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LongCodeZip: Compress Long Context for Code Language Models research paper by Stanford University, 2025

Stanford University · Oct 1, 2025 · Inference and efficiency · 47 citations · 77 upvotes · unverified

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What it shows

LongCodeZip is a code compression framework for LLMs that uses dual-stage compression to reduce context size without degrading performance, improving efficiency in code intelligence applications.

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

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About this paper
Authors
Yuling Shi, Yichun Qian, Hongyu Zhang and 2 more
arXiv
2510.00446 · PDF
Venue
International Conference on Automated Software Engineering
Citations
47, 7 influential · Semantic Scholar
Upvotes
77 · Hugging Face
Code
github.com/YerbaPage/LongCodeZip
Lab
Stanford University

Changes

What changed
Influential citationsfirst count: 7Sep 25, 2026
Citationsfirst count: 47Sep 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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