3 papers
cs.CL2026
SOP-Maze: Evaluating Large Language Models on Complicated Business Standard Operating Procedures
Jiaming Wang, Zhe Tang, Zehao Jin +5
As large language models (LLMs) are widely deployed as domain-specific agents, many benchmarks have been proposed to evaluate their ability to follow instructions and make decision…
cs.CL2025
Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs' Instruction Following Capability
Jiaming wang, Yunke Zhao, Peng Ding +8
The capability to precisely adhere to instructions is a cornerstone for Large Language Models (LLMs) to function as dependable agents in real-world scenarios. However, confronted w…
cs.CV2025
Spatio-Temporal LLM: Reasoning about Environments and Actions
Haozhen Zheng, Beitong Tian, Mingyuan Wu +3
Despite significant recent progress of Multimodal Large Language Models (MLLMs), current MLLMs are challenged by "spatio-temporal" prompts, i.e., prompts that refer to 1) the entir…