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cs.AI2026

Pythagoras-Prover: Advancing Efficient Formal Proving via Augmented Lean Formalisation

Joshua Ong Jun Leang, Zheng Zhao, Mihaela Cătălina Stoian +5

Modern Lean theorem provers achieve strong performance only with substantial training and inference compute, driven in part by scarce verified proof data and the long reasoning tra…

cs.AI2026

Can I Have Your Order? Monte-Carlo Tree Search for Slot Filling Ordering in Diffusion Language Models

Joshua Ong Jun Leang, Yu Zhao, Mihaela Cătălina Stoian +3

While plan-and-infill decoding in Masked Diffusion Models (MDMs) shows promise for mathematical and code reasoning, performance remains highly sensitive to slot infilling order, of…

cs.AI2026

Neural Theorem Proving for Verification Conditions: A Real-World Benchmark

Qiyuan Xu, Xiaokun Luan, Renxi Wang +5

Theorem proving is fundamental to program verification, where the automated proof of Verification Conditions (VCs) remains a primary bottleneck. Real-world program verification fre…

cs.AI2026

Theorem Prover as a Judge for Synthetic Data Generation

Joshua Ong Jun Leang, Giwon Hong, Wenda Li +1

The demand for synthetic data in mathematical reasoning has increased due to its potential to enhance the mathematical capabilities of large language models (LLMs). However, ensuri…

cs.AI2026

CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning

Joshua Ong Jun Leang, Aryo Pradipta Gema, Shay B. Cohen

Mathematical reasoning remains a significant challenge for large language models (LLMs), despite progress in prompting techniques such as Chain-of-Thought (CoT). We present **Chain…