24 citations · 33 across the 17 of their papers we have counts for
19 papers
What Really Works for Universal Multimodal Embeddings? A Controlled Study of Contrastive Training Strategies
Duo Li, Haojun Pan, Zuhao Yang +4
Contrastive learning has become the standard paradigm for multimodal representation learning, aligning semantically matched pairs while separating mismatched ones in a shared embed…
IndustryLLM: Failure-Driven LLM Training for Industrial Procurement
Liang Ding, Zhiang Xu, Yuyang Sheng +13
Industrial procurement requires language models to bridge informal buyer jargon, sparse marketplace attributes, and authoritative engineering standards under strict safety toleranc…
Learning from What You Retrieve: Online RL Fine-Tuning for Semantic Retrieval
Shaowei Wei, Chong Huang, Songtao Fang +3
In large-scale e-commerce retrieval, dual-encoder retrievers are op- timized for contrastive similarity, whereas downstream rerankers capture finer-grained relevance preferences; t…
CommerceVibe: Learning to Design E-Commerce Creatives as Executable Visual Code via Dual-Feedback Reinforcement Learning
Yajiao Xu, Jin Zhang, Jiangbo Ai +4
High-quality e-commerce creatives are essential for presenting products and conveying marketing messages. Recent diffusion models enable scalable creative generation and produce vi…
Bidirectional Context Self-Distillation for Reinforcement Learning of Skill-Based LLM Agents
Tianjun Pan, Yuan Li, Hongda Wang +8
External natural-language skills provide large language model (LLM) agents with reusable and editable guidance for solving complex tasks. Yet their effectiveness depends not only o…
OmniPack: Unified Token Compression for Efficient Omni-modal Large Language Models
Wanshun Su, Yang Shi, Feihu Liu +10
Omni-modal large language models (Omni-LLMs) have achieved remarkable performance on audio-visual understanding tasks, but processing long and highly redundant visual and audio tok…
MerchantBench: Benchmarking LLM Agents for Long-Term Coherence in E-Commerce Operations
Qiming Shi, Yulong Tao, Linbo Jin +10
Large language model agents are increasingly evaluated as autonomous tool users, yet most benchmarks focus on bounded tasks with immediate success criteria. Real-world deployments…
Beyond Solution-Centric Search: Adaptive Inquiry and Knowledge Revision for Autonomous ML Engineering
Shaokang Fu, Yulong Tao, Linbo Jin +7
Long-horizon autonomous research tasks such as machine learning engineering require systems to make interdependent decisions under a limited budget. Existing LLM-based agents typic…
IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Products
Haonan Qi, Jin Cao, Yongqi Zhang +9
Industrial products such as valves and circuit breakers are defined by dense technical specifications that govern procurement, compatibility, and safety across supply chains. These…
ARBOR: Online Process Rewards via a Reusable Rubric Buffer for Search Agents
Zheng Liu, Longxiang Zhang, Xintong Wang +8
LLM-based search agents are trained predominantly with outcome-only reward, leaving the search process itself unsupervised. This signal degenerates on outcome-homogeneous groups wh…
IndustryBench: Probing the Industrial Knowledge Boundaries of LLMs
Songlin Bai, Xintong Wang, Linlin Yu +12
In industrial procurement, an LLM answer is useful only if it survives a standards check: recommended material must match operating condition, every parameter must respect a regula…
How Do Answer Tokens Read Reasoning Traces? Self-Reading Patterns in Thinking LLMs for Quantitative Reasoning
Haoyang Chen, Yi Liu, Jianzhi Shao +3
Thinking LLMs produce reasoning traces before answering. Prior activation steering work mainly targets on shaping these traces. It remains less understood how answer tokens actuall…
Knowledge Enhanced Multi-intent Transformer Network for Recommendation
Ding Zou, Wei Wei, Feida Zhu +3
Incorporating Knowledge Graphs into Recommendation has attracted growing attention in industry, due to the great potential of KG in providing abundant supplementary information and…
Search Intenion Network for Personalized Query Auto-Completion in E-Commerce
Wei Bao, Mi Zhang, Tao Zhang +1
Query Auto-Completion(QAC), as an important part of the modern search engine, plays a key role in complementing user queries and helping them refine their search intentions.Today's…
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation
Zhao-Yang Liu, Liucheng Sun, Chenwei Weng +2
Bundle recommendation aims to provide a bundle of items to satisfy the user preference on e-commerce platform. Existing successful solutions are based on the contrastive graph lear…
Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data
Jiacheng Huang, Yao Zhao, Wei Hu +5
Knowledge graphs (KGs) have become a valuable asset for many AI applications. Although some KGs contain plenty of facts, they are widely acknowledged as incomplete. To address this…
Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning
Bang Lin, Xiuchong Wang, Yu Dong +3
Most real-world datasets are inherently heterogeneous graphs, which involve a diversity of node and relation types. Heterogeneous graph embedding is to learn the structure and sema…
Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection
Ben Chen, Bin Chen, Dehong Gao +5
With the pandemic of COVID-19, relevant fake news is spreading all over the sky throughout the social media. Believing in them without discrimination can cause great trouble to peo…
Spending Money Wisely: Online Electronic Coupon Allocation based on Real-Time User Intent Detection
Liangwei Li, Liucheng Sun, Chenwei Weng +2
Online electronic coupon (e-coupon) is becoming a primary tool for e-commerce platforms to attract users to place orders. E-coupons are the digital equivalent of traditional paper…