---
title: 'ACON: Optimizing Context Compression for Long-horizon LLM Agents'
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/2510-00615-acon.md
source_license: 'CC BY 4.0'
attribution: 'Summary by iinventedlight (AI-assisted). Source: Minki Kang, Wei-Ning Chen, Dongge Han, Huseyin A. Inan, Lukas Wutschitz, Yanzhi Chen, Robert Sim, Saravan Rajmohan, ACON: Optimizing Context Compression for Long-horizon LLM Agents (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: 2510.00615
alias: 2510.00615-acon
sources:
  - id: S1
    title: 'ACON: Optimizing Context Compression for Long-horizon LLM Agents'
    publisher: arXiv
    authors: 'Minki Kang, Wei-Ning Chen, Dongge Han, Huseyin A. Inan, Lukas Wutschitz, Yanzhi Chen, Robert Sim, Saravan Rajmohan'
    url: https://arxiv.org/abs/2510.00615
    version_read: 'v3 (1 Jun 2026)'
    accessed: 2026-11-11
    license: 'CC BY 4.0'
---
# ACON: Optimizing Context Compression for Long-horizon LLM Agents

## Summary
Acon (also written ACON) optimizes context compression for long-horizon agents through compression-guideline optimization and compressor distillation. The authors report a 26-54% reduction in peak token usage on AppWorld, OfficeBench, and Multi-objective QA, with better task success than existing compression baselines, and up to 46% performance improvement for smaller models by reducing context distraction. [S1]

## Claims
- id: C1
  claim: 'They report a 26-54% reduction in peak token usage.'
  source: S1
  evidence: "Acon reduces peak token usage by 26–54%"
- id: C2
  claim: 'They report up to 46% performance improvement for smaller models.'
  source: S1
  evidence: "achieving up to 46% performance improvement by mitigating context distraction"
- id: C3
  claim: 'They state that code is available at a public Microsoft repository.'
  source: S1
  evidence: "Our code is available at https://github.com/microsoft/acon"

## Verification
- Code available: Paper: https://github.com/microsoft/acon (HTTP 200 on 2026-11-11). Not run.
- Reproduced: not reproduced (2026-11-11). Code not run.
- Related work: Live note ao-compaction-summarize-and-resume. ReSum in this batch is a related summarize-and-continue method for search agents.
- Limits: Author-reported on the named benchmarks. The 46% figure is for smaller models in their setup, not a general guarantee.
- 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 1 Oct 2025 (v1); last revised 1 Jun 2026 (v3). Comments: ICML 2026. Sources: [S1] https://arxiv.org/abs/2510.00615, v3 (1 Jun 2026). checked_on: 2026-11-11.

## When to apply
Use when asking how to compress long agent histories while keeping task success, including when a smaller model is the agent.

## Facts
- id: F1
  claim: 'Acon reduces peak token usage by 26-54% on AppWorld, OfficeBench, and Multi-objective QA, while improving task success over existing compression baselines, according to the authors.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F2
  claim: 'They report up to 46% performance improvement for smaller models by mitigating context distraction.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F3
  claim: 'The authors state that their code is available at https://github.com/microsoft/acon.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0

## Sources
- [S1] arXiv 2510.00615, https://arxiv.org/abs/2510.00615. License: CC BY 4.0. v3 (1 Jun 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).