arXiv Artificial Intelligence

DeepDiscovery: A Location-Inference Framework for Task-Level Repository Understanding

1 hour ago 15.09.2026 07:00 👁 1 views
DeepDiscovery: A Location-Inference Framework for Task-Level Repository Understanding

Quick summary

arXiv:2606.22906v2 Announce Type: replace-cross Abstract: Large language models have shown strong performance on software engineering (SE) tasks, yet understanding large industrial repositories remains challenging. Existing methods often retrieve only local fragments and fail to recover the broader task-relevant context needed for complex repository-level tasks. We present DeepDiscovery, a task-level repository-understanding method for large industrial codebases. DeepDiscovery uses a two-stage \textit{Location--Inference} framework to localize high-confidence task anchors and recover broader t

Key takeaways

  • arXiv:2606.22906v2 Announce Type: replace-cross Abstract: Large language models have shown strong performance on software engineering (SE) tasks, yet understanding large industrial repositories remains challenging.
  • Existing methods often retrieve only local fragments and fail to recover the broader task-relevant context needed for complex repository-level tasks.
  • We present DeepDiscovery, a task-level repository-understanding method for large industrial codebases.

Why it matters

“DeepDiscovery: A Location-Inference Framework for Task-Level Repository Understanding” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗