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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…