AXLE is a multi-tenant cloud platform providing Lean 4 metaprogramming utilities with per-request isolation, multi-version support, and public access via SDK and API, having processed over 500 million requests.
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Hilbert: Recursively building formal proofs with informal reasoning
20 Pith papers cite this work. Polarity classification is still indexing.
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Goedel-Architect introduces blueprint generation and iterative refinement for Lean 4 theorem proving, reaching 99.2% on MiniF2F-test and 75.6% on PutnamBench with DeepSeek-V4-Flash.
A hypothesis-disciplined multi-agent pipeline in Lean 4 produces axiom-clean, source-faithful formalizations of parametric and semi-parametric asymptotic distribution and efficiency theorems.
An LLM-based agent with Lean verification autonomously solved multiple open Erdős problems and OEIS conjectures in the first large-scale test.
ProofRank benchmark shows substantial differences in LLM proof quality not captured by correctness, with trade-offs between quality metrics and accuracy.
LeetProof achieves higher rates of fully certified program synthesis from natural language by using a multi-modal verifier in Lean to validate specifications via randomized testing and delegate proofs to AI tools, outperforming single-mode baselines on benchmarks while uncovering defects in prior参考.
Lean Atlas visualizes Lean 4 dependency graphs and applies Lean Compass to reduce the nodes needing human semantic review by 27-99% across six evaluated projects.
LLM formal provers must shift from competition solvers to research agents that handle open-ended, under-specified frontier mathematics under machine-checked rigor.
Quarry splits Rocq proof automation into LLM-proposed decompositions and CoqHammer execution, ranking candidates by learned difficulty, improving success by 7–13 points.
Proof-Refactor is a four-phase agentic system that refactors LLM-generated Lean proofs from PutnamBench and Putnam2025 into more modular forms, outperforming a Claude Code baseline on rubric scores for signature quality and readability.
Agent-directed tree search improves LLM performance on Lean formal verification tasks, with context-based orchestration solving more intermediate specs at lower token cost than baseline agents.
A minimal agentic system achieves competitive performance in automated theorem proving with a simpler design and lower cost than state-of-the-art methods.
ContextPilot reduces LLM prefill latency by up to 3x via context indexing, ordering, de-duplication, and succinct annotations that maximize KV-cache reuse while preserving or improving reasoning quality.
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.
An agentic theorem prover in Lean uses a control plane to route actions based on cost and success estimates, achieving 28.9% lower average cost than a fixed-step baseline on a PutnamBench subset while preserving performance.
STAR-PólyaMath introduces a multi-agent framework with meta-strategic supervision and state-machine orchestration that reports state-of-the-art and perfect scores on eight top math competition benchmarks.
Rule-based annotation generation for ACSL outperforms LLM-based methods in achieving successful formal verification of C programs.
AI for math combines task-specific architectures and general foundation models to support research and advance AI reasoning capabilities.
citing papers explorer
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AXLE: A Cloud Infrastructure for Lean 4 Theorem Proving Utilities
AXLE is a multi-tenant cloud platform providing Lean 4 metaprogramming utilities with per-request isolation, multi-version support, and public access via SDK and API, having processed over 500 million requests.
-
Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement
Goedel-Architect introduces blueprint generation and iterative refinement for Lean 4 theorem proving, reaching 99.2% on MiniF2F-test and 75.6% on PutnamBench with DeepSeek-V4-Flash.
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Hypothesis-Disciplined Multi-Agent Automated Formalization of Asymptotic Statistical Theory
A hypothesis-disciplined multi-agent pipeline in Lean 4 produces axiom-clean, source-faithful formalizations of parametric and semi-parametric asymptotic distribution and efficiency theorems.
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Advancing Mathematics Research with AI-Driven Formal Proof Search
An LLM-based agent with Lean verification autonomously solved multiple open Erdős problems and OEIS conjectures in the first large-scale test.
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Not All Proofs Are Equal: Evaluating LLM Proof Quality Beyond Correctness
ProofRank benchmark shows substantial differences in LLM proof quality not captured by correctness, with trade-offs between quality metrics and accuracy.
-
Certified Program Synthesis with a Multi-Modal Verifier
LeetProof achieves higher rates of fully certified program synthesis from natural language by using a multi-modal verifier in Lean to validate specifications via randomized testing and delegate proofs to AI tools, outperforming single-mode baselines on benchmarks while uncovering defects in prior参考.
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Lean Atlas: An Integrated Proof Environment for Scalable Human-AI Collaborative Formalization
Lean Atlas visualizes Lean 4 dependency graphs and applies Lean Compass to reduce the nodes needing human semantic review by 27-99% across six evaluated projects.
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From Solvers to Research: Large Language Model-Driven Formal Mathematics at the Research Frontier
LLM formal provers must shift from competition solvers to research agents that handle open-ended, under-specified frontier mathematics under machine-checked rigor.
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Planning to Hammer: Difficulty-Aware Decomposition for Automating Rocq Proofs
Quarry splits Rocq proof automation into LLM-proposed decompositions and CoqHammer execution, ranking candidates by learned difficulty, improving success by 7–13 points.
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Proof-Refactor: Refactoring Generated Formal Proofs into Modular Artifacts
Proof-Refactor is a four-phase agentic system that refactors LLM-generated Lean proofs from PutnamBench and Putnam2025 into more modular forms, outperforming a Claude Code baseline on rubric scores for signature quality and readability.
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Automating Formal Verification with Agent-Guided Tree Search
Agent-directed tree search improves LLM performance on Lean formal verification tasks, with context-based orchestration solving more intermediate specs at lower token cost than baseline agents.
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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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ContextPilot: Fast Long-Context Inference via Context Reuse
ContextPilot reduces LLM prefill latency by up to 3x via context indexing, ordering, de-duplication, and succinct annotations that maximize KV-cache reuse while preserving or improving reasoning quality.
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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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Optimizing the Cost-Quality Tradeoff of Agentic Theorem Provers in Lean
An agentic theorem prover in Lean uses a control plane to route actions based on cost and success estimates, achieving 28.9% lower average cost than a fixed-step baseline on a PutnamBench subset while preserving performance.
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STAR-P\'olyaMath: Multi-Agent Reasoning under Persistent Meta-Strategic Supervision
STAR-PólyaMath introduces a multi-agent framework with meta-strategic supervision and state-machine orchestration that reports state-of-the-art and perfect scores on eight top math competition benchmarks.
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Evaluating LLM-Generated ACSL Annotations for Formal Verification
Rule-based annotation generation for ACSL outperforms LLM-based methods in achieving successful formal verification of C programs.
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AI for Mathematics: Progress, Challenges, and Prospects
AI for math combines task-specific architectures and general foundation models to support research and advance AI reasoning capabilities.
- Beyond the Library: An Agentic Framework for Autoformalizing Research Mathematics
- Scaling Self-Play with Self-Guidance