EVStateMem: Governing Conversational Memory through State and Evidence

Information in long-term conversations can be revised, withdrawn, or restricted in scope. Even when a memory is relevant to the current question, an agent can violate user intent by using outdated information, exceeding its permitted scope, or bringing up content that should not be invoked. Conversational memory systems therefore need to track information validity over time and constrain its use according to user instructions and the current context. We call this requirement governed use of memory and introduce EVStateMem to address it. The system separately manages source records, versioned states, and answer evidence, retaining statement provenance and the evidence supporting state changes. A derived Current View organizes current states, conflicts, and suppressions; answer-time evidence selection checks source availability, scope, and lifecycle status. We evaluate conversational memory question answering on LoCoMo and LongMemEval, and use TimlMemBench's nine behavioral diagnostics to assess adherence to memory-update and use constraints. EVStateMem achieves competitive question-answering results on both benchmarks and the highest scores among the compared configurations on seven of TimlMemBench's nine behavioral axes, including revocation, replacement, scope control, and selective activation.