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FastContext: Training Efficient Repository Explorer for Coding Agents research paper by Microsoft, 2026

Microsoft · Jun 12, 2026 · Inference and efficiency · 3 citations · 94 upvotes · unverified

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What it shows

FastContext separates repository exploration from code solving in LLM agents using specialized exploration models that reduce token consumption and improve resolution rates.

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

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About this paper
Authors
Shaoqiu Zhang, Maoquan Wang, Yuling Shi and 5 more
arXiv
2606.14066 · PDF
Venue
arXiv.org
Citations
3, 0 influential · Semantic Scholar
Upvotes
94 · Hugging Face
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: 3Sep 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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