MetaShare: Failure-Aware Collective Self-Improvement for Open-Ended Agents

Key points

• Humans often learn fastest not by repeating success, but by remembering which failures not to repeat.1
• Open-ended self-improving agents can share experience across lineages, as in GEA, or edit their own improvement mechanisms, as in DGM-Hyperagents, yet failed improvement attempts are rarely retained as reusable cross-branch knowledge, and search can collapse onto redundant strategies.1
• Multiple meta-agents propose modifications in parallel at heterogeneous temperatures behind a deterministic selector that gates applicability, scope, compilability, and size.1
• Experiments across paper review, coding, math grading, robotics reward design, and financial audit-risk assessment show that MetaShare's median matches or exceeds DGM-Hyperagents' final performance and reaches its own best-performing agent in 1.7--2.9$\times$ fewer evolution iterations.1

Sources (1)

• [1]MetaShare: Failure-Aware Collective Self-Improvement for Open-Ended Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 01:49 PM

  We introduce MetaShare, a failure-aware framework that records successful and unsuccessful agent modifications together with signed performance deltas.

  Humans often learn fastest not by repeating success, but by remembering which failures not to repeat.

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