ErRsi: Low-Cost Agent Self-Evolution via Elastic Error Localization

Agent recursive self-improvement (RSI) attracts growing interest as a way to improve agents through continued interaction. A crucial component of this process is accumulating and reusing experience to guide subsequent decisions. However, continually adding memories and instructions can increase recurring context costs and introduce irrelevant guidance across task environments. In this work, we propose ErRsi, a two-stage RSI framework with fixed model weights. In the first stage, to extract actionable evidence within context limits, we propose elastic error localization and organize diagnoses and update feedback into error lineages. In the second stage, to translate this evidence into behavioral improvement, we introduce targeted revision with conditional guidance and paired execution for update selection. On Who&When, localization achieves 30.43% exact-step accuracy at 9B and 36.96% at 27B, exceeding representative methods in the matched 9B evaluation. Diagnostic-input comparisons show that localized evidence supports effective revisions. On WebShop, complete-protocol token consumption falls by 44.67–69.77% relative to adapted Agentic Context Engineering (ACE). Frozen WebShop-to-ALFWorld transfer remains within one task of initial success across three model scales, with 75.39–78.64% fewer target-execution tokens than ACE. Together, these results demonstrate accurate error localization, efficient RSI, and low interference across benchmarks.