1 citations · 1 across the 17 of their papers we have counts for
19 papers
Structural Process Supervision for Latent Chain-of-Thought Reasoning
Yiqi Li, Xu Chen, Chen Ju +6
Latent reasoning approaches enhance token-level efficiency and robustness by replacing verbose, explicit chain-of-thought (CoT) tokens with compact continuous-space embeddings. How…
Enhancing Social Intelligence in LLMs with Hierarchical Reasoning and Utterance-Level Goal Rewarding
Xiaofeng Wang, Kakam Chong, Shuai Xiao +9
Large language models (LLMs) excel in structured tasks but struggle with dynamic social interactions, where success requires long-term goal coordination and rapid adaptation. Curre…
Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Search
Lei Chen, Chen Ju, Xu Chen +13
In this work, we presented Pailitao-VL, a comprehensive multi-modal retrieval system engineered for high-precision, real-time industrial search. We here address three critical chal…
Explore More, Learn Better: Parallel MLLM Embeddings under Mutual Information Minimization
Zhicheng Wang, Chen Ju, Xu Chen +5
Embedding models are a cornerstone of modern AI. Driven by Multimodal Large Language Models (MLLMs), they have made great progress in architecture and data curation, while the holi…
Wave-Particle (Continuous-Discrete) Dualistic Visual Tokenization for Unified Understanding and Generation
Yizhu Chen, Chen Ju, Zhicheng Wang +5
The unification of understanding and generation within a single multi-modal large model (MLLM) remains one significant challenge, largely due to the dichotomy between continuous an…
The FM Agent
Annan Li, Chufan Wu, Zengle Ge +19
Large language models (LLMs) are catalyzing the development of autonomous AI research agents for scientific and engineering discovery. We present FM Agent, a novel and general-purp…
Modernizing Facebook Scoped Search: Keyword and Embedding Hybrid Retrieval with LLM Evaluation
Yongye Su, Zeya Zhang, Jane Kou +5
Beyond general web-scale search, social network search uniquely enables users to retrieve information and discover potential connections within their social context. We introduce a…
Evaluating Multi-Turn Bargain Skills in LLM-Based Seller Agent
Issue Yishu Wang, Kakam Chong, Xiaofeng Wang +7
In online second-hand marketplaces, multi-turn bargaining is a crucial part of seller-buyer interactions. Large Language Models (LLMs) can act as seller agents, negotiating with bu…
Benchmarking Multi-National Value Alignment for Large Language Models
Weijie Shi, Chengyi Ju, Chengzhong Liu +8
Do Large Language Models (LLMs) hold positions that conflict with your country's values? Occasionally they do! However, existing works primarily focus on ethical reviews, failing t…
Beyond Static Scenes: Camera-controllable Background Generation for Human Motion
Mingshuai Yao, Mengting Chen, Qinye Zhou +9
In this paper, we investigate the generation of new video backgrounds given a human foreground video, a camera pose, and a reference scene image. This task presents three key chall…
Squeeze Out Tokens from Sample for Finer-Grained Data Governance
Weixiong Lin, Chen Ju, Haicheng Wang +8
Widely observed data scaling laws, in which error falls off as a power of the training size, demonstrate the diminishing returns of unselective data expansion. Hence, data governan…
FOLDER: Accelerating Multi-modal Large Language Models with Enhanced Performance
Haicheng Wang, Zhemeng Yu, Gabriele Spadaro +4
Recently, Multi-modal Large Language Models (MLLMs) have shown remarkable effectiveness for multi-modal tasks due to their abilities to generate and understand cross-modal data. Ho…
Advancing Myopia To Holism: Fully Contrastive Language-Image Pre-training
Haicheng Wang, Chen Ju, Weixiong Lin +9
In rapidly evolving field of vision-language models (VLMs), contrastive language-image pre-training (CLIP) has made significant strides, becoming foundation for various downstream…
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation
Jiajun Cui, Xu Chen, Shuai Xiao +4
For recommender systems in internet platforms, search activities provide additional insights into user interest through query-click interactions with items, and are thus widely use…
Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Large Models
Chen Ju, Haicheng Wang, Haozhe Cheng +6
Vision-Language Large Models (VLMs) recently become primary backbone of AI, due to the impressive performance. However, their expensive computation costs, i.e., throughput and dela…
Cell Variational Information Bottleneck Network
Zhonghua Zhai, Chen Ju, Jinsong Lan +1
In this work, we propose Cell Variational Information Bottleneck Network (cellVIB), a convolutional neural network using information bottleneck mechanism, which can be combined wit…
Wear-Any-Way: Manipulable Virtual Try-on via Sparse Correspondence Alignment
Mengting Chen, Xi Chen, Zhonghua Zhai +4
This paper introduces a novel framework for virtual try-on, termed Wear-Any-Way. Different from previous methods, Wear-Any-Way is a customizable solution. Besides generating high-f…
Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Models
Chen Ju, Haicheng Wang, Zeqian Li +4
Vision-Language Large Models (VLMs) have become primary backbone of AI, due to the impressive performance. However, their expensive computation costs, i.e., throughput and delay, i…
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation
Xu Chen, Zida Cheng, Jiangchao Yao +5
Cross-domain CTR (CDCTR) prediction is an important research topic that studies how to leverage meaningful data from a related domain to help CTR prediction in target domain. Most…