Hugging Face Trending Papers

MediaWiki Code2Code Search: Neural Retrieval for the Semantic Discovery of Open-Source Software Entities

Read the original on Hugging Face Trending Papers →

Code search in large-scale ecosystems is often hindered by the lexical gap between user queries and implementation details, alongside the trade-off between the low latency of traditional Information Retrieval (IR) and the precision of Deep Learning (DL). We present MediaWiki Code2Code Search, a neural retrieval system for semantic code-to-code discovery.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv AI
Jul 8

Scientific Code Search at Scale: A Multi-Domain Dataset and Benchmark

arXiv:2607. 05443v1 Announce Type: cross Abstract: Scientists increasingly rely on open-source tools to support their research workflows, yet discovering relevant software among over 600 million GitHub repositories remains challenging.

By Nishan Pantha, Pranath Reddy Kumbam, Sajil Awale, Pushwitha Krishnappa, Muthukumaran Ramasubramanian, Nidhi Jha, Emily Foshee, Ankur Kumar, Rachel Slank, Ashkbiz Danehkar, Rahul Ramachandran
arXiv AI
Jul 31

SimpleWikiSearch: A Clean Offline Wikipedia Environment for Agentic Search

arXiv:2607. 26070v1 Announce Type: cross Abstract: Large language model (LLM)-based agentic search systems are often evaluated as if the underlying LLM were the only component that matters, yet their measured performance also depends on the surrounding search environment: the Wikipedia snapshot, preprocessing pipeline, chunking policy, retrieval backend, tool schema, observation format, and answer submission rule.

By Guanming Xiong, Penghui Zhang