papers. Updated Daily.。支持多种 LLM 后端,可扩展性强。
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2026-05-31
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项目简介
AI 智能体项目,Autonomous Agents (LLMs) research papers. Updated Daily.。支持多种 LLM 后端,可扩展性强。
适用场景
代码审查与优化
自动化测试编写
文档生成与维护
代码重构建议
快速开始
# 克隆仓库
git clone https://github.com/tmgthb/Autonomous-Agents
# 进入目录
cd Autonomous-Agents
# 查看文档
cat README.md原文 README
Autonomous Agents
Autonomous Agents-research papers. Updated daily. Resources-section-section.
Research papers: 2026 5/5
2026 (5/5), 2026 (4/5), 2026 (3/5), 2026 (2/5), 2026 (1/5), 2025 (4/4),2025 (3/4), 2025 (2/4), 2025 (1/4), 2024, 2023, Earlier
Chronological order.
22nd May 2026
SkillOpt: Executive Strategy for Self-Evolving Agent Skills
- SkillOpt: introduces a systematic text-space optimization framework for agent skills that treats the skill document as a trainable external state for a frozen Target Model, utilizing an Optimizer Model to propose bounded edits based on Rollout Batch evidence.
- The framework employs a Validation Gate to ensure only performance-improving edits are accepted, while a Rejected-Edit Buffer and epoch-wise Slow-Update Field provide negative feedback and long-horizon stability without requiring model-weight updates.
- By decoupling the execution harness from the optimization loop, SkillOpt enables the creation of compact, reusable, and inspectable skill artifacts that transfer effectively across different model scales, execution environments, and benchmarks.
From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills
- SkillLens: introduces a utility-grounded evaluation framework that systematically analyzes the full lifecycle of model-generated agent skills across experience generation, skill extraction, and skill consumption.
- The framework identifies that skill utility is not determined by textual plausibility but by concrete failure mechanism encoding and actionable specificity, which are operationalized into a meta-skill to improve extraction.
- Experimental results across five diverse domains demonstrate that while model-generated skills are generally beneficial, they exhibit non-trivial negative transfer, necessitating a principled, utility-grounded approach to skill development.
CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces
- CHRONOS: introduces a three-layer architecture for temporal KG marketplaces that integrates T-LEGEND, Event-Conditioned MPV, Temporal Coordinator, and a Fixed-Dimension DP Pipeline to provide end-to-end temporal guarantees.
- The framework utilizes neural-ODE temporal decay for index freshness, BOCPD-conditioned Shapley valuation for fair attribution, and an EXP3-IX bandit agent for coordinated DP budget manage