2 citations · 2 across the 10 of their papers we have counts for
10 papers
MetaReason: Precise Interleaved Multimodal Reasoning via Editing Meta Information for Solving Geometry Problems
Penghao Yin, Haomin Wang, Qihong Tang +3
Although visual reasoning is crucial for solving complex geometry tasks, existing vision-language models rely heavily on text-only reasoning. Some recent methods introduce intermed…
AutoTrainess: Teaching Language Models to Improve Language Models Autonomously
Zhaojian Yu, Penghao Yin, Shuzheng Gao +3
Training language models (LMs) remains a highly human-intensive process, even as frontier language model agents become increasingly capable at software engineering and other long-h…
VEN-VL: A Visual Ensemble MoE Framework for Effective and Efficient Multi-Modal Understanding
Yinghao Wu, Zhuoyan Luo, Yiyao Yu +3
Despite the remarkable progress achieved by recent efficient methods in accelerating multimodal understanding, they still suffer from noticeable performance degradation. Their emph…
Causal Tongue-Tie: LLMs Can Encode Causal Direction, But Their Yes/No Outputs Fail to Express
Ziyi Ding, Xiao-Ping Zhang
We find a mismatch between what large language models encode about a causal question and what they answer. On anti-commonsense CLadder items, a fixed linear probe recovers the evid…
Reasoning to Align: Implicit Reasoning in Diffusion Transformers for Video Editing
Yan Li, Lin Liu, Xiaopeng Zhang +1
Instruction-based video editing requires transforming a source video according to a natural-language instruction while preserving irrelevant content and remaining temporally cohere…
Z1: Efficient Test-time Scaling with Code
Zhaojian Yu, Yinghao Wu, Yilun Zhao +2
Large Language Models (LLMs) can achieve enhanced complex problem-solving through test-time computing scaling, yet this often entails longer contexts and numerous reasoning token c…
HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation
Zhaojian Yu, Yilun Zhao, Arman Cohan +1
We introduce self-invoking code generation, a new task designed to evaluate the progressive reasoning and problem-solving capabilities of LLMs. In this task, models are presented w…
ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models
Xubing Ye, Yukang Gan, Yixiao Ge +2
Large Vision Language Models (LVLMs) have achieved significant success across multi-modal tasks. However, the computational cost of processing long visual tokens can be prohibitive…
OpenCarbonEval: A Unified Carbon Emission Estimation Framework in Large-Scale AI Models
Zhaojian Yu, Yinghao Wu, Zhuotao Deng +2
In recent years, large-scale auto-regressive models have made significant progress in various tasks, such as text or video generation. However, the environmental impact of these mo…
CoHD: A Counting-Aware Hierarchical Decoding Framework for Generalized Referring Expression Segmentation
Zhuoyan Luo, Yinghao Wu, Tianheng Cheng +5
The newly proposed Generalized Referring Expression Segmentation (GRES) amplifies the formulation of classic RES by involving complex multiple/non-target scenarios. Recent approach…