HABEAS: Evidence-Gated Reversible Memory Management for LLM Agents

External memory lets LLM agents reuse experience, but its value depends on which records remain available. History-based deletion permanently removes memories with low average utility on retrieved tasks. We identify two evidence gaps in this rule: deficient support under deterministic retrieval leaves many task–memory effects unidentified, and task outcomes confound a memory's contribution with difficulty, context, and execution noise. We propose HABEAS, an evidence-gated reversible lifecycle that separates suspension from verdict. Poor historical feedback quarantines a memory; paired audits re-execute completed tasks with and without it; restoration or deletion requires agreement across tasks; and restored memories remain under review. On two benchmarks (CICIoT2023 traffic classification and LAB-DB SQL tasks) and two models, HABEAS raises mean task success over history-based deletion in all sixteen model–benchmark–condition cells. In the primary CICIoT2023 Natural condition, gains are 6.13 and 2.67 percentage points, while the correct-memory deletion rate falls from up to 24.54% to zero. Ablations show that immediate quarantine accounts for most of the gain, the evidence gate reduces incorrect restorations relative to a simple reversible policy, and post-restoration review adds a smaller improvement. HABEAS creates the same-task evidence that retrieval logs cannot supply and uses it to govern irreversible memory decisions.