1 citations · 1 across the 13 of their papers we have counts for
20 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…
RubricArmor: Adversarial Evolution Improves LLM-Based Rubric Generation
Haocheng Yang, Yuchao Zhang, Licheng Pan +8
Rubric-based reinforcement learning (RL) provides interpretable rewards for aligning large language models (LLMs) by evaluating responses against query-specific evaluation criteria…
SkillGate: Training In-Policy Skill Selection in Long-Horizon Agents
Qingyao Li, Wenxiang Jiao, Shuai Shao +6
Agent frameworks increasingly package procedural knowledge as skills: instruction files an agent reads on demand, while public libraries now hold thousands of them. Which skill to…
Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation
Kangning Zhang, Haotian Fang, Xukun Luo +6
Semantic-ID generative recommenders represent each item as a short sequence of discrete semantic tokens and predict the next item by autoregressively generating this token sequence…
Harness-R1: Learning to Edit Executable Runtime Harnesses from Agent Failure Trajectories
Shuai Shao, Kangning Zhang, Qingyao Li +7
Agents built around large language models continually accumulate interaction trajectories during deployment, yet their behavior typically remains fixed. Beyond updating model weigh…
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…
SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution
Zhiyuan Yao, Yuxin Chen, Zhengxi Lu +13
Large language model agents often encounter related yet distinct tasks that share reusable solution patterns. Yet standard agentic reinforcement learning treats tasks as independen…
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…
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…
SWE-Cycle: Benchmarking Code Agents across the Complete Issue Resolution Cycle
Hao Guan, Lingyue Fu, Shao Zhang +8
As autonomous code agents move toward end-to-end software development, evaluating their practical autonomy becomes critical. Current benchmarks hide friction by testing agents in p…
MuSEAgent: A Multimodal Reasoning Agent with Stateful Experiences
Shijian Wang, Jiarui Jin, Runhao Fu +11
Research agents have recently achieved significant progress in information seeking and synthesis across heterogeneous textual and visual sources. In this paper, we introduce MuSEAg…
LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls
Kangning Zhang, Wenxiang Jiao, Kounianhua Du +4
Augmenting Large Language Models (LLMs) with external tools enables them to execute complex, multi-step tasks. However, tool learning is hampered by the static synthetic data pipel…
Fints: Efficient Inference-Time Personalization for LLMs with Fine-Grained Instance-Tailored Steering
Kounianhua Du, Jianxing Liu, Kangning Zhang +6
The rapid evolution of large language models (LLMs) has intensified the demand for effective personalization techniques that can adapt model behavior to individual user preferences…
A Survey of Process Reward Models: From Outcome Signals to Process Supervisions for Large Language Models
Congmin Zheng, Jiachen Zhu, Zhuoying Ou +8
Although Large Language Models (LLMs) exhibit advanced reasoning ability, conventional alignment remains largely dominated by outcome reward models (ORMs) that judge only final ans…
An Automatic Graph Construction Framework based on Large Language Models for Recommendation
Rong Shan, Jianghao Lin, Chenxu Zhu +7
Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation. However, most existing GNN-based recommendation method…
MOTOR: Learning ID-free Item Representation with Token Crossing for Embedding-based Multimodal Recommendation
Kangning Zhang, Jiarui Jin, Yingjie Qin +4
While multimodal recommendation models have effectively integrated visual and textual information, their reliance on unique ID embeddings constitutes a fundamental performance bott…
DREAM: A Dual Representation Learning Model for Multimodal Recommendation
Kangning Zhang, Yingjie Qin, Jiarui Jin +4
Multimodal recommendation focuses primarily on effectively exploiting both behavioral and multimodal information for the recommendation task. However, most existing models suffer f…
AlignRec: Aligning and Training in Multimodal Recommendations
Yifan Liu, Kangning Zhang, Xiangyuan Ren +7
With the development of multimedia systems, multimodal recommendations are playing an essential role, as they can leverage rich contexts beyond interactions. Existing methods mainl…
ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction
Jianghao Lin, Bo Chen, Hangyu Wang +7
Click-through rate (CTR) prediction has become increasingly indispensable for various Internet applications. Traditional CTR models convert the multi-field categorical data into ID…