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ACON: Optimizing Context Compression for Long-horizon LLM Agents research paper by Microsoft, 2025

Microsoft · Oct 1, 2025 · Inference and efficiency · 103 citations · 36 upvotes · unverified

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What it shows

Agent Context Optimization (ACON) compresses context in large language models for efficient long-horizon tasks by analyzing failure cases and distilling the compressor into smaller models.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Minki Kang, Wei-Ning Chen, Dongge Han and 5 more
arXiv
2510.00615 · PDF
Venue
arXiv.org
Citations
103, 6 influential · Semantic Scholar
Upvotes
36 · Hugging Face
Code
github.com/microsoft/acon
Lab
Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

What changed
Influential citationsfirst count: 6Sep 25, 2026
Citationsfirst count: 103Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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