32 citations · 119 across the 72 of their papers we have counts for
82 papers
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…
Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents
Yuanyi Song, Yukai Wang, Xinbei Ma +7
Long-term memory is essential for LLM-based agents operating over extended interactions. Existing memory systems primarily update memory when new information arrives, treating retr…
Weaving Visual Narratives: Agentic Image Bundle Composition Beyond Atomic Visual Matching
Rong Shan, Tianyi Xu, Congmin Zheng +9
Image retrieval has traditionally been formulated as a point-wise matching problem, where each candidate image is scored in isolation. However, this atomic paradigm fails to captur…
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…
DynaKRAG: A Unified Framework for Learnable Evidence Control in Multi-Hop Retrieval-Augmented Generation
Chenyu Zhou, Yaqi Wu, Xiaolei Guo +7
Multi-hop retrieval-augmented generation (RAG) acquires evidence sequentially, with each document contributing supporting facts, bridge entities, query refinements, or sufficient e…
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…
Skill2Query: Exploiting Skill Structure to Generate Pseudo-Queries for Agent Skill Retrieval
Lihui Ding, Zihan Guo, Bingwei Lu +5
Pseudo-query generation can alleviate the supervision bottleneck for agent skill retrieval, but existing document-level approaches typically leave the rich internal relations among…
VAD: Attributing Visual Evidence for Target Reconstruction in Multimodal On-Policy Distillation
Kangning Zhang, Yixing Li, Shuai Shao +9
Multimodal on-policy distillation (OPD) transfers fine-grained visual knowledge by supervising student-generated trajectories with a privileged-view teacher. Yet its next-token cor…
DiffCold: A Diffusion-based Generative Model for Cold-Start Item Recommendation
Kangning Zhang, Yingjie Qin, Weinan Zhang +2
Cold-start item recommendation remains a persistent challenge in real-world systems due to the absence of interaction histories. While prior models attempt to bridge this gap using…
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…
LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents
Aofan Yu, Chenyu Zhou, Tianyi Xu +8
Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and e…
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…
DynaTree: Dynamic Agentic Retrieval Tree for Time-Sensitive News Retrieval
Siyuan Qi, Xinyuan Wang, Yingxuan Yang +5
Agentic Retrieval-Augmented Generation improves retrieval by integrating planning, tool use, and iterative reasoning, but existing agentic RAG methods often couple semantic expansi…
Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agents
Haoyi Hu, Qirong Lyu, Xianghan Kong +7
While AI agents demonstrate remarkable capabilities in reasoning and tool use, they remain fundamentally reactive: they compute responses only after explicit user prompts. This par…
Holder Policy Optimisation
Yuxiang Chen, Dingli Liang, Yihang Chen +8
Group Relative Policy Optimisation (GRPO) enhances large language models by estimating advantages across a group of sampled trajectories. However, mapping these trajectory-level ad…
Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench
Tianyu Wang, Jiajun Li, Jianghao Lin
LLMs are increasingly used as ``digital consumers'' to simulate public opinion, pre-test marketing decisions, and anticipate audience response. However, existing evaluations rarely…
SkillMAS: Skill Co-Evolution with LLM-based Multi-Agent System
Shuai Pan, Yixiang Liu, Jiaye Gao +7
Large language model (LLM) agent systems are increasingly expected to improve after deployment, but existing work often decouples two adaptation targets: skill evolution and multi-…
Contexting as Recommendation: Evolutionary Collaborative Filtering for Context Engineering
Jiachen Zhu, Zhuoying Ou, Congmin Zheng +9
Large Language Models (LLMs) are highly sensitive to their input contexts, motivating the development of automated context engineering. However, existing methods predominantly trea…
SMMBench: A Benchmark for Source-Distributed Multimodal Agent Memory
Huacan Chai, Yukai Wang, Yingxuan Yang +7
Existing benchmarks for multimodal memory reasoning largely evaluate systems within pre-assembled contexts, but under-evaluate whether agents can use evidence distributed across in…
OSDN: Improving Delta Rule with Provable Online Preconditioning in Linear Attention
Chenyu Zhou, Hongpei Li, Yuerou Liu +3
Linear attention and state-space models offer constant-memory alternatives to softmax attention, but often struggle with in-context associative recall. The Delta Rule mitigates thi…
Position: Academic Conferences are Potentially Facing Denominator Gaming Caused by Fully Automated Scientific Agents
Rong Shan, Te Gao, Hang Zheng +6
The implicit policy of maintaining relatively stable acceptance rates at top AI conferences, despite exponentially growing submissions, introduces a critical structural vulnerabili…