CARE-Mem: Decision-Level Counterfactual Credit for Schema-Free Memory Revision

Agents with long-term memory must update outdated information while preserving useful memories. However, a single reward for an entire memory update can reinforce unnecessary edits alongside correct revisions. This paper introduces CARE-Mem, which learns how to revise memory using decision-level counterfactual credit. Given supplied or retrieved memories, CARE-Mem predicts node actions and propagation gates without predefined domain slots or an ontology. During training, it executes counterfactual proposals from the same memory snapshot and assigns the resulting utility differences to the corresponding decision spans in the sampled proposal. At inference, CARE-Mem executes a single proposal without counterfactual evaluation. This study also introduces CAREBench-240, a human-annotated benchmark for node actions, propagation gates, and replacement text. CARE-Mem achieves higher action and edge prediction scores than graph-memory baselines mapped to the same interface. In post hoc exploratory comparisons, CARE-Mem achieves strict replacement exact-match accuracy of 48.68%, versus 39.57% for the parent policy trained by supervised fine-tuning (SFT) and 40.77% for the global-plus-local control. On MemoryAgentBench FactConsolidation, CARE-Mem averages 44.00% accuracy across single- and multi-hop tasks, exceeding the SFT parent and global-plus-local control by 11.00 and 9.17 percentage points, respectively, without further parameter updates.