BISE: Brain-Inspired Memory Consolidation for Self-Evolving Agents

Accumulating usable knowledge through autonomous exploration of an unfamiliar environment is a core capability of self-evolving agents. The challenge is to preserve the context that makes experience reusable while keeping emerging rules open to correction. We present BISE, a knowledge-accumulating agent inspired by the complementary-learning-systems view of biological memory. BISE separates ordered accounts of individual attempts from a compact, source-linked summary, allowing knowledge to be revised without erasing the experience behind it. On six Jericho games with GLM-5.3, BISE achieves a mean normalized score of 55.7%, 22.1 points above its matched ReAct control and 18.0 points above reproduced EvoTest. Gains over ReAct hold on two further backbones. In ScienceWorld, the 211-pair panel reaches a mean cumulative best score of 95.5 across up to five attempts. Ablations favor ordered episode context, while trajectory audits trace revised advice to subsequent actions. Together, these findings support retaining the context behind reusable procedures while keeping the knowledge drawn from them open to correction.