2 citations · 2 across the 8 of their papers we have counts for
9 papers
LeAct: Learning to Reason from Expert Actions
Ziran Yang, Chengshuai Shi, Raj Ghugare +3
Modern reasoning models depend on reasoning data, today sourced from human annotations or distilled from stronger LLMs. However, a rich and largely untapped source of supervision l…
Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement
Jui-Hui Chung, Ziyang Cai, Zihao Li +14
We introduce Goedel-Architect, an agentic framework for formal theorem proving in Lean 4 centered on blueprint generation and refinement. A blueprint is a dependency graph of defin…
Continual Harness: Online Adaptation for Self-Improving Foundation Agents
Seth Karten, Joel Zhang, Tersoo Upaa +5
Coding harnesses such as Claude Code and OpenHands wrap foundation models with tools, memory, and planning, but no equivalent exists for embodied agents' long-horizon partial-obser…
MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI
Bohan Lyu, Yucheng Yang, Siqiao Huang +25
Modern AI progress has been driven by ML methods that are generalizable across settings and scalable to larger regimes. As large language models demonstrate advanced capabilities i…
Awakening the Sleeping Agent: Lean-Specific Agentic Data Reactivates General Tool Use in Goedel Prover
Jui-Hui Chung, Hongzhou Lin, Lai Jiang +2
Heavy supervised fine-tuning on a target domain can strongly suppress capabilities that were present in the base model. We study this phenomenon in formal mathematics using Goedel-…
Goedel-Code-Prover: Hierarchical Proof Search for Open State-of-the-Art Code Verification
Zenan Li, Ziran Yang, Deyuan He +7
Large language models (LLMs) can generate plausible code but offer limited guarantees of correctness. Formally verifying that implementations satisfy specifications requires constr…
Ineq-Comp: Benchmarking Human-Intuitive Compositional Reasoning in Automated Theorem Proving on Inequalities
Haoyu Zhao, Yihan Geng, Shange Tang +5
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 math…
Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?
Wenzhe Li, Yong Lin, Mengzhou Xia +1
Ensembling outputs from diverse sources is a straightforward yet effective approach to boost performance. Mixture-of-Agents (MoA) is one such popular ensemble method that aggregate…
Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving
Yong Lin, Shange Tang, Bohan Lyu +8
We introduce Goedel-Prover, an open-source language model that achieves state-of-the-art (as of April 5 2025) performance in automated formal proof generation for mathematical prob…