Small 7B reasoning models were fine-tuned on synthetic and curated QFT problems using RL and SFT, yielding performance gains, error analysis, and public release of data and traces.
Ax-prover: A deep reasoning agentic framework for theorem proving in mathematics and quantum physics
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NeuroClaw introduces a three-tier multi-agent framework and NeuroBench benchmark that improve executability and reproducibility scores for neuroimaging tasks when used with multimodal LLMs.
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.
Frontier LLMs with in-context learning and CAS integration solve most algorithmic tasks in theoretical physics when supplied with worked examples.
citing papers explorer
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Fine-Tuning Small Reasoning Models for Quantum Field Theory
Small 7B reasoning models were fine-tuned on synthetic and curated QFT problems using RL and SFT, yielding performance gains, error analysis, and public release of data and traces.
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NeuroClaw Technical Report
NeuroClaw introduces a three-tier multi-agent framework and NeuroBench benchmark that improve executability and reproducibility scores for neuroimaging tasks when used with multimodal LLMs.
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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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LLMs with in-context learning for Algorithmic Theoretical Physics
Frontier LLMs with in-context learning and CAS integration solve most algorithmic tasks in theoretical physics when supplied with worked examples.
- Automated Conjecture Resolution with Formal Verification