10 citations · 19 across the 20 of their papers we have counts for
8 papers · 1 filter
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 (…