5 citations · 7 across the 18 of their papers we have counts for
19 papers
How Data Scales in Agentic Reinforcement Learning: Laws and Synthesis Strategies
Bowei He, Yankai Chen, Xiaokun Zhang +4
Pretraining scaling laws treat training data as a one-dimensional quantity: a token count. Agentic reinforcement learning (RL) inherits this scalar abstraction, but modern synthesi…
SCAFFOLD: Self-Improving Web Agents via Recursive Parametric Skill Abstraction
Bowei He, Xiaokun Zhang, Meng Ding +1
Web agents need to navigate visually rich, long-horizon interfaces that change across sites, yet most previous agents still learn each task in isolation and discard the procedural…
Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility
Yansen Zhang, Yilu Liu, Tianyu Liu +7
Large language models increasingly support scientific and algorithmic discovery through inference-time search over evaluated candidates. Existing adaptive discovery controllers ass…
Branching Policy Optimization: Sandbox-Native Language Agent Reinforcement Learning
Bowei He, Yankai Chen, Xiaokun Zhang +1
Reinforcement learning has emerged as the dominant paradigm for training large language model (LLM) agents that interact with executable sandboxes. State-of-the-art algorithms such…
LLM-as-a-Judge for Reliable and Explainable Offline Evaluation in Top-K Recommendation
Yue Que, Junyi Zhou, Xiaokun Zhang +3
Recommendation evaluation plays a crucial role in guiding the refinement and deployment of recommender systems. Most existing trials rely on offline evaluation using Top-K metrics…
SEAL: Can Saturated Benchmarks Be Revived by LLM-as-a-Meta-Judge?
Jiamin Chen, Yidi Wu, Qiexiang Wang +6
Widely used language-model benchmarks are increasingly saturated, with frontier systems often receiving near-tied scores that standard metrics cannot resolve. Rather than construct…
DirectorBench: Diagnosing Long-Form Video Generation with Personalized Multi-Agent Evaluation
Jiamin Chen, Qianben Chen, Jiawen Zhang +5
Long-form video generation is rapidly moving from short, single-scene synthesis toward minute-long, multi-shot creation with narrative structure, cinematic control, audio, and cros…
Looking Farther with Confidence: Uncertainty-Guided Future Learning for Sequential Recommendation
Ziqiang Cui, Xing Tang, Peiyang Liu +4
Sequential recommendation effectively models dynamic user interests but continues to face challenges related to data sparsity. While self-supervised learning has alleviated this is…
Beyong Tokens: Item-aware Attention for LLM-based Recommendation
Xiaokun Zhang, Bowei He, Jiamin Chen +2
Large Language Models (LLMs) have recently gained increasing attention in the field of recommendation. Existing LLM-based methods typically represent items as token sequences, and…
Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation
Xing Tang, Ziqiang Cui, Jingyang Bin +6
The sequential recommendation (SR) task aims to predict the next item based on users' historical interaction sequences. Typically trained on historical data, SR models often strugg…
Pedagogically-Inspired Data Synthesis for Language Model Knowledge Distillation
Bowei He, Yankai Chen, Xiaokun Zhang +4
Knowledge distillation from Large Language Models (LLMs) to smaller models has emerged as a critical technique for deploying efficient AI systems. However, current methods for dist…
Have We Really Understood Collaborative Information? An Empirical Investigation
Xiaokun Zhang, Zhaochun Ren, Bowei He +2
Collaborative information serves as the cornerstone of recommender systems which typically focus on capturing it from user-item interactions to deliver personalized services. Howev…
Preserving LLM Capabilities through Calibration Data Curation: From Analysis to Optimization
Bowei He, Lihao Yin, Huiling Zhen +5
Post-training compression has been a widely employed approach to scale down large language model (LLM) and facilitate efficient inference. In various proposed compression methods,…
Grounding Long-Context Reasoning with Contextual Normalization for Retrieval-Augmented Generation
Jiamin Chen, Yuchen Li, Xinyu Ma +5
Retrieval-Augmented Generation (RAG) has become an essential approach for extending the reasoning and knowledge capacity of large language models (LLMs). While prior research has p…
Counterfactual Multi-player Bandits for Explainable Recommendation Diversification
Yansen Zhang, Bowei He, Xiaokun Zhang +3
Existing recommender systems tend to prioritize items closely aligned with users' historical interactions, inevitably trapping users in the dilemma of ``filter bubble''. Recent eff…
Shapley Value-driven Data Pruning for Recommender Systems
Yansen Zhang, Xiaokun Zhang, Ziqiang Cui +1
Recommender systems often suffer from noisy interactions like accidental clicks or popularity bias. Existing denoising methods typically identify users' intent in their interaction…
Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation
Ziqiang Cui, Yunpeng Weng, Xing Tang +8
Contrastive learning has shown effectiveness in improving sequential recommendation models. However, existing methods still face challenges in generating high-quality contrastive p…
Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation
Renqi Jia, Xiaokun Zhang, Bowei He +4
User behavior records serve as the foundation for recommender systems. While the behavior data exhibits ease of acquisition, it often suffers from varying quality. Current methods…
PASER: Post-Training Data Selection for Efficient Pruned Large Language Model Recovery
Bowei He, Lihao Yin, Hui-Ling Zhen +3
Model pruning is an effective approach for compressing large language models (LLMs). However, this process often leads to significant degradation of model capabilities. While post-…