2 citations · 3 across the 20 of their papers we have counts for
22 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…
EMRB: A Multi-Level Benchmark for Evaluating LLM Reasoning over Raw Electromagnetic Signals
Mingxu Zhang, Ying Sun, Yuhan Li +4
Large language models (LLMs) are increasingly used as code agents for scientific and engineering analysis, but their ability to analyze raw physical-layer measurements remains unte…
-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…
PHF: Privileged Hidden Flow for On-Policy Self-Distillation
Yuhan Li, Mingxu Zhang, Dazhong Shen +1
On-policy self-distillation (OPSD) trains a reasoning model on rollouts sampled from its own policy by matching a privileged teacher that also sees verified reference solutions. Ex…
EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective
Yuyao Wang, Zhongjian Zhang, Mo Chi +7
Recent benchmarks for Large Language Model (LLM) agents mainly evaluate reasoning, planning, and execution. However, memory is also essential for agents, as it enables them to stor…
IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage
Yuhan Li, Mingxu Zhang, Dazhong Shen +1
Reinforcement learning with verifiable rewards (RLVR) has become a key technique for en- hancing LLM reasoning, yet its data ineffi- ciency remains a major bottleneck. Existing met…
SAE-FD: Sparse Autoencoder Feature Distillation for Continual Learning of Large Language Models
Mingxu Zhang, Yuhan Li, Lujundong Li +3
Continual learning enables large language models to adapt to evolving tasks without retraining from scratch, yet catastrophic forgetting remains a central obstacle. Among continual…
CP-Agent: A Calibrated Risk-Controlled Agent for Feedback-Driven Competitive Programming
Peisong Wang, Bowen Liu, Zehua Li +4
Large language models still struggle with contest-level programming, while many agentic remedies rely on massive inference-time sampling or expensive multi-stage post-training. We…
CoLVR: Enhancing Exploratory Latent Visual Reasoning via Contrastive Optimization
Ziyang Ding, Linjian Meng, Yiming Wu +3
Due to the potential for exploratory reasoning of Latent Visual Reasoning, recent works tend to enable MLLMs (Multimodal Large Language Models) to perform visual reasoning by propa…
SLIM: Sparse Latent Steering for Interpretable and Property-Directed LLM-Based Molecular Editing
Mingxu Zhang, Yuhan Li, Lujundong Li +3
Large language models possess strong chemical reasoning capabilities, making them effective molecular editors. However, property-relevant information is implicitly entangled across…
LoopRPT: Reinforcement Pre-Training for Looped Language Models
Guo Tang, Shixin Jiang, Heng Chang +7
Looped language models (LoopLMs) perform iterative latent computation to refine internal representations, offering an alternative to explicit chain-of-thought (CoT) reasoning. Howe…
Unlocking the Potentials of Retrieval-Augmented Generation for Diffusion Language Models
Chuanyue Yu, Jiahui Wang, Yuhan Li +6
Diffusion Language Models (DLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. However, the potential of Retrieval-Augmented Generation (…
NPG-Muse: Scaling Long Chain-of-Thought Reasoning with NP-Hard Graph Problems
Yuyao Wang, Bowen Liu, Jianheng Tang +6
Reasoning Large Language Models (RLLMs) have recently achieved remarkable progress on complex reasoning tasks, largely enabled by their long chain-of-thought (Long CoT) capabilitie…
GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learning
Chuanyue Yu, Kuo Zhao, Yuhan Li +8
Graph Retrieval-Augmented Generation (GraphRAG) has shown great effectiveness in enhancing the reasoning abilities of LLMs by leveraging graph structures for knowledge representati…
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…
Can LLMs Alleviate Catastrophic Forgetting in Graph Continual Learning? A Systematic Study
Ziyang Cheng, Zhixun Li, Yuhan Li +6
Nowadays, real-world data, including graph-structure data, often arrives in a streaming manner, which means that learning systems need to continuously acquire new knowledge without…
G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
Yuhan Li, Xinni Zhang, Linhao Luo +4
Explainable recommendation has demonstrated significant advantages in informing users about the logic behind recommendations, thereby increasing system transparency, effectiveness,…
GCoder: Improving Large Language Model for Generalized Graph Problem Solving
Qifan Zhang, Xiaobin Hong, Jianheng Tang +5
Large Language Models (LLMs) have demonstrated strong reasoning abilities, making them suitable for complex tasks such as graph computation. Traditional reasoning steps paradigm fo…
GLBench: A Comprehensive Benchmark for Graph with Large Language Models
Yuhan Li, Peisong Wang, Xiao Zhu +5
The emergence of large language models (LLMs) has revolutionized the way we interact with graphs, leading to a new paradigm called GraphLLM. Despite the rapid development of GraphL…
GraphArena: Evaluating and Exploring Large Language Models on Graph Computation
Jianheng Tang, Qifan Zhang, Yuhan Li +2
The ``arms race'' of Large Language Models (LLMs) demands new benchmarks to examine their progresses. In this paper, we introduce GraphArena, a benchmarking tool designed to evalua…
GraphWiz: An Instruction-Following Language Model for Graph Problems
Nuo Chen, Yuhan Li, Jianheng Tang +1
Large language models (LLMs) have achieved impressive success across several fields, but their proficiency in understanding and resolving complex graph problems is less explored. T…
ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs
Yuhan Li, Peisong Wang, Zhixun Li +2
With the development of foundation models such as large language models, zero-shot transfer learning has become increasingly significant. This is highlighted by the generative capa…