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Paper · arXiv 2607.25431

CodeNib: A Multi-View Data System for Serving Repository Context to Coding Agents

Zhongming Yu, Hengjia Yu, Boqin Yuan, Shuting Zhao, Yizhao Chen, Aryan Dokania, Mihir Jagtap, Jiayu Chang, Yitong Ma, Yash Jayswal, Wentao Ni, Hejia Zhang, Zhaoling Chen, Gangda Deng, Jishen Zhao

113 upvotesJuly 28, 2026arXiv 预印本
AI 摘要

CodeNib provides reusable multi-view repository indexing with faster updates and reduced context tokens while maintaining accurate navigation across code edits.

lexical viewsdense viewsstructural viewsrepository commitsource rangesranked searchsymbol navigationbounded contextgraph updatesvector updatesstatic-navigationlive-server latencytrajectory tokensmulti-view repository-context servingvalidity boundaries

Abstract

Coding agents repeatedly search, navigate, and retain context from evolving repositories, but disconnected indexes, language servers, and task-local histories force repeated discovery and obscure lifecycle costs. CodeNib builds reusable lexical, dense, and structural views per repository commit, maps outputs to repository-relative source ranges, maintains selected views across edits, and serves ranked search, symbol navigation, and bounded context through one runtime. Across 100 snapshots, we map quality-cost frontiers across the repository-context lifecycle. When outputs match an independent rebuild, graph and vector updates are 8.7times and 25.4times faster at the median. On the static-navigation subset matching normalized live-server locations (63% of 1,000 requests), the median per-request live/static latency ratio is 4.7times. Across five models, selected context policies preserve localization with 50--87% fewer trajectory tokens than paired grep/read. Together, these results support multi-view repository-context serving with explicit, operation-specific validity boundaries.

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