---
title: "Amory — paper by Zhou et al. (2026) — summary and ideas"
description: "The authors propose Amory, a working-memory framework grounded in cognitive science. 21 references."
authors: ["Yue Zhou", "Xiaobo Guo", "Belhassen Bayar", "Srinivasan H. Sengamedu"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.eacl-long.183/"
doi: "10.18653/v1/2026.eacl-long.183"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhou-2026-amory-building-coherent-narrative-driven/"
---
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# Amory: Building Coherent Narrative-Driven Agent Memory through Agentic Reasoning

**Authors:** [Yue Zhou](https://md.research.elamur.ai/free/en/authors/yue-zhou/), Xiaobo Guo, Belhassen Bayar and [Srinivasan H. Sengamedu](https://md.research.elamur.ai/free/en/authors/srinivasan-h-sengamedu/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-long.183/) · [PDF](https://aclanthology.org/2026.eacl-long.183.pdf) · [DOI 10.18653/v1/2026.eacl-long.183](https://doi.org/10.18653/v1/2026.eacl-long.183) · [OpenAlex](https://openalex.org/W7140130886)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary

Conversational agents that interact with users over weeks or months cannot afford to reread the whole dialogue history every turn. Most existing memory layers split dialogue into separate embeddings or graph nodes and fetch them in a RAG-like way, so memory construction stays shallow. The authors propose Amory, a working-memory framework grounded in cognitive science. Its heavy reasoning runs asynchronously, offline, where an LLM agent builds memory in three ways:

- it binds new dialogue fragments into episodic narratives (characters, a headline plot, and a tree of subplots and timestamped fragments);
- it consolidates those narratives guided by conversational momentum;
- it moves peripheral facts into a triple-based semantic memory.

When a query arrives, an agentic retriever judges which narratives are coherent with the question, instead of ranking them by embedding similarity. On the LOCOMO long-term reasoning benchmark, Amory clearly beats earlier memory systems. Its answers are close to full-context reasoning in quality, at roughly half the response time. This suggests that richer offline memory construction can deliver both quality and speed for real-time agents.

## Key points

- Combining episodic and semantic memory gives up to +27.8% absolute over Mem0 on LOCOMO. Other baselines include RAG, HippoRAG, A-Mem, ReadAgent and Zep.
- Inactive (momentum-aware) consolidation improves temporal reasoning compared with rapid consolidation or none.
- The coherence-driven retriever covers more relevant memory than an embedding retriever, especially on multi-hop questions. At top-k = 2 it typically uses under 3.7% of the conversation (median compression above 96.3%).
- For an agentic test, the authors converted 200 AgentIF instructions into one interleaved conversation of about 350,000 tokens. AgentIF itself contains 707 instructions from 50 applications.
- On this task, Amory recovers 47.4% of the constraints. The closest baseline is ReadAgent at 36.8%, followed by A-Mem (27.8), Mem0 (25.1), HippoRAG (23.9) and Zep (21.3).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [University of Illinois Chicago](https://md.research.elamur.ai/free/en/universities/university-of-illinois-chicago/) — University, Chicago, United States · 70 works · 20,363 citations

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## Cite

Cite as: Yue Zhou, Xiaobo Guo, Belhassen Bayar and Srinivasan H. Sengamedu. 2026. Amory: Building Coherent Narrative-Driven Agent Memory through Agentic Reasoning. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhou2026amory,
  title = {{Amory: Building Coherent Narrative-Driven Agent Memory through Agentic Reasoning}},
  author = {Yue Zhou, Xiaobo Guo, Belhassen Bayar and Srinivasan H. Sengamedu},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.183},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.183}
}
```

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## References

21 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*