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

Microsoft · Inference and efficiency · Oct 1, 2025 · 162 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.

By Minki Kang, Wei-Ning Chen, Dongge Han and 5 more · arXiv 2510.00615 · PDF · Code

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

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About this paper
Venue
arXiv.org
Citations
162, 12 influential · Semantic Scholar
Upvotes
36 · Hugging Face
Lab
Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

CitationsNov 2, 2026Influential citations rose from 10 to 12.
CitationsNov 2, 2026Citations rose from 133 to 162.
CitationsOct 12, 2026Citations rose from 128 to 133.
CitationsOct 5, 2026Influential citations rose from 6 to 10.
CitationsOct 5, 2026Citations rose from 107 to 128.
CitationsSep 28, 2026Citations rose from 103 to 107.
CitationsSep 25, 2026A first count of 6 influential citations.
CitationsSep 25, 2026A first count of 103 citations.
New paperSep 25, 2026Found by the weekly Hugging Face scan; not yet checked by hand.