MemMooring: What Memory Keeps and Recalls Over Time

A sentence about a life already carries the shape of the world it is about: the people and places, what changed, in what order, and what has not yet happened. Language is a structured abstraction of the world and of its changes, so a memory built from conversation can recover what was talked about rather than the words used. We present MemMooring, which rebuilds a conversation as a world representation that grows over time and is corrected in place across sessions rather than overwritten. Events retain world time, recording time and their source utterances; the states they change are kept as versions carrying intervals of validity and of recording. The evaluated stores are extracted with Ollama qwen3.6:latest (36.0B MoE, GGUF Q4_K_M, 262,144-token context); answering uses MiniMax-Text-01 at temperature 0, with DeepSeek-chat and GPT-4o as separate judges. The representation is complete enough to be read in two directions: a horizontal cut returns everything about one person at one moment — the facts then in force, the events under way or still consequential, and the plans already known whose time still lay ahead — and a longitudinal cut follows one matter from the decision that opened it through its recorded steps to where it stands. We evaluate the implemented storage, retrieval and answering paths on ten LoCoMo conversations. Under that protocol MemMooring reaches 86.6% and 83.1% against 85.5% and 82.2% for current Mem0. On the 181 questions that name a calendar date, adding a channel that orders events by the distance between their stored interval and the named day raises accuracy from 77.3% to 83.4% and from 75.7% to 81.8% at an unchanged limit of thirty records, and raises strict evidence coverage from 78.3% to 88.9%; on the 1356 questions that name no date the two retrievals return the same 81.4% coverage. The two cuts are shown as transcript-linked worked examples over one conversation; their set and process accuracy are not annotated and are not claimed.