Zero-Mem: Zero-Token Memory Operations for LLM Agents

Zero-Mem proposes memory operations for LLM agents that use no additional LLM calls or input/output tokens outside the final question-answering step. It preserves original interaction traces as the source of record, then organizes them through two complementary views: an entity-context graph for cross-interaction connections and a temporal hierarchy for conversational locality and session state. Queries retrieve from both views, follow supporting relations or nearby context, and apply deterministic calibration to discard conflicting evidence before the final reader answers. The supplied abstract does not include the paper’s benchmark results or full methodological details.