10 citations · 19 across the 20 of their papers we have counts for
23 papers
A Lightweight Velocity Correction Framework for Improving Instruction Following in Few-Step Text-to-Image Generation
Yating Gao, Zhenlong Zhu, Wen Zihao +6
Few-step text-to-image generators produce high-quality images with few network forward passes, yet struggle with complex prompts involving multiple subjects, interactions, spatial…
Trace, Verify, and Correct: A Training-Free Framework for Spatial Reasoning in Multimodal LLMs
Yang Yang, Jiawei Chen, Tairan Chen +1
Although Multimodal Large Language Models (MLLMs) have made substantial progress, their spatial reasoning may still produce intermediate judgments inconsistent with the input image…
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs
Rosie Zhao, Anshul Shah, Xiaoyu Zhu +5
Reinforcement learning (RL) finetuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivating its extension to vision-langua…
LongCat-Flash-Thinking-2601 Technical Report
Meituan LongCat Team, Anchun Gui, Bei Li +162
We introduce LongCat-Flash-Thinking-2601, a 560-billion-parameter open-source Mixture-of-Experts (MoE) reasoning model with superior agentic reasoning capability. LongCat-Flash-Thi…
FNF: Functional Network Fingerprint for Large Language Models
Yiheng Liu, Junhao Ning, Sichen Xia +8
The development of large language models (LLMs) is costly and has significant commercial value. Consequently, preventing unauthorized appropriation of open-source LLMs and protecti…
-Reader: Dual Evolving Graphs for Multimodal Document QA
Yaxin Du, Junru Song, Yifan Zhou +8
Retrieval-augmented generation is a practical paradigm for question answering over long documents, but it remains brittle for multimodal reading where text, tables, and figures are…
MedVL-SAM2: A unified 3D medical vision-language model for multimodal reasoning and prompt-driven segmentation
Yang Xing, Jiong Wu, Savas Ozdemir +4
Recent progress in medical vision-language models (VLMs) has achieved strong performance on image-level text-centric tasks such as report generation and visual question answering (…
MeshRipple: Structured Autoregressive Generation of Artist-Meshes
Junkai Lin, Hang Long, Huipeng Guo +8
Meshes serve as a primary representation for 3D assets. Autoregressive mesh generators serialize faces into sequences and train on truncated segments with sliding-window inference…
AdaptVision: Efficient Vision-Language Models via Adaptive Visual Acquisition
Zichuan Lin, Yicheng Liu, Yang Yang +2
Vision-Language Models (VLMs) have achieved remarkable success in visual question answering tasks, but their reliance on large numbers of visual tokens introduces significant compu…
Red Teaming Large Reasoning Models
Jiawei Chen, Yang Yang, Chao Yu +6
Large Reasoning Models (LRMs) have emerged as a powerful advancement in multi-step reasoning tasks, offering enhanced transparency and logical consistency through explicit chains o…
CameraMaster: Unified Camera Semantic-Parameter Control for Photography Retouching
Qirui Yang, Yang Yang, Ying Zeng +5
Text-guided diffusion models have greatly advanced image editing and generation. However, achieving physically consistent image retouching with precise parameter control (e.g., exp…
Dual-Head Reasoning Distillation: Improving Classifier Accuracy with Train-Time-Only Reasoning
Jillian Xu, Dylan Zhou, Vinay Shukla +6
Chain-of-Thought (CoT) prompting often improves classification accuracy, but it introduces a significant throughput penalty with rationale generation (Wei et al., 2022; Cheng and V…
LongCat-Flash Technical Report
Meituan LongCat Team, Bayan, Bei Li +179
We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capabilities. Stemming f…
DATE: Dynamic Absolute Time Enhancement for Long Video Understanding
Chao Yuan, Yang Yang, Yehui Yang +1
Long video understanding remains a fundamental challenge for multimodal large language models (MLLMs), particularly in tasks requiring precise temporal reasoning and event localiza…
What Can We Learn from Harry Potter? An Exploratory Study of Visual Representation Learning from Atypical Videos
Qiyue Sun, Qiming Huang, Yang Yang +2
Humans usually show exceptional generalisation and discovery ability in the open world, when being shown uncommon new concepts. Whereas most existing studies in the literature focu…
Step-Audio 2 Technical Report
Boyong Wu, Chao Yan, Chen Hu +106
This paper presents Step-Audio 2, an end-to-end multi-modal large language model designed for industry-strength audio understanding and speech conversation. By integrating a latent…
RAP: Real-time Audio-driven Portrait Animation with Video Diffusion Transformer
Fangyu Du, Taiqing Li, Qian Qiao +7
Audio-driven portrait animation aims to synthesize realistic and natural talking head videos from an input audio signal and a single reference image. While existing methods achieve…
Separate Source Channel Coding Is Still What You Need: An LLM-based Rethinking
Tianqi Ren, Rongpeng Li, Ming-min Zhao +5
Along with the proliferating research interest in Semantic Communication (SemCom), Joint Source Channel Coding (JSCC) has dominated the attention due to the widely assumed existenc…
Semi-Supervised Image Captioning Considering Wasserstein Graph Matching
Yang Yang
Image captioning can automatically generate captions for the given images, and the key challenge is to learn a mapping function from visual features to natural language features. E…
Large Language Models are Contrastive Reasoners
Liang Yao
Prompting methods play a crucial role in enhancing the capabilities of pre-trained large language models (LLMs). We explore how contrastive prompting (CP) significantly improves th…
Sora as a World Model? A Complete Survey on Text-to-Video Generation
Fachrina Dewi Puspitasari, Chaoning Zhang, Joseph Cho +13
The evolution of video generation from text, from animating MNIST to simulating the world with Sora, has progressed at a breakneck speed. Here, we systematically discuss how far te…
Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method
Tian Xia, Zhiwei He, Tong Ren +4
Bargaining is an important and unique part of negotiation between humans. As LLM-driven agents learn to negotiate and act like real humans, how to evaluate agents' bargaining abili…