LeanSearch v2 recovers 46.1% of ground-truth premise groups for research-level Lean 4 theorems within 10 candidates and raises fixed-loop proof success to 20%.
Benchmarking information retrieval models on complex retrieval tasks.arXiv preprint arXiv:2509.07253,
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An LLM simulation framework generates multilingual tip-of-the-tongue queries, validated by rank correlation with real queries, producing the first large-scale ToT benchmarks for four languages.
QPP methods can select query variants that boost end-to-end RAG quality over the original query, though retrieval-optimized variants often fail to produce the best generated answers, revealing a utility gap.
Reproducing GAR on BRIGHT shows it boosts reasoning-intensive retrieval effectiveness with low overhead when the reranker's signal quality is strong.
citing papers explorer
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LeanSearch v2: Global Premise Retrieval for Lean 4 Theorem Proving
LeanSearch v2 recovers 46.1% of ground-truth premise groups for research-level Lean 4 theorems within 10 candidates and raises fixed-loop proof success to 20%.
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Multilingual and Domain-Agnostic Tip-of-the-Tongue Query Generation for Simulated Evaluation
An LLM simulation framework generates multilingual tip-of-the-tongue queries, validated by rank correlation with real queries, producing the first large-scale ToT benchmarks for four languages.
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Can QPP Choose the Right Query Variant? Evaluating Query Variant Selection for RAG Pipelines
QPP methods can select query variants that boost end-to-end RAG quality over the original query, though retrieval-optimized variants often fail to produce the best generated answers, revealing a utility gap.
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Reproducing Adaptive Reranking for Reasoning-Intensive IR
Reproducing GAR on BRIGHT shows it boosts reasoning-intensive retrieval effectiveness with low overhead when the reranker's signal quality is strong.