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TOPIC · ENTITY #18593

self-improving agents

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2

RESEARCH · 1 SOURCE · arXiv cs.AI

Paper finds fragility in skill-policy co-evolution and introduces the CODA drift audit

The arXiv paper 'Evolving Together, Failing Apart' analyzes the training paradigm where an agent's policy and its skill bank co-evolve, showing theoretically and empirically that cooperative acceptance links a policy's competence to the specific bank it trained with and makes it fragile when skills drift post-deployment. The authors propose CODA (CO-evolution Drift Audit), a deployment-oriented protocol that simulates skill drift and adds metrics — worst-case success under drift, a consistency rate for meaning-preserving edits, and a resistance rate to corrupted skills — and report that across four co-evolution methods and five benchmarks, worst-case success is 5–13 points below peak, erasing 52–93% of delivered-bank gains over a no-skill baseline.

7.0

RESEARCH · 1 SOURCE · arXiv cs.AI

ClawLoop: framework for self-improving harness-based LLM agents (arXiv:2610.14118v1)

arXiv preprint arXiv:2610.14118v1 introduces ClawLoop, a closed training loop for harness-based LLM agents that iteratively synthesizes tasks, trains agents (SFT/RL), and diagnoses failures to generate next-round training data without relying on an external teacher model. Using Qwen3.6-27B as the loop model, the authors report consistent gains on SkillsBench, Claw-Eval-Live, and ClawGym-Bench, and show the synthesized tasks transfer performance improvements to smaller Qwen3.5 4B and 9B models.

7.0