12 papers
Beyond Global Scalars: Synergizing Token-Level Statistics and Deep Semantics for Adversarial AIGC Text Detection
Peiming Li, Yifan Wang, Zhiyuan Hu +3
The rapid evolution of large language models necessitates robust machine-generated text detection. Existing paradigms typically follow two isolated tracks. Training-free methods re…
Conan-embedding-v3: Fusing Modality-Specific Models for Omni-Modal Embedding
Shiyu Li, Zhiyuan Hu, Yifan Wang +3
Omni-modal retrieval promises a single embedding space for text, image, video, document, and audio inputs, but building such a unified retriever is difficult since these modalities…
ProLaViT: Learning Progressive Latent Visual Thoughts in Structured Latent Space
Peiming Li, Yifan Wang, Xiaotian Zhang +4
Multimodal Large Language Models (MLLMs) have achieved remarkable progress but still struggle with complex visual reasoning tasks requiring multi-step perception and logical deduct…
Focus When Necessary: Adaptive Routing and Collaborative Grounding for Training-Free Visual Grounding
Yifan Wang, Peiming Li, Shiyu Li +5
While Multimodal Large Language Models (MLLMs) excel in cross-modal reasoning, they often struggle to perceive fine-grained details in complex high-resolution images. Recent traini…
Universal Skeleton Understanding via Differentiable Rendering and MLLMs
Ziyi Wang, Peiming Li, Xinshun Wang +3
Multimodal large language models (MLLMs) exhibit strong visual-language reasoning, yet cannot process structured, non-visual data such as human skeletons. Existing methods either c…
Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning
Yifan Wang, Shiyu Li, Peiming Li +3
Chain-of-Thought (CoT) prompting has achieved remarkable success in unlocking the reasoning capabilities of Large Language Models (LLMs). Although CoT prompting enhances reasoning,…
SPAN: Spatial-Projection Alignment for Monocular 3D Object Detection
Yifan Wang, Yian Zhao, Fanqi Pu +4
Existing monocular 3D detectors typically tame the pronounced nonlinear regression of 3D bounding box through decoupled prediction paradigm, which employs multiple branches to esti…
Finetune Once: Decoupling General & Domain Learning with Dynamic Boosted Annealing
Yang Tang, Ruijie Liu, Yifan Wang +2
Large language models (LLMs) fine-tuning shows excellent implications. However, vanilla fine-tuning methods often require intricate data mixture and repeated experiments for optima…
Aligning Deep Implicit Preferences by Learning to Reason Defensively
Peiming Li, Zhiyuan Hu, Yang Tang +2
Personalized alignment is crucial for enabling Large Language Models (LLMs) to engage effectively in user-centric interactions. However, current methods face a dual challenge: they…
ReSeek: A Self-Correcting Framework for Search Agents with Instructive Rewards
Shiyu Li, Yang Tang, Yifan Wang +2
Search agents powered by Large Language Models (LLMs) have demonstrated significant potential in tackling knowledge-intensive tasks. Reinforcement learning (RL) has emerged as a po…
Conan-Embedding-v2: Training an LLM from Scratch for Text Embeddings
Shiyu Li, Yang Tang, Ruijie Liu +2
Large language models (LLMs) have recently demonstrated excellent performance in text embedding tasks. Previous work usually use LoRA to fine-tune existing LLMs, which are limited…
Conan-embedding: General Text Embedding with More and Better Negative Samples
Shiyu Li, Yang Tang, Shizhe Chen +1
With the growing popularity of RAG, the capabilities of embedding models are gaining increasing attention. Embedding models are primarily trained through contrastive loss learning,…