16 citations · 33 across the 11 of their papers we have counts for
7 papers · 1 filter
Balancing Fine-tuning and RAG: A Hybrid Strategy for Dynamic LLM Recommendation Updates
Changping Meng, Hongyi Ling, Jianling Wang +9
Large Language Models (LLMs) empower recommendation systems through their advanced reasoning and planning capabilities. However, the dynamic nature of user interests and content po…
Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics
Lianhao Zhou, Hongyi Ling, Cong Fu +14
Computing has long served as a cornerstone of scientific discovery. Recently, a paradigm shift has emerged with the rise of large language models (LLMs), introducing autonomous sys…
Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning
Shubham Parashar, Shurui Gui, Xiner Li +8
We aim to improve the reasoning capabilities of language models via reinforcement learning (RL). Recent RL post-trained models like DeepSeek-R1 have demonstrated reasoning abilitie…
Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists
Lianhao Zhou, Hongyi Ling, Keqiang Yan +5
We aim at designing language agents with greater autonomy for crystal materials discovery. While most of existing studies restrict the agents to perform specific tasks within prede…
Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation
Keqiang Yan, Xiner Li, Hongyi Ling +8
We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior…
Complex LLM Planning via Automated Heuristics Discovery
Hongyi Ling, Shubham Parashar, Sambhav Khurana +6
We consider enhancing large language models (LLMs) for complex planning tasks. While existing methods allow LLMs to explore intermediate steps to make plans, they either depend on…
Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insights
Shubham Parashar, Blake Olson, Sambhav Khurana +4
We examine the reasoning and planning capabilities of large language models (LLMs) in solving complex tasks. Recent advances in inference-time techniques demonstrate the potential…