BEAVER is the first practical deterministic verifier that maintains sound probability bounds on LLM safety properties using token tries and frontier data structures, finding 2-3x more violations than sampling at 1/10 the compute.
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Gold-medalist performance in solving olympiad geometry with alphageometry2
11 Pith papers cite this work. Polarity classification is still indexing.
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Synthetic data improves models only in information-open generation-training loops with external signals, and coarser signals like binary correctness enable better generalization by converging to the most information-efficient component.
The topological dual of a dataset is introduced as a transformation that encodes logical structures into topological ones to expose invariants in neural latent spaces for AlphaGeometry-style reasoning.
A hybrid pipeline lets an LLM write high-level proof sketches in a compact DSL that a lightweight kernel then expands into explicit, checkable obligations for reliable math and logic reasoning.
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.
Aristotle reaches gold-medal-equivalent performance on 2025 IMO problems via integrated Lean proof search, informal lemma formalization, and a dedicated geometry solver.
KG-R1 trains a single RL agent to retrieve from and reason over knowledge graphs in one loop, achieving higher accuracy with fewer tokens than multi-module baselines and transferring to unseen graphs.
Riemann-Bench is a private benchmark of 25 research-level math problems on which all tested frontier AI models score below 10%.
Pass-rate rewards in critic-free RL for code generation fail to outperform binary rewards because partial-pass solutions induce conflicting gradient directions that do not consistently favor full correctness.
AI for math combines task-specific architectures and general foundation models to support research and advance AI reasoning capabilities.
AI4EOSC is a federated cloud platform that integrates modular AI development, serverless AI-as-a-Service, and distributed orchestration with built-in FAIR metadata and provenance tracking for scientific AI workloads in EOSC.
citing papers explorer
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BEAVER: An Efficient Deterministic LLM Verifier
BEAVER is the first practical deterministic verifier that maintains sound probability bounds on LLM safety properties using token tries and frontier data structures, finding 2-3x more violations than sampling at 1/10 the compute.
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An Information-Theoretic Criterion for Efficient Data Synthesis
Synthetic data improves models only in information-open generation-training loops with external signals, and coarser signals like binary correctness enable better generalization by converging to the most information-efficient component.
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The Topological Dual of a Dataset: A Logic-to-Topology Encoding for AlphaGeometry-Style Data
The topological dual of a dataset is introduced as a transformation that encodes logical structures into topological ones to expose invariants in neural latent spaces for AlphaGeometry-style reasoning.
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ProofSketcher: Hybrid LLM + Lightweight Proof Checker for Reliable Math/Logic Reasoning
A hybrid pipeline lets an LLM write high-level proof sketches in a compact DSL that a lightweight kernel then expands into explicit, checkable obligations for reliable math and logic reasoning.
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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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Aristotle: IMO-level Automated Theorem Proving
Aristotle reaches gold-medal-equivalent performance on 2025 IMO problems via integrated Lean proof search, informal lemma formalization, and a dedicated geometry solver.
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Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning
KG-R1 trains a single RL agent to retrieve from and reason over knowledge graphs in one loop, achieving higher accuracy with fewer tokens than multi-module baselines and transferring to unseen graphs.
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Riemann-Bench: A Benchmark for Moonshot Mathematics
Riemann-Bench is a private benchmark of 25 research-level math problems on which all tested frontier AI models score below 10%.
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Exploring Pass-Rate Reward in Reinforcement Learning for Code Generation
Pass-rate rewards in critic-free RL for code generation fail to outperform binary rewards because partial-pass solutions induce conflicting gradient directions that do not consistently favor full correctness.
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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.
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AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research
AI4EOSC is a federated cloud platform that integrates modular AI development, serverless AI-as-a-Service, and distributed orchestration with built-in FAIR metadata and provenance tracking for scientific AI workloads in EOSC.