Tootfinder

Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning
Yana Wei, Liang Zhao, Jianjian Sun, Kangheng Lin, Jisheng Yin, Jingcheng Hu, Yinmin Zhang, En Yu, Haoran Lv, Zejia Weng, Jia Wang, Chunrui Han, Yuang Peng, Qi Han, Zheng Ge, Xiangyu Zhang, Daxin Jiang, Vishal M. Patel
https://arxiv.org/abs/2507…

Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning
The remarkable reasoning capability of large language models (LLMs) stems from cognitive behaviors that emerge through reinforcement with verifiable rewards. This work investigates how to transfer this principle to Multimodal LLMs (MLLMs) to unlock advanced visual reasoning. We introduce a two-stage paradigm built on Qwen2.5-VL-7B: a massive linguistic cold-start fine-tuning, followed by multimodal reinforcement learning (RL) spanning nearly 1,000 steps, surpassing all previous open-source effo…

MoLink: Distributed and Efficient Serving Framework for Large Models
Lewei Jin, Yongqi Chen, Kui Zhang, Yifan Zhuo, Yi Gao, Bowei Yang, Zhengong Cai, Wei Dong
https://arxiv.org/abs/2507.05043

MoLink: Distributed and Efficient Serving Framework for Large Models
Large language models represent a groundbreaking shift in generative AI. Yet, these advances come with a significant challenge: the high cost of model serving. To mitigate these costs, consumer-grade GPUs emerge as a more affordable alternative. This presents an opportunity for more cost-efficient LLM serving by leveraging these GPUs. However, it is non-trivial to achieve high-efficiency LLM serving on consumer-grade GPUs, mainly due to two challenges: 1) these GPUs are often deployed in limi…

On the Comprehensibility of Multi-structured Financial Documents using LLMs and Pre-processing Tools
Shivani Upadhyay, Messiah Ataey, Shariyar Murtuza, Yifan Nie, Jimmy Lin
https://arxiv.org/abs/2506.05182

On the Comprehensibility of Multi-structured Financial Documents using LLMs and Pre-processing Tools
The proliferation of complex structured data in hybrid sources, such as PDF documents and web pages, presents unique challenges for current Large Language Models (LLMs) and Multi-modal Large Language Models (MLLMs) in providing accurate answers. Despite the recent advancements of MLLMs, they still often falter when interpreting intricately structured information, such as nested tables and multi-dimensional plots, leading to hallucinations and erroneous outputs. This paper explores the capabilit…

This https://arxiv.org/abs/2505.05098 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

X-Driver: Explainable Autonomous Driving with Vision-Language Models
End-to-end autonomous driving has advanced significantly, offering benefits such as system simplicity and stronger driving performance in both open-loop and closed-loop settings than conventional pipelines. However, existing frameworks still suffer from low success rates in closed-loop evaluations, highlighting their limitations in real-world deployment. In this paper, we introduce X-Driver, a unified multi-modal large language models(MLLMs) framework designed for closed-loop autonomous driving…

Millions of low-income Americans could experience
🆘 staggering financial losses
under the domestic policy package that Republicans advanced through the Senate on Tuesday,
💥which reserves its greatest benefits for the rich while threatening to strip health insurance, food stamps and other aid from the poor.
For many of these families, the loss of critical federal support is likely to negate any improvements they might have seen as a result of slightly lower taxes, expe…

Poorest Americans Would Be Hurt By Trump’s Big Bill
Small improvements in taxes are overshadowed by cuts to health insurance and other federal aid, resulting in a package favoring the wealthy.

This https://arxiv.org/abs/2505.19901 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Dynamic-I2V: Exploring Image-to-Video Generaion Models via Multimodal LLM
Recent advancements in image-to-video (I2V) generation have shown promising performance in conventional scenarios. However, these methods still encounter significant challenges when dealing with complex scenes that require a deep understanding of nuanced motion and intricate object-action relationships. To address these challenges, we present Dynamic-I2V, an innovative framework that integrates Multimodal Large Language Models (MLLMs) to jointly encode visual and textual conditions for a diffus…

MMReason: An Open-Ended Multi-Modal Multi-Step Reasoning Benchmark for MLLMs Toward AGI
Huanjin Yao, Jiaxing Huang, Yawen Qiu, Michael K. Chen, Wenzheng Liu, Wei Zhang, Wenjie Zeng, Xikun Zhang, Jingyi Zhang, Yuxin Song, Wenhao Wu, Dacheng Tao
https://arxiv.org/abs/2506.23563

MMReason: An Open-Ended Multi-Modal Multi-Step Reasoning Benchmark for MLLMs Toward AGI
Reasoning plays a crucial role in advancing Multimodal Large Language Models (MLLMs) toward Artificial General Intelligence. However, existing MLLM benchmarks often fall short in precisely and comprehensively evaluating long-chain reasoning abilities from three key aspects: (1) lack of difficulty and diversity, (2) susceptibility to guessability and memorization, (3) inadequate assessment of intermediate reasoning steps. To fill this gap, we introduce MMReason, a new benchmark designed to preci…

This https://arxiv.org/abs/2412.18407 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A Statistical Framework for Ranking LLM-Based Chatbots
Large language models (LLMs) have transformed natural language processing, with frameworks like Chatbot Arena providing pioneering platforms for evaluating these models. By facilitating millions of pairwise comparisons based on human judgments, Chatbot Arena has become a cornerstone in LLM evaluation, offering rich datasets for ranking models in open-ended conversational tasks. Building upon this foundation, we propose a statistical framework that incorporates key advancements to address specif…

This https://arxiv.org/abs/2505.24139 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation
The latest advancements in multi-modal large language models (MLLMs) have spurred a strong renewed interest in end-to-end motion planning approaches for autonomous driving. Many end-to-end approaches rely on human annotations to learn intermediate perception and prediction tasks, while purely self-supervised approaches--which directly learn from sensor inputs to generate planning trajectories without human annotations often underperform the state of the art. We observe a key gap in the input re…

Exact Distributions for the Solutions of the Compressible Viscous Navier Stokes Differential Equations: An Application in the Aeronautical Industry
Rensley A. Meulens
https://arxiv.org/abs/2506.23932

Exact Distributions for the Solutions of the Compressible Viscous Navier Stokes Differential Equations: An Application in the Aeronautical Industry
Wind tunnels and linearized turbulence and boundary-layer models have been so far necessary to simulate and approximate the stationery lift and drag forces on (base-mounted) airfoils by means of statistically determined or approximated values of the relevant situational coefficients as the drag and lift coefficients.To improve this process, we introduce transient and exact formulae to separate these forces in advance by means of the solutions found from the fluid dynamics model of the Navier St…