arXiv Machine Learning
Retrieval Supersedes Scale: Efficient Log Level Prediction via Multiplex Socio-Technical Context
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arXiv AI
Jun 18
Improve Large Language Model Systems with User Logs
arXiv:2602. 06470v3 Announce Type: replace-cross Abstract: Scaling training data and model parameters has long driven progress in large language models (LLMs), but this paradigm is increasingly constrained by the scarcity of high-quality data and diminishing returns from rising computational costs.
arXiv Machine Learning
Oct 22
Code Comprehensor: Hierarchical Documentation and Cross-Modal Retrieval for Large Repositories
Code Comprehensor is a hybrid retrieval‑reasoning framework designed to aid understanding of large codebases. It combines a code dependency callgraph, CodeScribe—which generates hierarchical documentation using code‑aware LLMs—and CodeContrast, a CLIP‑style dual‑encoder that learns aligned text‑code embeddings for fast, semantically precise retrieval. Experiments on OpenCV and a 5M‑LOC proprietary repository show that CodeScribe‑derived supervision improves cross‑modal retrieval accuracy, and LLM‑based evaluation reports over 90% faithfulness.
arXiv AI
Aug 17
The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion
arXiv:2508. 16131v3 Announce Type: replace-cross Abstract: Code completion entails the task of providing missing tokens given a surrounding context.
arXiv AI
Jul 29
Agent Retrieval Bench: Evaluating Repository Context Retrieval for Coding Agents
arXiv:2607. 24882v1 Announce Type: cross Abstract: Modern coding agents are usually evaluated by whether they eventually produce a correct patch, but patch generation depends on an earlier context-acquisition stage: finding the repository files needed for the task.
arXiv AI
Sep 2
Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation
arXiv:2609.01601v1 Announce Type: cross Abstract: The repository-level code generation task requires synthesizing code that satisfies task requirements while remaining consistent with the target repo...
arXiv Machine Learning
Aug 31
RealSWE: A Compositional Evaluation of Coding Agents under Realistic User Requests
arXiv:2608.27831v1 Announce Type: cross Abstract: Coding agents are now commonly evaluated on the SWE-bench family of benchmarks, whose tasks are built from curated GitHub issues--long, structured, a...