newsReddit r/MachineLearningTrust 52 · CommunityPublished 5d agoLive · 4d ago
Is designing a memory graph around known data structure “overfitting” if I never touch the questions? [D]
building a missing data infrastructure and started benchmarking long multi-session conversations (LoCoMo). I know the data looks like: people, facts, claims, events, timestamps, relations. So I extract those into a graph. I did not look at the QA pairs while building extractors or retrieval rules. No “if question contains X, fetch fact #173.” Recall is very high and it keeps working on new conversations in the same format. Is this classical ove
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 67%CABLE: Extending the Reach of Memory Retrieval via Complementary Antecedent-Based Linking and Expansion →
- PossiblePossibly related (embedding) · 62%MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations →
- PossiblePossibly related (embedding) · 61%UTILMEM: Benchmarking Evidence Utilization in Long-Term Conversational Memory →
- PossiblePossibly related (embedding) · 61%Reconstructing the Right Episode: Evaluating Interleaved Conversational Memory Beyond Long Context →
- PossiblePossibly related (embedding) · 59%RUMBA: Russian User Memory Benchmark →
Covers
paperCABLE: Extending the Reach of Memory Retrieval via Complementary Antecedent-Based Linking and ExpansionpaperMemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon ConversationspaperUTILMEM: Benchmarking Evidence Utilization in Long-Term Conversational MemorypaperReconstructing the Right Episode: Evaluating Interleaved Conversational Memory Beyond Long ContextpaperRUMBA: Russian User Memory Benchmark
Related across the graph
paperMemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon ConversationspaperRUMBA: Russian User Memory BenchmarkpaperReconstructing the Right Episode: Evaluating Interleaved Conversational Memory Beyond Long ContextpaperUTILMEM: Benchmarking Evidence Utilization in Long-Term Conversational MemorypaperCABLE: Extending the Reach of Memory Retrieval via Complementary Antecedent-Based Linking and Expansion
