@inproceedings{ferreira-etal-2026-episodic,
title = "Episodic Memory from Compression Boundaries in Latent Representation Space",
author = "Ferreira, David Oneil Campos and
Ribeiro, Priscila Rocha Maia Freitas and
Passinato, Emanuel Borges and
Silva, Diogo Fernandes Costa and
Galv{\~a}o Filho, Arlindo Rodrigues",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.19/",
pages = "364--385",
ISBN = "979-8-89176-439-2",
abstract = "Long-term memory in Large Language Model (LLM) agents requires selective persistence: only a subset of interactions should be consolidated beyond the current context window. Existing memory systems rely on heuristic importance rules or similarity-based novelty, which remain external to the model{'}s internal computation. We propose a geometric principle for memory formation: episodic memory can emerge from compression failure in latent representation space. We approximate the manifold of routine LLM activations using Sparse Autoencoders (SAEs) and define \textit{representational surprise} as reconstruction error relative to this learned manifold. Deviations from routine structure yield elevated residuals, providing an unsupervised, model-internal signal for memory writing. We demonstrate this principle through ReSuME, a surprise-gated memory mechanism that commits turns to memory only when normalized reconstruction error exceeds a calibrated threshold. In long-horizon multi-turn dialogue settings, representational surprise separates routine, critical, and out-of-distribution states, and achieves a superior performance{--}memory trade-off against established memory-agent systems (MemGPT, EM-LLM) and unsupervised anomaly detectors, while operating without labels and at substantially lower inference cost than LLM-judge controllers. Covariance-aware normalization further enables robust cross-domain calibration. These results suggest that episodic memory gating in neural agents can be grounded in intrinsic latent geometry rather than externally engineered rules."
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<abstract>Long-term memory in Large Language Model (LLM) agents requires selective persistence: only a subset of interactions should be consolidated beyond the current context window. Existing memory systems rely on heuristic importance rules or similarity-based novelty, which remain external to the model’s internal computation. We propose a geometric principle for memory formation: episodic memory can emerge from compression failure in latent representation space. We approximate the manifold of routine LLM activations using Sparse Autoencoders (SAEs) and define representational surprise as reconstruction error relative to this learned manifold. Deviations from routine structure yield elevated residuals, providing an unsupervised, model-internal signal for memory writing. We demonstrate this principle through ReSuME, a surprise-gated memory mechanism that commits turns to memory only when normalized reconstruction error exceeds a calibrated threshold. In long-horizon multi-turn dialogue settings, representational surprise separates routine, critical, and out-of-distribution states, and achieves a superior performance–memory trade-off against established memory-agent systems (MemGPT, EM-LLM) and unsupervised anomaly detectors, while operating without labels and at substantially lower inference cost than LLM-judge controllers. Covariance-aware normalization further enables robust cross-domain calibration. These results suggest that episodic memory gating in neural agents can be grounded in intrinsic latent geometry rather than externally engineered rules.</abstract>
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%0 Conference Proceedings
%T Episodic Memory from Compression Boundaries in Latent Representation Space
%A Ferreira, David Oneil Campos
%A Ribeiro, Priscila Rocha Maia Freitas
%A Passinato, Emanuel Borges
%A Silva, Diogo Fernandes Costa
%A Galvão Filho, Arlindo Rodrigues
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F ferreira-etal-2026-episodic
%X Long-term memory in Large Language Model (LLM) agents requires selective persistence: only a subset of interactions should be consolidated beyond the current context window. Existing memory systems rely on heuristic importance rules or similarity-based novelty, which remain external to the model’s internal computation. We propose a geometric principle for memory formation: episodic memory can emerge from compression failure in latent representation space. We approximate the manifold of routine LLM activations using Sparse Autoencoders (SAEs) and define representational surprise as reconstruction error relative to this learned manifold. Deviations from routine structure yield elevated residuals, providing an unsupervised, model-internal signal for memory writing. We demonstrate this principle through ReSuME, a surprise-gated memory mechanism that commits turns to memory only when normalized reconstruction error exceeds a calibrated threshold. In long-horizon multi-turn dialogue settings, representational surprise separates routine, critical, and out-of-distribution states, and achieves a superior performance–memory trade-off against established memory-agent systems (MemGPT, EM-LLM) and unsupervised anomaly detectors, while operating without labels and at substantially lower inference cost than LLM-judge controllers. Covariance-aware normalization further enables robust cross-domain calibration. These results suggest that episodic memory gating in neural agents can be grounded in intrinsic latent geometry rather than externally engineered rules.
%U https://aclanthology.org/2026.aacl-long.19/
%P 364-385
Markdown (Informal)
[Episodic Memory from Compression Boundaries in Latent Representation Space](https://aclanthology.org/2026.aacl-long.19/) (Ferreira et al., AACL-IJCNLP 2026)
ACL
- David Oneil Campos Ferreira, Priscila Rocha Maia Freitas Ribeiro, Emanuel Borges Passinato, Diogo Fernandes Costa Silva, and Arlindo Rodrigues Galvão Filho. 2026. Episodic Memory from Compression Boundaries in Latent Representation Space. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 364–385, Hengqin, China. Association for Computational Linguistics.