Reconstructing the Right Episode: Evaluating Interleaved Conversational Memory Beyond Long Context
Conversations with chat assistants increasingly span many topics in a single long-running thread, challenging memory systems. Existing long-context and memory benchmarks often expose session or topic boundaries, or probe direct personal-memory questions. These settings understate a harder assistant-memory regime: a flat mixed-topic thread where the system must infer which earlier episode makes a later task decision valid. We introduce SCALE-QA, a constraint-grounded task QA benchmark for flat unsegmented threads targeting episode integrity failure. The dataset contains 3,000 audited questions
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- LinkedLinked via arxiv author · 85%Zhexi Feng →
“Reconstructing the Right Episode: Evaluating Interleaved Conversational Memory Beyond Long Context”
- LinkedLinked via arxiv author · 85%Ruiyi Zhang →
“Reconstructing the Right Episode: Evaluating Interleaved Conversational Memory Beyond Long Context”
- LinkedLinked via arxiv author · 85%Yongbo Yang →
“Reconstructing the Right Episode: Evaluating Interleaved Conversational Memory Beyond Long Context”
- LinkedLinked via arxiv author · 85%Pengtao Xie →
“Reconstructing the Right Episode: Evaluating Interleaved Conversational Memory Beyond Long Context”