An RL-guided MCTS proof search for Tamarin finds more and shorter proofs than standard search across 16 protocol models.
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14 Pith papers cite this work. Polarity classification is still indexing.
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CAM-Bench is a new Lean 4 theorem-proving benchmark of 1,000 problems in computational and applied mathematics, built from textbook exercises using a dependency-recovery pipeline to reconstruct local context.
Re²Math is a new benchmark that evaluates AI models on retrieving and verifying the applicability of theorems from math literature to advance steps in partial proofs, accepting any sufficient theorem while controlling for leakage.
Multi-agent AI system formalizes entire 500-page graduate algebraic combinatorics textbook into Lean, creating 130K lines of code in one week at human-expert cost.
A Lean library called Palamedes uses synthesis rules from generator semantics and catamorphism-anamorphism rewriting to automatically produce correct constrained random generators.
LCPO trains L1 reasoning models to adhere to prompt-specified CoT lengths, supporting accuracy-compute trade-offs and yielding short reasoning models that outperform larger baselines at matched lengths.
Lean Refactor uses retrieval from a curated multi-objective strategy database to guide frozen LLMs in refactoring Lean proofs, reporting over 70% token compression on benchmarks and improved version transfer.
OProver-32B achieves top Pass@32 scores on MiniF2F, ProverBench, and PutnamBench by combining continued pretraining with iterative agentic proving, retrieval, SFT on repairs, and RL on unresolved cases using a 6.86M-proof dataset.
Segment-level supervision extracts coherent proof segments to train policy models that achieve 61-66% success on miniF2F, outperforming step-level and whole-proof methods while also improving existing provers.
CauSim turns scarce causal reasoning labels into scalable supervised data by having LLMs incrementally construct complex executable structural causal models.
A minimal agentic system achieves competitive performance in automated theorem proving with a simpler design and lower cost than state-of-the-art methods.
Ax-Prover is a tool-using multi-agent LLM system that matches state-of-the-art provers on public math benchmarks and outperforms them on new abstract-algebra and quantum-theory benchmarks while also assisting an expert with a cryptography proof.
Lack of exploration from conditioning on prior answers is the primary reason parallel sampling outperforms sequential sampling in large reasoning models.
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.
citing papers explorer
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Less Effort, Shorter Proofs: Reinforcement Learning for Security Protocol Analysis in Tamarin
An RL-guided MCTS proof search for Tamarin finds more and shorter proofs than standard search across 16 protocol models.
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CAM-Bench: A Benchmark for Computational and Applied Mathematics in Lean
CAM-Bench is a new Lean 4 theorem-proving benchmark of 1,000 problems in computational and applied mathematics, built from textbook exercises using a dependency-recovery pipeline to reconstruct local context.
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Re$^2$Math: Benchmarking Theorem Retrieval in Research-Level Mathematics
Re²Math is a new benchmark that evaluates AI models on retrieving and verifying the applicability of theorems from math literature to advance steps in partial proofs, accepting any sufficient theorem while controlling for leakage.
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Automatic Textbook Formalization
Multi-agent AI system formalizes entire 500-page graduate algebraic combinatorics textbook into Lean, creating 130K lines of code in one week at human-expert cost.
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The Search for Constrained Random Generators
A Lean library called Palamedes uses synthesis rules from generator semantics and catamorphism-anamorphism rewriting to automatically produce correct constrained random generators.
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L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning
LCPO trains L1 reasoning models to adhere to prompt-specified CoT lengths, supporting accuracy-compute trade-offs and yielding short reasoning models that outperform larger baselines at matched lengths.
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Lean Refactor: Multi-Objective Controllable Proof Optimization via Agentic Strategy Search
Lean Refactor uses retrieval from a curated multi-objective strategy database to guide frozen LLMs in refactoring Lean proofs, reporting over 70% token compression on benchmarks and improved version transfer.
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OProver: A Unified Framework for Agentic Formal Theorem Proving
OProver-32B achieves top Pass@32 scores on MiniF2F, ProverBench, and PutnamBench by combining continued pretraining with iterative agentic proving, retrieval, SFT on repairs, and RL on unresolved cases using a 6.86M-proof dataset.
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Rethinking Supervision Granularity: Segment-Level Learning for LLM-Based Theorem Proving
Segment-level supervision extracts coherent proof segments to train policy models that achieve 61-66% success on miniF2F, outperforming step-level and whole-proof methods while also improving existing provers.
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CauSim: Scaling Causal Reasoning with Increasingly Complex Causal Simulators
CauSim turns scarce causal reasoning labels into scalable supervised data by having LLMs incrementally construct complex executable structural causal models.
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A Minimal Agent for Automated Theorem Proving
A minimal agentic system achieves competitive performance in automated theorem proving with a simpler design and lower cost than state-of-the-art methods.
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Ax-Prover: A Deep Reasoning Agentic Framework for Theorem Proving in Mathematics and Quantum Physics
Ax-Prover is a tool-using multi-agent LLM system that matches state-of-the-art provers on public math benchmarks and outperforms them on new abstract-algebra and quantum-theory benchmarks while also assisting an expert with a cryptography proof.
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Understanding Performance Gap Between Parallel and Sequential Sampling in Large Reasoning Models
Lack of exploration from conditioning on prior answers is the primary reason parallel sampling outperforms sequential sampling in large reasoning models.
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Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.