5 citations · 5 across the 9 of their papers we have counts for
10 papers
OR-License: Evaluating LLM Agents as Real-World Operations Research Practitioners
Peisong Wang, Feixue Liu, Yize Meng +16
Can large language model (LLM) agents deliver expert-level operations research (OR) solutions directly from original client materials, without prior expert reformulation? Existing…
-Bench: LLMs Struggle with Resource-Rational Reasoning under Shared Budgets
Peisong Wang, Zhiwei Ma, Bowen Liu +6
In cognitive science, resource rationality asks how an agent should allocate limited computation to maximize expected value. Most reasoning and agent benchmarks use independent per…
Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach
Ziqi Gao, Chenyi Zi, Zijing Liu +3
Protein-protein interactions (PPIs) are fundamental to cellular function and disease mechanisms. Current learning-based PPI predictors focus on learning powerful protein representa…
PiCA: Pivot-Based Credit Assignment for Search Agentic Reinforcement Learning
Dongyi Liu, Yifan Niu, Qinwen Wang +2
Large Language Model (LLM)-based search agents trained with reinforcement learning (RL) have significantly improved the performance of knowledge-intensive tasks. However, existing…
ProteinOPD: Towards Effective and Efficient Preference Alignment for Protein Design
Yulin Zhang, He Cao, Zihao Jiang +6
Designing proteins with desired functions or properties represents a core goal in synthetic biology and drug discovery. Recent advances in protein language models (PLMs) have enabl…
Revisiting LoRA through the Lens of Parameter Redundancy: Spectral Encoding Helps
Jiashun Cheng, Aochuan Chen, Nuo Chen +4
Low-Rank Adaptation (LoRA) has emerged as a prominent technique for fine-tuning large foundation models. Despite its successes, the substantial parameter redundancy, which limits t…
A Survey of Cross-domain Graph Learning: Progress and Future Directions
Haihong Zhao, Zhixun Li, Chenyi Zi +4
Graph learning plays a vital role in mining and analyzing complex relationships within graph data and has been widely applied to real-world scenarios such as social, citation, and…
Parameter-Efficient Fine-Tuning via Circular Convolution
Aochuan Chen, Jiashun Cheng, Zijing Liu +4
Low-Rank Adaptation (LoRA) has gained popularity for fine-tuning large foundation models, leveraging low-rank matrices and to represent weight changes (i.…
Deep Reinforcement Learning for Modelling Protein Complexes
Ziqi Gao, Tao Feng, Jiaxuan You +4
AlphaFold can be used for both single-chain and multi-chain protein structure prediction, while the latter becomes extremely challenging as the number of chains increases. In this…
Parameter-Efficient Fine-Tuning with Discrete Fourier Transform
Ziqi Gao, Qichao Wang, Aochuan Chen +4
Low-rank adaptation~(LoRA) has recently gained much interest in fine-tuning foundation models. It effectively reduces the number of trainable parameters by incorporating low-rank m…