Cross-Task Experience Memory through Selective Strategy Revision

Experience memory lets language-model agents reuse strategies from earlier tasks. As related tasks reveal new conditions, the memory system must decide which parts of a strategy to preserve and which to revise. We introduce Cross-Task Experience Memory (CTEM), which makes this decision using the relationship between new trajectory evidence and a stored strategy. CTEM links compact strategy items to source fragments: confirming evidence is attached without rewriting, while refinements, counterexamples, and unresolved conditions guide selective updates. The resulting strategy fields provide concise guidance for later tasks, with source context retained for future revisions. On WebArena, CTEM improves macro accuracy over ReasoningBank by 2.1–7.7 percentage points across three backbones; on AppWorld, dense scores improve by 8.4 and 8.8 points. With query enhancement disabled, expanding the update policy from support-only to support+refine and then full relations raises WebArena macro accuracy from 35.1 to 36.3 to 38.2. These results support selective revision as a useful design for maintaining reusable experience across tasks.