AI & ML Papers
33.4K subscribers
7.17K photos
556 videos
24 files
7.87K links
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.

Admin: @HusseinSheikho || @Hussein_Sheikho
Download Telegram
🔥 CodeNib: A Multi-View Data System for Serving Repository Context to Coding Agents

💡 The paper introduces CodeNib, a multi-view data system designed to provide repository context to coding agents. The problem addressed is that 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 life cycle 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.

The method involves creating a system that can efficiently serve context to coding agents by building and maintaining multiple views of the repository. The system is evaluated across 100 snapshots, mapping quality-cost frontiers across the repository-context life cycle. The results show that when outputs match an independent rebuild, graph and vector updates are 8.7 times and 25.4 times faster at the median.

The system is also evaluated on a static-navigation subset matching normalized live-server locations, where the median per-request live/static latency ratio is 4.7 times. Additionally, the results show that selected context policies preserve localization with 50-87 percent fewer trajectory tokens than paired grep/read. Overall, the results support multi-view repository-context serving with explicit, operation-specific validity boundaries, providing a solution to the problem of efficiently serving context to coding agents.


📅 Published on Jul 28

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2607.25431
• PDF: https://arxiv.org/pdf/2607.25431
• Project Page: https://codenib.ai

📊 Datasets citing this paper:
https://huggingface.co/datasets/sysevol-ai/codenib-synthesis

━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://t.iss.one/PaperNexus

#CodeAnalysisTools #MultiViewDataSystems #CodingAgents #RepositoryContext #SoftwareDevelopmentTools