9 citations · 34 across the 28 of their papers we have counts for
28 papers
IACM-RL: Intent-Aware Context Management and Reinforcement Learning for Complex Tool Invocation under Dynamic Intent Fluctuations
Dingwei Zhu, Jiahan Li, Chengjun Pan +22
Executing long-horizon tool invocations in real-world environments is severely challenged by dynamic user intent noise. Existing methods attempt robustness via implicit history sca…
World Action Models: The Next Frontier in Embodied AI
Siyin Wang, Junhao Shi, Zhaoyang Fu +11
Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning, yet they learn reactive observation-to-action mappings without explic…
Beyond Attention Magnitude: Leveraging Inter-layer Rank Consistency for Efficient Vision-Language-Action Models
Peiju Liu, Jinming Liu, Xipeng Qiu +1
Vision-Language-Action (VLA) models excel in robotic manipulation but suffer from significant inference latency due to processing dense visual tokens. Existing token reduction meth…
Mirror: A Multi-Agent System for AI-Assisted Ethics Review
Yifan Ding, Yuhui Shi, Zhiyan Li +10
Ethics review is a foundational mechanism of modern research governance, yet contemporary systems face increasing strain as ethical risks arise as structural consequences of large-…
CL-bench: A Benchmark for Context Learning
Shihan Dou, Ming Zhang, Zhangyue Yin +24
Current language models (LMs) excel at reasoning over prompts using pre-trained knowledge. However, real-world tasks are far more complex and context-dependent: models must learn f…
Emergent Structured Representations Support Flexible In-Context Inference in Large Language Models
Ningyu Xu, Qi Zhang, Xipeng Qiu +1
Large language models (LLMs) exhibit emergent behaviors suggestive of human-like reasoning. While recent work has identified structured conceptual representations within these mode…
DFPO: Scaling Value Modeling via Distributional Flow towards Robust and Generalizable LLM Post-Training
Dingwei Zhu, Zhiheng Xi, Shihan Dou +17
Training reinforcement learning (RL) systems in real-world environments remains challenging due to noisy supervision and poor out-of-domain (OOD) generalization, especially in LLM…
FRoM-W1: Towards General Humanoid Whole-Body Control with Language Instructions
Peng Li, Zihan Zhuang, Yangfan Gao +16
Humanoid robots are capable of performing various actions such as greeting, dancing and even backflipping. However, these motions are often hard-coded or specifically trained, whic…
AstroAgentBench: Evaluating Agentic Planning on Space Mission Planning Tasks
Weiyi Wang, Xinchi Chen, Jingjing Gong +2
Recent LLM-for-Space systems address mission planning, scheduling, operations support, simulator control, and autonomy, but their evaluations use different task contracts, control…
VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction
Jie Yang, Jiajun Chen, Zhangyue Yin +7
Intelligent vehicle cockpits present unique challenges for API Agents, requiring coordination across tightly-coupled subsystems that exceed typical task environments' complexity. T…
Dynamic and Generalizable Process Reward Modeling
Zhangyue Yin, Qiushi Sun, Zhiyuan Zeng +3
Process Reward Models (PRMs) are crucial for guiding Large Language Models (LLMs) in complex scenarios by providing dense reward signals. However, existing PRMs primarily rely on h…
Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric
Yixin Cao, Jiahao Ying, Yaoning Wang +3
Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications, yet current evaluation methods struggle to keep pace with their rapid deve…
FamilyTool: A Multi-hop Personalized Tool Use Benchmark
Yuxin Wang, Yiran Guo, Yining Zheng +7
The integration of tool learning with Large Language Models (LLMs) has expanded their capabilities in handling complex tasks by leveraging external tools. However, existing benchma…
Game-RL: Synthesizing Multimodal Verifiable Game Data to Boost VLMs' General Reasoning
Jingqi Tong, Jixin Tang, Hangcheng Li +21
Vision-language reinforcement learning (RL) has primarily focused on narrow domains (e.g. geometry or chart reasoning). This leaves broader training scenarios and resources underex…
Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners
Yuxin Wang, Botian Jiang, Yiran Guo +4
Prior-Fitted Networks (PFNs) have recently been proposed to efficiently perform tabular classification tasks. Although they achieve good performance on small datasets, they encount…
AutoLogi: Automated Generation of Logic Puzzles for Evaluating Reasoning Abilities of Large Language Models
Qin Zhu, Fei Huang, Runyu Peng +6
While logical reasoning evaluation of Large Language Models (LLMs) has attracted significant attention, existing benchmarks predominantly rely on multiple-choice formats that are v…
Thus Spake Long-Context Large Language Model
Xiaoran Liu, Ruixiao Li, Mianqiu Huang +11
Long context is an important topic in Natural Language Processing (NLP), running through the development of NLP architectures, and offers immense opportunities for Large Language M…
Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework
Yuhong Sun, Zhangyue Yin, Xuanjing Huang +2
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. Math Word Problems (MWPs) serve as a crucial benchmark for evaluating LLMs' reasoning…
Revealing emergent human-like conceptual representations from language prediction
Ningyu Xu, Qi Zhang, Chao Du +4
People acquire concepts through rich physical and social experiences and use them to understand and navigate the world. In contrast, large language models (LLMs), trained solely th…
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective
Zhiyuan Zeng, Qinyuan Cheng, Zhangyue Yin +6
OpenAI o1 represents a significant milestone in Artificial Inteiligence, which achieves expert-level performances on many challanging tasks that require strong reasoning ability.Op…
Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision
Zhiheng Xi, Dingwen Yang, Jixuan Huang +21
Training large language models (LLMs) to spend more time thinking and reflection before responding is crucial for effectively solving complex reasoning tasks in fields such as scie…
Multi-Programming Language Sandbox for LLMs
Shihan Dou, Jiazheng Zhang, Jianxiang Zang +25
We introduce MPLSandbox, an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Lang…
Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse Autoencoders
Zhengfu He, Wentao Shu, Xuyang Ge +9
Sparse Autoencoders (SAEs) have emerged as a powerful unsupervised method for extracting sparse representations from language models, yet scalable training remains a significant ch…
What's Wrong with Your Code Generated by Large Language Models? An Extensive Study
Shihan Dou, Haoxiang Jia, Shenxi Wu +14
The increasing development of LLMs in code generation has drawn significant attention among researchers. To enhance LLM-based code generation ability, current efforts are predomina…