most citedFineBadminton: A Multi-Level Dataset for Fine-Grained Badminton Video Understanding

2 citations · 2 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2026

ASTER: Agentic Scaling with Tool-integrated Extended Reasoning

Xuqin Zhang, Quan He, Zhenrui Zheng +3

Reinforcement learning (RL) has emerged as a dominant paradigm for eliciting long-horizon reasoning in Large Language Models (LLMs). However, scaling Tool-Integrated Reasoning (TIR…

cs.AI2025

AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications

Dawei Gao, Zitao Li, Yuexiang Xie +20

Driven by rapid advancements of Large Language Models (LLMs), agents are empowered to combine intrinsic knowledge with dynamic tool use, greatly enhancing their capacity to address…

cs.MM20252 cited

FineBadminton: A Multi-Level Dataset for Fine-Grained Badminton Video Understanding

Xusheng He, Wei Liu, Shanshan Ma +3

Fine-grained analysis of complex and high-speed sports like badminton presents a significant challenge for Multimodal Large Language Models (MLLMs), despite their notable advanceme…

cs.SE2025

BitsAI-Fix: LLM-Driven Approach for Automated Lint Error Resolution in Practice

Yuanpeng Li, Qi Long, Zhiyuan Yao +7

As enterprise codebases continue to grow in scale and complexity, the volume of lint errors far exceeds engineers' manual remediation capacity, leading to continuous accumulation o…

cs.RO2025

RationalVLA: A Rational Vision-Language-Action Model with Dual System

Wenxuan Song, Jiayi Chen, Wenxue Li +12

A fundamental requirement for real-world robotic deployment is the ability to understand and respond to natural language instructions. Existing language-conditioned manipulation ta…

cs.LG2025

Trajectory World Models for Heterogeneous Environments

Shaofeng Yin, Jialong Wu, Siqiao Huang +4

Heterogeneity in sensors and actuators across environments poses a significant challenge to building large-scale pre-trained world models on top of this low-dimensional sensor info…