# CodeNib：为编程智能体提供仓库上下文的多视图数据系统

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-07-28 08:00
- AIHOT 分数：47
- AIHOT 链接：https://aihot.virxact.com/items/cms5ytlz01142robk8gfotiko
- 原文链接：https://arxiv.org/abs/2607.25431

## AI 摘要

CodeNib 为每个仓库提交构建可复用的词法、稠密和结构视图，通过单一运行时提供排序搜索、符号导航和限定上下文。在 100 个快照上，图更新和向量更新中位数分别比独立重建快 8.7 倍和 25.4 倍；在静态导航子集上，每请求中位延迟比为 4.7 倍。跨五个模型，选定上下文策略比 grep/read 配对减少 50–87% 的轨迹 token。

## 正文

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 $8.7\times$ and $25.4\times$ 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 $4.7\times$. 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.