Temporal Validity on Real Software Histories: Eliminating Stale-Fact Errors in Code-Assistant Memory over GitHub Fixes
Organizations: MemStrata.dev — Called It Inc. (Enterprise)
Abstract
Retrieval-augmented generation (RAG) has no model of time: when a fact changes across a coding session - a function is renamed, an endpoint moves, a dependency is bumped - RAG retrieves both the old and new value with near-identical similarity and cannot tell which is current, so it serves the superseded value. Paper 1 showed, on synthetic single-value benchmarks, that a deterministic (subject, relation, object) supersession memory eliminates this failure. Here we validate it end-to-end on real software history. From 707 real GitHub issues (SWE-bench Lite + Verified) we extract 130 clean atomic state transitions, a fix that changes one identifiable value from a pre-fix to a post-fix form, and render each marker-free (the stale and current statements differ only in the value). On this set, MemStrata reaches 0.91 answer accuracy versus RAG's 0.57-0.59; and, the structural result, when forced to answer RAG serves the superseded value 36-38% of the time (an LLM reranker does not help) while MemStrata drives this to ~0, at RAG retrieval latency (~2.1 s vs ~18 s for the reranker). We are explicit about scope: only ~18% of real fixes are clean atomic transitions; Paper 2 isolates the memory mechanism on that class, and extraction coverage of the remaining fixes is the orthogonal problem we defer to follow-on work. A real product bug surfaced and was fixed during the study (a case/punctuation-insensitive value comparison), with the moat property (deterministic-supersession accuracy on clean code mutations) preserved and verified.
Figures & tables
| metric | naive_rag | advanced_rag | temporal_v6 |
|---|---|---|---|
| accuracy — allowed | 0.569 | 0.585 | 0.908 |
| accuracy — forced | 0.615 | 0.592 | 0.985 |
| stale-fact-error — allowed | 0.262 | 0.262 | 0.023 |
| stale-fact-error — forced | 0.361 | 0.377 | 0.00 |
| conditional fabrication — allowed | 0.290 | 0.291 | 0.168 |
| conditional fabrication — forced | 0.654 | 0.651 | 0.341 |
Agent Memory Management
Agent memory management studies how AI agents store, update, retrieve, compress, and forget information across long-running tasks.
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