most citedKnowing When to Ask -- Bridging Large Language Models and Data

2 citations · 2 across the 8 of their papers we have counts for

collaborators

8 papers

cs.RO2025

An Anatomy of Vision-Language-Action Models: From Modules to Milestones and Challenges

Chao Xu, Suyu Zhang, Yang Liu +11

Vision-Language-Action (VLA) models are driving a revolution in robotics, enabling machines to understand instructions and interact with the physical world. This field is exploding…

cs.CV2025

TinyChemVL: Advancing Chemical Vision-Language Models via Efficient Visual Token Reduction and Complex Reaction Tasks

Xuanle Zhao, Shuxin Zeng, Xinyuan Cai +4

While Vision Language Models (VLMs) have demonstrated remarkable capabilities in general visual understanding, their application in the chemical domain has been limited, with previ…

cs.CL2025

Empowering LLMs with Parameterized Skills for Adversarial Long-Horizon Planning

Sijia Cui, Shuai Xu, Aiyao He +2

Recent advancements in Large Language Models(LLMs) have led to the development of LLM-based AI agents. A key challenge is the creation of agents that can effectively ground themsel…

cs.CL2025

INSEva: A Comprehensive Chinese Benchmark for Large Language Models in Insurance

Shisong Chen, Qian Zhu, Wenyan Yang +15

Insurance, as a critical component of the global financial system, demands high standards of accuracy and reliability in AI applications. While existing benchmarks evaluate AI capa…

cs.CL2025

Thinking with Nothinking Calibration: A New In-Context Learning Paradigm in Reasoning Large Language Models

Haotian Wu, Bo Xu, Yao Shu +2

Reasoning large language models (RLLMs) have recently demonstrated remarkable capabilities through structured and multi-step reasoning. While prior research has primarily focused o…

cs.AI2025

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation

Shuai Xu, Sijia Cui, Yanna Wang +2

Efficiently modeling and exploiting opponents is a long-standing challenge in adversarial domains. Large Language Models (LLMs) trained on extensive textual data have recently demo…