Hugging Face Trending Papers
Lean Finder V2: Semantic Search for Mathlib That Disambiguates User Queries
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arXiv Machine Learning
1d ago
Lean Finder V2: Semantic Search for Mathlib That Disambiguates User Queries
arXiv:2610.30843v1 Announce Type: new Abstract: We introduce Lean Finder V2, a semantic search framework for Mathlib that disambiguates user queries through complementary model-level adversarial trai...
arXiv Machine Learning
Oct 16
Lean Graph Search for Formal Theorem Proving
LeanGraphSearch is a graph‑based retrieval method for the Lean 4 theorem prover that builds a dependency graph from Mathlib’s forward dependencies and traverses it to find supporting lemmas via multi‑hop chains. By linking each declaration to its premises, propagating relevance through a graph‑ranking algorithm, and re‑ranking candidates against the original query, it improves recall by up to 12% over LeanSearch v2 on MathlibQR and increases coverage of complete annotated proof routes on MathlibMPR by up to 3.7%. When integrated into an iterative proving loop, LeanGraphSearch raises pass rates at 8, 16, and 32 reflection turns by as much as 40% compared to a vanilla prover and up to 10% compared to LeanSearch v2, demonstrating higher accuracy‑cost efficiency.
arXiv Machine Learning
Jul 30
MathNet: a Global Multimodal Benchmark for Mathematical Reasoning and Retrieval
arXiv:2604. 18584v2 Announce Type: replace-cross Abstract: Mathematical problem solving remains a challenging test of reasoning for large language and multimodal models, yet existing benchmarks are limited in size, language coverage, and task diversity.
arXiv Computation and Language
Sep 10
Benchmarking Hybrid Deep Research Across Database Querying and Web Search
arXiv:2609.09410v1 Announce Type: new Abstract: While autonomous agents have made significant strides in "deep research" by iteratively navigating the open web to synthesize information, real-world p...
arXiv AI
Aug 24
Clarify-Then-Search: A Clarification Benchmark for Deep Search with End-to-End Nugget Restoration
Clarify-Then-Search is a benchmark that tests whether large language models can ask clarification questions to improve the usefulness of deep search results. It uses 518 real-world query pairs from Baidu, where each intent query is paired with an underspecified version. The evaluation involves a clarifier asking up to three questions, a user answerer providing only explicit information, and a rewriter generating a new query that is then searched; performance is measured by a weighted nugget-recall score.
arXiv AI
Oct 16
Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries
The paper introduces Librarian Agents, a framework that trains large language models to proactively curate long-context workspaces by reorganizing raw documents into a hierarchical filesystem of synthetic files and reference links. This builder agent, optimized via reinforcement learning, aims to maximize downstream answering accuracy by anticipating future queries rather than reacting to them. Experiments show that Librarian Agents outperform leading retrieval and precomputation methods, achieving a 33‑point accuracy gain on BrowseComp‑Plus and a 31‑point improvement on BRIGHT in zero‑shot settings.