7 citations · 20 across the 40 of their papers we have counts for
8 papers · 1 filter
More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Models
Tianxiang Gao, Jinzhe Li, Zhiyuan Li +2
Direct-decision models enable efficient classification and evaluation by selecting from predefined candidates, but their reliability depends on more than prediction accuracy. Acros…
ConflictVLA-Bench: Benchmarking Behavioral Responses of Vision-Language-Action Models to Premise Conflicts
Liyu Hou, Yuan Wu, Yi Chang
While Vision-Language-Action (VLA) models perform strongly on manipulation tasks, their responses to invalid task premises remain underexplored. Existing evaluations of premise con…
RPCBench: A Benchmark for Proactive Premise Critique in LLM-based Recommendation
Zhongru Chen, Yuan Wu, Yi Chang
Large language models are increasingly used as interactive recommender assistants. Their evaluation should therefore go beyond plausible item recommendation and test whether they c…
MMPCBench: Benchmarking Multimodal Large Language Models on Proactive Critique of Flawed Inputs
Jinzhe Li, Gengxu Li, Jinnan Li +2
As Multimodal Large Language Models (MLLMs) evolve into sophisticated interactive assistants, their reliability depends not only on following instructions but also on validating th…
CaSKG: Counterfactual-Causal Skill Graphs for Scalable Agent Skill Retrieval
Zhiyuan Li, Linyuan Gao, Xuechun Ding +3
Reusable skill libraries allow large language model (LLM) agents to reuse procedural knowledge across tasks, but they also turn memory access into a challenging retrieval problem.…
BV-Blend: Uncertainty-Weighted Historical Baselines for Stable Critic-Free RL with Verifiable Rewards
Yupeng Chang, Yuan Wu, Yi Chang
Critic-free reinforcement learning with verifiable rewards (RLVR), exemplified by Group Relative Policy Optimization (GRPO), avoids training a value function (critic) and reduces m…
ConfProBench: A Confidence Evaluation Benchmark for MLLM-Based Process Judges
Yue Zhou, Yi Chang, Yuan Wu
Reasoning is a critical capability of multimodal large language models (MLLMs) for solving complex multimodal tasks, and judging the correctness of reasoning steps is crucial for i…
Refining Critical Thinking in LLM Code Generation: A Faulty Premise-based Evaluation Framework
Jialin Li, Jinzhe Li, Gengxu Li +2
With the advancement of code generation capabilities in large language models (LLMs), their reliance on input premises has intensified. When users provide inputs containing faulty…