Imitation Holds a Key to Evolution: Localized Repair and Verified Skill Acquisition for Adaptive Agents

Key points

• An agent can retain successful trajectories without learning which part of an experience should repair a new discrepancy.1
• We instantiate this loop as a deterministic symbolic controller and analyze its finite repair-query complexity under explicit identifiability and noninterference assumptions.1
• Across ten simulated tool worlds, PI solves all 600 cold held-out composition tasks with three allowed small trials, compared with 6.17% for a whole-trajectory control, while an equally expressive compositional repair control matches PI.1
• On an unseen tool revision, PI achieves 70.0% success versus 24.0% without small trials and 79.0% with self-trial enumeration; an exploratory 24-task adaptation stream subsequently raises first-submission success to 100%, supporting a limited correction-and-retention mechanism rather than a unique incentive advantage or general intelligence.1

Sources (1)

• [1]Imitation Holds a Key to Evolution: Localized Repair and Verified Skill Acquisition for Adaptive Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 03:42 AM

  We formulate passive imitation (PI) as an architectural loop that localizes a violated contract, extracts a local goal, proposes an effect-matched skill, tests a small patch, and retains evidence after full-task verification.

  An agent can retain successful trajectories without learning which part of an experience should repair a new discrepancy.

Extractive summary: sentences quoted from the sources.