32 citations · 119 across the 72 of their papers we have counts for
16 papers · 1 filter
PhysCodeGym: Benchmarking Coding Agents for Physically Verifiable 4D Reconstruction
Shuai Shao, Jinchen He, Cunxin Fan +10
Building physical simulations from video requires coding agents to translate observations into executable models and refine them through tool use. We introduce PhysCodeGym, a bench…
FleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization
Kounianhua Du, Jianxing Liu, Kangning Zhang +7
With the rapid evolution of large language models (LLMs), there is an increasing demand for effective personalization techniques that can adapt model behavior to individual user pr…
AMBER: Multi-View Adaptive Budget Allocation for Listwise Vision-Language Reranking
Wenteng Chen, Jiachen Zhu, Rong Shan +10
Vision-language models (VLMs) are powerful listwise rerankers for multimodal retrieval, but high inference costs restrict them to evaluating small local candidate views. Existing m…
What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents
Xingshan Zeng, Zishan Xu, Boju Zhang +11
LLM agents increasingly rely on generated interaction data to learn how to interact with external environments. Agentic data generation must maintain consistency among environments…
SEAM: Shot Entity-Attribute Memory for Consistent Short-Drama Generation at Scale
Jiaqi Liu, Maolin Ran, Xiaoyang Lu +5
Short-drama generation has grown into a large, industrialized pipeline, and as it scales from isolated shots to the episode level, visual continuity has become a critical bottlenec…
SAGE: Self-Evolving Storyboard Skills via Attribution-Guided Rule Evolution
Maolin Ran, Xiaoyang Lu, Jiaqi Liu +5
Storyboards turn screenplays into visual shot plans for automated short drama production. Professional storyboarding relies on tacit directorial expertise and remains an industrial…
SkillJuror: Measuring How Agent Skill Organization Changes Runtime Behavior
Zhiyu Chen, Zihan Guo, Bo Huang +4
Agent Skills augment large language model (LLM) agents with procedural knowledge at inference time, but current benchmarks rarely distinguish what a Skill says from how it is organ…
MMSkills: Towards Multimodal Skills for General Visual Agents
Kangning Zhang, Shuai Shao, Qingyao Li +8
Reusable skills have become a core substrate for improving agent capabilities, yet most existing skill packages encode reusable behavior primarily as textual prompts, executable co…
Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization
Jiachen Zhu, Lingyu Yang, Rong Shan +6
The rise of autonomous GUI agents has triggered adversarial countermeasures from digital platforms, yet existing research prioritizes utility and robustness over the critical dimen…
Holos: A Web-Scale LLM-Based Multi-Agent System for the Agentic Web
Xiaohang Nie, Zihan Guo, Zicai Cui +20
As large language models (LLM)-driven agents transition from isolated task solvers to persistent digital entities, the emergence of the Agentic Web, an ecosystem where heterogeneou…
OSCAR: Optimization-Steered Agentic Planning for Composed Image Retrieval
Teng Wang, Rong Shan, Jianghao Lin +8
Composed image retrieval (CIR) requires complex reasoning over heterogeneous visual and textual constraints. Existing approaches largely fall into two paradigms: unified embedding…
ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling
Jianghao Lin, Yuanyuan Shi, Xin Peng +10
Large language models (LLMs) excel at function calling, but inference scaling has been explored mainly for unstructured generation. We propose an inference-scaling framework for st…
Superplatforms Have to Attack AI Agents
Jianghao Lin, Jiachen Zhu, Zheli Zhou +4
Over the past decades, superplatforms, digital companies that integrate a vast range of third-party services and applications into a single, unified ecosystem, have built their for…
A Survey of AI Agent Protocols
Yingxuan Yang, Huacan Chai, Yuanyi Song +11
The rapid development of large language models (LLMs) has led to the widespread deployment of LLM agents across diverse industries, including customer service, content generation,…
Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning
Jiachen Zhu, Congmin Zheng, Jianghao Lin +5
While large language models (LLMs) have significantly advanced mathematical reasoning, Process Reward Models (PRMs) have been developed to evaluate the logical validity of reasonin…
SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model
Lingyue Fu, Hao Guan, Kounianhua Du +6
Knowledge Tracing (KT) aims to determine whether students will respond correctly to the next question, which is a crucial task in intelligent tutoring systems (ITS). In educational…