4 citations · 8 across the 8 of their papers we have counts for
10 papers
Write Once, Plan Many: Building Reusable Visual Planning Systems from One Scene
Ruixiao Yang, Yilun Hao, Mingxin Yu +2
Vision-language models (VLMs) can reason about visual planning tasks, but querying them for every instance incurs repeated inference cost and latency. Generating reusable planning…
RECAST: Learning to Compute the Right Context through Adaptive Evidence Routing
Yilun Hao, Krishna Sayana, Isabella Ye +4
Large language models are increasingly applied to tasks grounded in long, heterogeneous information sources. Conventional Retrieval-Augmented Generation (RAG) relies on fixed simil…
ASI-Bench: At the Dawn of Artificial Superintelligence
Junwei Zhou, Zhen Sun, Binyu Li +39
Artificial superintelligence (ASI) requires AI to move beyond mastering existing knowledge toward exploring the unknown, creating new knowledge, and turning new ideas into verifiab…
Simulation to Rules: A Dual-VLM Framework for Formal Visual Planning
Yilun Hao, Yongchao Chen, Chuchu Fan +1
Vision Language Models (VLMs) show strong potential for visual planning but struggle with precise spatial and long-horizon reasoning, while Planning Domain Definition Language (PDD…
R1-Code-Interpreter: LLMs Reason with Code via Supervised and Multi-stage Reinforcement Learning
Yongchao Chen, Yueying Liu, Junwei Zhou +5
Practical guidance on training Large Language Models (LLMs) to leverage Code Interpreter across diverse tasks remains lacking. We present R1-Code-Interpreter, an extension of a tex…
Code-as-Symbolic-Planner: Foundation Model-Based Robot Planning via Symbolic Code Generation
Yongchao Chen, Yilun Hao, Yang Zhang +1
Recent works have shown great potentials of Large Language Models (LLMs) in robot task and motion planning (TAMP). Current LLM approaches generate text- or code-based reasoning cha…
CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance
Yongchao Chen, Yilun Hao, Yueying Liu +2
Existing methods fail to effectively steer Large Language Models (LLMs) between textual reasoning and code generation, leaving symbolic computing capabilities underutilized. We int…
Planning Anything with Rigor: General-Purpose Zero-Shot Planning with LLM-based Formalized Programming
Yilun Hao, Yang Zhang, Chuchu Fan
While large language models (LLMs) have recently demonstrated strong potential in solving planning problems, there is a trade-off between flexibility and complexity. LLMs, as zero-…
Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools
Yilun Hao, Yongchao Chen, Yang Zhang +1
Large Language Models (LLMs) struggle to directly generate correct plans for complex multi-constraint planning problems, even with self-verification and self-critique. For example,…
PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based Sampling
Yongchao Chen, Jacob Arkin, Yilun Hao +3
Prompt optimization aims to find the best prompt to a large language model (LLM) for a given task. LLMs have been successfully used to help find and improve prompt candidates for s…