1 citations · 2 across the 14 of their papers we have counts for
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The Unreliable Progress Bar: Can LLM Agents Reliably Report Task Progress Throughout Execution?
Boyang Wang, Yunhan Wang, Yalun Wu
Recent large language models can emit task-progress signals that agent frameworks use to decide whether a task should continue or stop, yet whether a model can reliably report its…
FLY-EVAL++: An Evidence-Driven Evaluation Protocol for Safety-Constrained Flight Prediction with Large Language Models
Yalun Wu, Junfeng Fang, Jiawei Wang +6
Evaluating large language models (LLMs) in safety-critical, physics-governed environments requires more than accuracy-based metrics, because predictions that are numerically close…
Evaluating Stochastic Collapse and Implicit Bias in Multimodal Large Language Models
Huiyuan Zheng, Houtao Zhang, Boyang Wang +2
Current evaluations for Multimodal Large Language Models (MLLMs) overwhelmingly focus on utility-driven objectives, leaving model behavior under logic-neutral scenarios largely und…
PilotBench: A Benchmark for General Aviation Agents with Safety Constraints
Yalun Wu, Haotian Liu, Zhoujun Li +1
As Large Language Models (LLMs) advance toward embodied AI agents operating in physical environments, a fundamental question emerges: can models trained on text corpora reliably re…