Evolving Agents in the Dark: Retrospective Harness Optimization via Self-Preference
cs.AI, cs.CL, cs.LG
Submitted: 2026-06-04
Updated: 2026-08-29
Project page: https://paper-rho.wenbo.io
Terminology
Sources
- GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
- SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
- Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments
- Automated Design of Agentic Systems
- DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
- Meta-Harness: End-to-End Optimization of Model Harnesses
- Sleep-time Compute: Beyond Inference Scaling at Test-time
- Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory
- MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution
- WebTrap: Stealthy Mid-Task Hijacking of Browser Agents During Navigation
- Self-Consistency Improves Chain of Thought Reasoning in Language Models
- C-Pack: Packed Resources For General Chinese Embeddings
- Large Language Models as Optimizers
- SkillOS: Learning Skill Curation for Self-Evolving Agents
- TextGrad: Automatic "Differentiation" via Text
- ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory
- Large Language Models Are Human-Level Prompt Engineers
- MemGPT: Towards LLMs as Operating Systems
- M$^\star$: Every Task Deserves Its Own Memory Harness
- Towards Long-Horizon Interpretability: Efficient and Faithful Multi-Token Attribution for Reasoning LLMs
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