arXiv AI
Which Self-Improvements Should We Trust? Reliable Self-Improvement When Agents Reuse Their Benchmarks
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
1d ago
SelfSearch: Reward-Free Search for Self-Improving Agents
arXiv:2609. 37968v1 Announce Type: new Abstract: Advances in the coding capabilities of LLM agents allow them to inspect and modify their own instructions, tools, and execution procedures.
arXiv AI
Sep 10
Safe Harness Self-Evolution: A Theoretical Analysis of Feasibility and Limits
arXiv:2609.08175v1 Announce Type: new Abstract: Harness self-evolution is the process by which an agent modifies its prompts, tools, code, or orchestration in response to task feedback while keeping...
arXiv AI
Jun 26
The Red Queen G\"odel Machine: Co-Evolving Agents and Their Evaluators
arXiv:2606. 26294v1 Announce Type: cross Abstract: Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains.
arXiv AI
Jun 11
Evolving Agents in the Dark: Retrospective Harness Optimization via Self-Preference
arXiv:2606. 05922v2 Announce Type: replace Abstract: AI agents rely on a harness of skills, tools, and workflows to solve complex problems.
arXiv AI
Jul 17
Reward-Free Evolving Agents via Pairwise Validator
arXiv:2607. 14408v1 Announce Type: new Abstract: A self-evolving agentic loop repeatedly proposes a tweaked version of an agent (its prompt template or program) and accepts or rejects the change based on a per-iteration quality signal.
arXiv AI
Aug 21
Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection
arXiv:2608. 20169v1 Announce Type: cross Abstract: We present a novel approach to efficient LLM agent harness optimization through adaptive validation task selection.