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Bohan Lyu

7 accepted papers

2026

Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction

ICLR 2026poster

Automated theorem proving (ATP) --- the task of generating a proof that passes automated proof verification given a math question in formal language --- is a critical challenge at the intersection of mathematics and Artificial Intelligence (AI). We introduce Goedel-Prover-V2, a family of two languag…

Cited by 0SourcecodeScholar
2025

Adapting While Learning: Grounding LLMs for Scientific Problems with Tool Usage Adaptation

ICML 2025poster

Large Language Models (LLMs) demonstrate promising capabilities in solving scientific problems but often suffer from the issue of hallucination. While integrating LLMs with tools can mitigate this issue, models fine-tuned on tool usage become overreliant on them and incur unnecessary costs. Insp…

2025

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub

ACL 2025long

Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. While equipping LLMs with external tools to build LLM-based agents can enhance their capabilities, existing approaches lack…

2025

Ineq-Comp: Benchmarking Human-Intuitive Compositional Reasoning in Automated Theorem Proving of Inequalities

NeurIPS 2025poster

LLM-based formal proof assistants (e.g., in Lean) hold great promise for automating mathematical discovery. But beyond syntactic correctness, do these systems truly understand mathematical structure as humans do? We investigate this question in context of mathematical inequalities---specifically the…

Cited by 0SourcecodeScholar
2025

MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks

ICLR 2025poster

We present MEGA-Bench, an evaluation suite that scales multimodal evaluation to over 500 real-world tasks, to address the highly heterogeneous daily use cases of end users. Our objective is to optimize for a set of high-quality data samples that cover a highly diverse and rich set of multimodal task…

2025

Surge: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors

EMNLP 2025

Neural surrogate models are powerful and efficient tools in data mining. Meanwhile, large language models (LLMs) have demonstrated remarkable capabilities in code-related tasks, such as generation and understanding. However, an equally important yet underexplored question is whether LLMs can serve a

2024

VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation

EMNLP 2024main

The recent years have witnessed great advances in video generation. However, the development of automatic video metrics is lagging significantly behind. None of the existing metric is able to provide reliable scores over generated videos. The main barrier is the lack of large-scale human-annotated d…