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    <abstract>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 across 10 domains, uses deterministic four-way multiple-choice grading, and includes a deterministic runtime builder; experiments use all 3,000 questions through 128k and a stratified 400-question diagnostic at 1M. SCALE-QA questions are ordinary task-oriented requests whose correct answer depends on causally related evidence introduced earlier in the conversation. We also propose Temporal-Semantic Interleaved Memory Reconstruction (TSIM), which segments the turn stream into coherent episodes and indexes them through a hierarchical multi-view memory stack with deterministic episode-level summary and cluster-routing views. Experiments show that SCALE-QA challenges strong RAG baselines and long-context LLMs alike; across three open-source and proprietary LLM backends, TSIM achieves the highest accuracy in every backend setting, gaining 5.6–17.6 accuracy points over the strongest corresponding baseline.</abstract>
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%0 Conference Proceedings
%T Reconstructing the Right Episode: Evaluating Interleaved Conversational Memory Beyond Long Context
%A Feng, Zhexi
%A Zhang, Ruiyi
%A Yang, Yongbo
%A Xie, Pengtao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F feng-etal-2026-reconstructing
%X 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 across 10 domains, uses deterministic four-way multiple-choice grading, and includes a deterministic runtime builder; experiments use all 3,000 questions through 128k and a stratified 400-question diagnostic at 1M. SCALE-QA questions are ordinary task-oriented requests whose correct answer depends on causally related evidence introduced earlier in the conversation. We also propose Temporal-Semantic Interleaved Memory Reconstruction (TSIM), which segments the turn stream into coherent episodes and indexes them through a hierarchical multi-view memory stack with deterministic episode-level summary and cluster-routing views. Experiments show that SCALE-QA challenges strong RAG baselines and long-context LLMs alike; across three open-source and proprietary LLM backends, TSIM achieves the highest accuracy in every backend setting, gaining 5.6–17.6 accuracy points over the strongest corresponding baseline.
%U https://aclanthology.org/2026.emnlp-main.1550/
%P 35367-35385

Markdown (Informal)

[Reconstructing the Right Episode: Evaluating Interleaved Conversational Memory Beyond Long Context](https://aclanthology.org/2026.emnlp-main.1550/) (Feng et al., EMNLP 2026)

• Reconstructing the Right Episode: Evaluating Interleaved Conversational Memory Beyond Long Context (Feng et al., EMNLP 2026)

ACL

• Zhexi Feng, Ruiyi Zhang, Yongbo Yang, and Pengtao Xie. 2026. Reconstructing the Right Episode: Evaluating Interleaved Conversational Memory Beyond Long Context. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 35367–35385, Budapest, Hungary. Association for Computational Linguistics.