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AutoSaddler: Automatic Harness Optimization with Durable Updates from Agent Execution Traces research paper by Microsoft, 2026

Microsoft · Aug 24, 2026 · Agents and evaluation · 2 citations · 63 upvotes · unverified

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What it shows

AutoSaddler automatically improves LLM agent harnesses via offline failure-driven optimization, boosting performance on long-horizon benchmarks.

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

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About this paper
Authors
Sungho Park, Wonjoong Kim, Rongyuan Tan and 10 more
arXiv
2608.23041 · PDF
Citations
2, 0 influential · Semantic Scholar
Upvotes
63 · Hugging Face
Code
github.com/microsoft/AutoSaddler
Lab
Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 2Sep 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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