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Yu-An Tan

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#natural language process... Preprint Jul 2026

Code Consistency Preference Optimization Verification for Language Model Alignment

This work proposes a method that generates computationally sound solutions with dependency graphs for execution-consistent preference optimization, and achieves 50.1% accuracy on PhyX multimodal physics reasoning, surpassing DeepSeek-R1 and OpenAI o3-mini.

Yu-An Tan, Ming-Qiao Mo, Hao Zhang · 0 citations
#machine learning Preprint Jul 2026

CompileRover: Revolutionizing Virtual Machine Compiler Optimization with a Tri-Role LLM-Driven Framework

CompileRover employs a sophisticated three-role collaboration mechanism, comprising a referee, an advisor, and an operator, effectively overcoming performance bottlenecks by leveraging comprehensive optimization algorithms and novel methodologies, including control flow analysis, code structure transformations, and dyn...

Ming-Qiao Mo, Yu-An Tan, Hao Zhang · 0 citations
Jul 2026

Though Language Models Err While They Strive: Conformal Prediction for Self-Correcting Scientific Generation

Scientific Feasibility Control SFC is introduced, a graph-structured conformal prediction framework that provides statistical guarantees for scientific reasoning validity through progressive absolute-coherent-factuality validation and decomposes scientific reasoning into atomic absolute-coherent-factuality units.

Ming-Qiao Mo, Yu-An Tan, Hao Zhang · 0 citations

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