2 citations · 2 across the 15 of their papers we have counts for
15 papers
ConvMem: Convolutional Memory for Long-Context Reasoning
Hongming Zhang, Zhaozhen Gu, Fengshuo Bai +6
While Large Language Models (LLMs) have demonstrated impressive capabilities, they often struggle with extremely long contexts due to fixed context limits. To address this, sequent…
HyMem: Hierarchical Context Management for Long-Horizon Agents via Information Isolation
XinQi Wang, Jinwei Xiao, Sijia Cui +4
Large language model (LLM) agents often perform poorly on complex, long-horizon tasks because their context becomes increasingly cluttered over time. As interactions accumulate, de…
AMA: Adaptive Memory via Multi-Agent Collaboration
Weiquan Huang, Zixuan Wang, Hehai Lin +6
The rapid evolution of Large Language Model (LLM) agents has necessitated robust memory systems to support cohesive long-term interaction and complex reasoning. Benefiting from the…
MMErroR: A Benchmark for Erroneous Reasoning in Vision-Language Models
Yang Shi, Yifeng Xie, Minzhe Guo +6
Recent advances in Vision-Language Models (VLMs) have improved performance in multi-modal learning, raising the question of whether these models truly understand the content they p…
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…
EchoTrail-GUI: Building Actionable Memory for GUI Agents via Critic-Guided Self-Exploration
Runze Li, Yuwen Zhai, Bo Xu +5
Contemporary GUI agents, while increasingly capable due to advances in Large Vision-Language Models (VLMs), often operate with a critical limitation: they treat each task in isolat…
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…
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…
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…
Coarse-to-Fine Grounded Memory for LLM Agent Planning
Wei Yang, Jinwei Xiao, Hongming Zhang +3
Recent advancements in Large Language Models (LLMs) have driven growing interest in LLM-based agents for complex planning tasks. To avoid costly agent training, many studies adopte…
Self-Guided Function Calling in Large Language Models via Stepwise Experience Recall
Sijia Cui, Aiyao He, Shuai Xu +5
Function calling enables large language models (LLMs) to interact with external systems by leveraging tools and APIs. When faced with multi-step tool usage, LLMs still struggle wit…
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…
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…
TUMS: Enhancing Tool-use Abilities of LLMs with Multi-structure Handlers
Aiyao He, Sijia Cui, Shuai Xu +2
Recently, large language models(LLMs) have played an increasingly important role in solving a wide range of NLP tasks, leveraging their capabilities of natural language understandi…
Knowing When to Ask -- Bridging Large Language Models and Data
Prashanth Radhakrishnan, Jennifer Chen, Bo Xu +5
Large Language Models (LLMs) are prone to generating factually incorrect information when responding to queries that involve numerical and statistical data or other timely facts. I…