Hippo: Training-Free Agent Self-Evolution via Hippocampal Replay

Key points

• Extracting useful experience from a single attempt is difficult: flawed reasoning can produce success, while poor execution can obscure a sound strategy.1
• Preserving its value over time is also difficult: our accumulation baseline without consolidation initially improves but eventually performs below a no-memory baseline.1
• We introduce Hippo, a memory framework inspired by selective hippocampal encoding and replay that learns from these comparisons when shortcomings are diagnosed.1
• Experiments on SWE-bench Verified, WebArena, and Mind2Web show performance gains in all three settings and substantially fewer retained experiences than the accumulation baseline.1

Sources (1)

• [1]Hippo: Training-Free Agent Self-Evolution via Hippocampal Replay
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 04:43 AM

  Motivated by recursive self-improvement (RSI), we study how LLM agents can learn through external memory without updating model parameters.

  Extracting useful experience from a single attempt is difficult: flawed reasoning can produce success, while poor execution can obscure a sound strategy.

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