---
title: 'REMORY: Learning Residual Memory for Context Compaction'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is CC BY 4.0 (verified on arXiv abs page 2026-11-11; icon by-4.0.png, http://creativecommons.org/licenses/by/4.0/); note is our own summary'
source: https://iinventedlight.com/library/notes/2610-11287-remory.md
source_license: 'CC BY 4.0'
attribution: 'Summary by iinventedlight (AI-assisted). Source: Hanchen Xia, Baoyou Chen, Yutang Ge, Naihao Deng, Senqiao Yang, Zilong Dong, Weihao Yuan, Siyu Zhu, REMORY: Learning Residual Memory for Context Compaction (CC BY 4.0).'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:07 PT, Librarian chat widget t1715u: \"okay you can publish the ones you're comfortable with\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Not reproduced. Abs page and HTML key sections read 2026-11-11. Named code URLs checked for HTTP 200; code not run.'
category: arxiv
topic_area: 'ai-research: agent memory and context compaction'
tags: agents, memory, compaction, arxiv
arxiv_id: 2610.11287
alias: 2610.11287-remory
sources:
  - id: S1
    title: 'REMORY: Learning Residual Memory for Context Compaction'
    publisher: arXiv
    authors: 'Hanchen Xia, Baoyou Chen, Yutang Ge, Naihao Deng, Senqiao Yang, Zilong Dong, Weihao Yuan, Siyu Zhu'
    url: https://arxiv.org/abs/2610.11287
    version_read: 'v3 (21 Oct 2026)'
    accessed: 2026-11-11
    license: 'CC BY 4.0'
---
# REMORY: Learning Residual Memory for Context Compaction

## Summary
Remory adds a learned residual memory: after a text summary, a network appends a bounded sequence of soft tokens meant to help a frozen model approximate the continuation it would have produced from the full history. On SummHay, the authors say this improves source attribution at nearly unchanged insight coverage and approaches the full-context joint score using only 5.2% of the input positions. [S1]

## Claims
- id: C1
  claim: 'Remory is a neural memory network for context compaction.'
  source: S1
  evidence: "We introduce Remory , a neural memory network for context compaction."
- id: C2
  claim: 'On SummHay they report approaching the full-context score at 5.2% of input positions.'
  source: S1
  evidence: "approaches the full-context joint score using only 5.2% of the input positions"
- id: C3
  claim: 'They report consistent gains with residual memory on long-horizon agent benchmarks.'
  source: S1
  evidence: "show consistent gains with residual memory"

## Verification
- Code available: No official Remory repository URL was found on the abs page or in the HTML text searched on 2026-11-11. Cited third-party repos were not treated as this paper's code.
- Reproduced: not reproduced (2026-11-11). Code not run.
- Related work: Live note ao-compaction-memory-and-parallel. Parallel Context Compaction in this batch changes how summaries are produced, not residual tokens.
- Limits: No official Remory code URL was found on the abs page or in the HTML text. Agent-benchmark gains for Qwen3.8-27B and GLM-5.3-Flash are described as consistent; details sit in figures.
- License: CC BY 4.0 on the abs page (2026-11-11; icon by-4.0.png; http://creativecommons.org/licenses/by/4.0/). Submitted 8 Oct 2026 (v1); last revised 21 Oct 2026 (v3). Sources: [S1] https://arxiv.org/abs/2610.11287, v3 (21 Oct 2026). checked_on: 2026-11-11.

## When to apply
Use when asking whether a short learned memory can supplement a readable summary after compaction, instead of relying on the summary alone.

## Facts
- id: F1
  claim: 'Remory generates a bounded sequence of soft memory tokens, conditioned on the summary and appended after it.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F2
  claim: 'On SummHay, the authors say Remory approaches the full-context joint score using only 5.2% of the input positions.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F3
  claim: 'They report consistent gains with residual memory for Qwen3.8-27B and GLM-5.3-Flash on long-horizon agent benchmarks.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0

## Sources
- [S1] arXiv 2610.11287, https://arxiv.org/abs/2610.11287. License: CC BY 4.0. v3 (21 Oct 2026). Accessed 2026-11-11.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted). Paper is CC BY 4.0.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).