INCUMBENT: THE MODEL PROPOSES, THE STORE DISPOSES — STRUCTURAL TRUTH MAINTENANCE FOR AGENT MEMORY

Long-horizon agents let a language model write their memory: it paraphrases what the user said, infers what they want, and decides when a fact has expired, so the model’s own output becomes state that is later retrieved as evidence. We introduce INCUMBENT, a hypergraph memory in which the model proposes and the store disposes: every span it keeps is verified against the user’s words, the store assigns provenance so an inference is never read as a user statement, and each attribute holds one current value to which the reader is bound. Against six baselines, INCUMBENT lifts truthfulness on AI-LieDar to 95.0% from the best baseline’s 53.3%, raises AMemGym’s memory score by 67% (0.344 against 0.206), and reaches 59.0 F1 on LoCoMo while reading 9× fewer context tokens than an agent that holds the transcript. Enforcement, not recording, is what pays: a record of superseded values is worth 0.2 percentage points of AMemGym accuracy to a reader that ranks by similarity and 5.7 to one bound by it.