Temporal Validity on Real Software Histories: Eliminating Stale-Fact Errors in Code-Assistant Memory over GitHub Fixes
On the map: Retrieval-augmented generation → RAG for code
Abstract (arXiv)
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retrieves both the old and new value with near-identical similarity and cannot tell which is current, so it serves the superseded value.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reaches 0.91 answer accuracy versus RAG's 0.57-0.59; and, the structural result, when forced to answer RAG serves the superseded value 36-38% of the time (an LLM reranker does not help) while MemStrata drives this to ~0, at RAG retrieval latency (~2.1 s vs ~18 s for the reranker).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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG for code, one of 15 directions in Retrieval-augmented generation. Builds on 7 of its 7 references that are in the map. First posted on arXiv 21 Aug 2026.
Builds on
7 of 7 references.
References · 7
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
- 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
- 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 800
- 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memorycited by 722
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026When Retrieval Hurts Code Completion: A Diagnostic Study of Stale Repository Context
- 2026MemTrace: State-Consistent Memory for Long-Horizon Coding Agents
- 2024A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency Validation
- 2026VibeMemBench: Evaluating Memory Systems for Coding Agents on Real Repository Coding Tasks
- 2026Not All RAGs Are Created Equal: A Component-Wise Empirical Study for Software Engineering Tasks
- 2026NuggetIndex: Governed Atomic Retrieval for Maintainable RAG
- 2026AtomicCommitBench: Can Coding Agents Reconstruct Commit Histories from Squashed Patches?
- 2026Beyond "What to Retrieve": Uncertainty in Retrieval-Augmented Code Generation