arXiv AI
DeepDiscovery: A Location-Inference Framework for Task-Level Repository Understanding
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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 AI
Sep 16
RepoAtlas: Guiding Coding Agents via Evolving Multimodal Repository Views
arXiv:2609.16936v1 Announce Type: cross Abstract: Large language model (LLM)-powered coding agents have made rapid progress in automating software engineering tasks, yet repository-level issue resolu...
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 Computation and Language
Sep 11
DeepResearch Bench II: Diagnosing Deep Research Agents via Rubrics from Expert Reports
Deep Research Bench II is a new benchmark designed to evaluate Deep Research Agents (DRAs) by requiring them to produce research reports for 132 grounded tasks across 22 domains. Each report is assessed using 9,430 fine‑grained binary rubrics that cover information recall, analysis, and presentation, all derived from expert‑written investigative articles through a rigorous LLM‑plus‑human pipeline. Evaluation of current state‑of‑the‑art DRAs shows that even the best models satisfy fewer than 50% of these rubrics, highlighting a significant gap between automated agents and human experts.
arXiv AI
Sep 1
Cost-Effective Repository Exploration for Agentic Issue Localization
arXiv:2608.29675v1 Announce Type: cross Abstract: Repository exploration is a distinct and costly stage of coding-agent pipelines: before generating a patch, an agent must identify which repository f...
arXiv Machine Learning
Jul 31
Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments
arXiv:2607. 28591v1 Announce Type: cross Abstract: Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation.