@inproceedings{lv-etal-2026-calibrating,
title = "Calibrating Hierarchical Memory Utilization for Long-Term Memory {QA}",
author = "Lv, Hang and
Zhi, Xuyang and
Gu, Hongchao and
Wang, Hao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1927/",
pages = "41072--41081",
ISBN = "979-8-89176-462-0",
abstract = "Long-term memory systems increasingly organize interaction histories into hierarchical contexts, where coarse summaries, mid-level events, and fine-grained evidence provide complementary signals for future queries. Yet after retrieval, these signals are often collapsed into a single generation context and used implicitly. We ask how retrieved hierarchical memory should shape generation after access, framing it as a post-retrieval calibration problem. We propose HiR, a training-free inference-time framework that contrasts full-hierarchy decoding with a coarse-only reference view, preserving the complete hierarchy while tempering over-reliance on broad abstractions. Extensive experiments and analyses show that HiR enables more effective and robust use of hierarchical memory without changing memory construction or retrieval; more broadly, our results suggest that memory research should consider not only what to store or retrieve, but also how retrieved information shapes generation."
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<abstract>Long-term memory systems increasingly organize interaction histories into hierarchical contexts, where coarse summaries, mid-level events, and fine-grained evidence provide complementary signals for future queries. Yet after retrieval, these signals are often collapsed into a single generation context and used implicitly. We ask how retrieved hierarchical memory should shape generation after access, framing it as a post-retrieval calibration problem. We propose HiR, a training-free inference-time framework that contrasts full-hierarchy decoding with a coarse-only reference view, preserving the complete hierarchy while tempering over-reliance on broad abstractions. Extensive experiments and analyses show that HiR enables more effective and robust use of hierarchical memory without changing memory construction or retrieval; more broadly, our results suggest that memory research should consider not only what to store or retrieve, but also how retrieved information shapes generation.</abstract>
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%0 Conference Proceedings
%T Calibrating Hierarchical Memory Utilization for Long-Term Memory QA
%A Lv, Hang
%A Zhi, Xuyang
%A Gu, Hongchao
%A Wang, Hao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lv-etal-2026-calibrating
%X Long-term memory systems increasingly organize interaction histories into hierarchical contexts, where coarse summaries, mid-level events, and fine-grained evidence provide complementary signals for future queries. Yet after retrieval, these signals are often collapsed into a single generation context and used implicitly. We ask how retrieved hierarchical memory should shape generation after access, framing it as a post-retrieval calibration problem. We propose HiR, a training-free inference-time framework that contrasts full-hierarchy decoding with a coarse-only reference view, preserving the complete hierarchy while tempering over-reliance on broad abstractions. Extensive experiments and analyses show that HiR enables more effective and robust use of hierarchical memory without changing memory construction or retrieval; more broadly, our results suggest that memory research should consider not only what to store or retrieve, but also how retrieved information shapes generation.
%U https://aclanthology.org/2026.findings-emnlp.1927/
%P 41072-41081
Markdown (Informal)
[Calibrating Hierarchical Memory Utilization for Long-Term Memory QA](https://aclanthology.org/2026.findings-emnlp.1927/) (Lv et al., Findings 2026)
ACL