Read the Order, Don't Extract It: Deriving Agent Memory Structure from the Bytes

LLM agents depend on memory that is organized and served in real time. Their stores differ in what they already record: logs number their lines, example banks delimit their records, and prose does neither. Current systems either fix one configuration for every store or pay a language model to rebuild structure at write time. We introduce MUSTER, which reads that structure from the raw bytes instead. Two query-independent statistics, the share of lines that open with a serial marker and the share of text inside bounded records, set the retrieval unit, the parent hierarchy, whether an order may be used, and how retrieval slots are shared across channels. No generative model runs at write time, so a memory is ready in seconds as the store grows, and a query costs one ranking pass and one reader call. On MemoryAgentBench the same rule configures thirteen of fourteen stores in three seconds, and MUSTER scores 57.0, 60.1 and 64.0 at GPT-4o-mini, GPT-4.1-mini and GPT-5-mini. At the benchmark's GPT-4o-mini reader it is first among the retrieval and memory systems reported, 15.4 points above the strongest, and at GPT-4.1-mini it is 13.2 points above reading the whole store in context. On BEAM from 100K to 10M tokens, the rule's 512-token units score 18.6 points above unrefined 4096-token chunks at 100K, and MUSTER matches the strongest model-built memory under our main reader (74.9, 72.0 and 64.2 against 75.4, 72.3 and 62.4) while building each 10M-token memory in 36 seconds instead of 13 hours and 10,429 model calls, leads a hosted memory service by 7.8 to 15.3 points, and, read together with a model-built memory at that memory's write-time cost, scores above both. Agent memory can thus keep pace with the conversation and sit beneath or alongside model-built memories.