Tootfinder

GlobalizeEd: A Multimodal Translation System that Preserves Speaker Identity in Academic Lectures
Hoang-Son Vo, Karina Kolmogortseva, Ngumimi Karen Iyortsuun, Hong-Duyen Vo, Soo-Hyung Kim
https://arxiv.org/abs/2510.11596

GlobalizeEd: A Multimodal Translation System that Preserves Speaker Identity in Academic Lectures
A large amount of valuable academic content is only available in its original language, creating a significant access barrier for the global student community. This is a challenge for translating in several subjects, such as history, culture, and the arts, where current automated subtitle tools fail to convey the appropriate pedagogical tone and specialized meaning. In addition, reading traditional automated subtitles increases cognitive load and leads to a disconnected learning experience. Thr…

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation
Xiangxu Zhang, Lei Li, Yanyun Zhou, Xiao Zhou, Yingying Zhang, Xian Wu
https://arxiv.org/abs/2510.09275

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation
Medical diagnostics is a high-stakes and complex domain that is critical to patient care. However, current evaluations of large language models (LLMs) are fundamentally misaligned with real-world clinical practice. Most of them rely on static benchmarks derived from public medical exam items, which tend to overestimate model performance and ignore the difference between textbook cases and the ambiguous, varying conditions in the real world. Recent efforts toward dynamic evaluation offer a promi…

Dynamic Line Ratings in AC Optimal Power Flow: Transient Temperature, Decomposition, and Large-scale Evaluation
Baptiste Rabecq, Thomas Lee, Andy Sun
https://arxiv.org/abs/2510.10832

Dynamic Line Ratings in AC Optimal Power Flow: Transient Temperature, Decomposition, and Large-scale Evaluation
As power grids experience increasing renewable penetration and rapid load growth from AI data centers and electrification, alleviating line congestion becomes critical to unlocking additional grid capacity. This work investigates Dynamic Line Rating (DLR), a congestion mitigation method that adjusts power line current limits in response to meteorological conditions. Unlike traditional approaches that impose predefined time-varying limits, we propose a novel optimization framework that embeds th…

HERO: Hardware-Efficient RL-based Optimization Framework for NeRF Quantization
Yipu Zhang, Chaofang Ma, Jinming Ge, Lin Jiang, Jiang Xu, Wei Zhang
https://arxiv.org/abs/2510.09010

HERO: Hardware-Efficient RL-based Optimization Framework for NeRF Quantization
Neural Radiance Field (NeRF) has emerged as a promising 3D reconstruction method, delivering high-quality results for AR/VR applications. While quantization methods and hardware accelerators have been proposed to enhance NeRF's computational efficiency, existing approaches face crucial limitations. Current quantization methods operate without considering hardware architecture, resulting in sub-optimal solutions within the vast design space encompassing accuracy, latency, and model size. Additio…

pyspect: An Extensible Toolbox for Automatic Construction of Temporal Logic Trees via Reachability Analysis
Kaj Munhoz Arfvidsson, Loizos Hadjiloizou, Frank J. Jiang, Karl H. Johansson, Jonas M{\aa}rtensson
https://arxiv.org/abs/2510.11316

pyspect: An Extensible Toolbox for Automatic Construction of Temporal Logic Trees via Reachability Analysis
In this paper, we present pyspect, a Python toolbox that simplifies the use of reachability analysis for temporal logic problems. Currently, satisfying complex requirements in cyber-physical systems requires significant manual effort and domain expertise to develop the underlying reachability programs. This high development effort limits the broader adoption of reachability analysis for complex verification problems. To address this, pyspect provides a method-agnostic approach to performing rea…

RefineShot: Rethinking Cinematography Understanding with Foundational Skill Evaluation
Hang Wu, Yujun Cai, Haonan Ge, Hongkai Chen, Ming-Hsuan Yang, Yiwei Wang
https://arxiv.org/abs/2510.02423

RefineShot: Rethinking Cinematography Understanding with Foundational Skill Evaluation
Cinematography understanding refers to the ability to recognize not only the visual content of a scene but also the cinematic techniques that shape narrative meaning. This capability is attracting increasing attention, as it enhances multimodal understanding in real-world applications and underpins coherent content creation in film and media. As the most comprehensive benchmark for this task, ShotBench spans a wide range of cinematic concepts and VQA-style evaluations, with ShotVL achieving sta…

SATER: A Self-Aware and Token-Efficient Approach to Routing and Cascading
Yuanzhe Shen, Yide Liu, Zisu Huang, Ruicheng Yin, Xiaoqing Zheng, Xuanjing Huang
https://arxiv.org/abs/2510.05164

SATER: A Self-Aware and Token-Efficient Approach to Routing and Cascading
Large language models (LLMs) demonstrate remarkable performance across diverse tasks, yet their effectiveness frequently depends on costly commercial APIs or cloud services. Model selection thus entails a critical trade-off between performance and cost: high-performing LLMs typically incur substantial expenses, whereas budget-friendly small language models (SLMs) are constrained by limited capabilities. Current research primarily proposes two routing strategies: pre-generation routing and casca…

Rethinking Thinking Tokens: LLMs as Improvement Operators
Lovish Madaan, Aniket Didolkar, Suchin Gururangan, John Quan, Ruan Silva, Ruslan Salakhutdinov, Manzil Zaheer, Sanjeev Arora, Anirudh Goyal
https://arxiv.org/abs/2510.01123

Rethinking Thinking Tokens: LLMs as Improvement Operators
Reasoning training incentivizes LLMs to produce long chains of thought (long CoT), which among other things, allows them to explore solution strategies with self-checking. This results in higher accuracy, but inflates context length, token/compute cost, and answer latency. We ask: Can current models leverage their metacognition to provide other combinations on this Pareto frontier, e.g., better accuracy with lower context length and/or latency? Abstractly, we view the model as an improvement op…

Bridging Text and Video Generation: A Survey
Nilay Kumar, Priyansh Bhandari, G. Maragatham
https://arxiv.org/abs/2510.04999 https://arxiv.org/pdf/2510.0499…

Bridging Text and Video Generation: A Survey
Text-to-video (T2V) generation technology holds potential to transform multiple domains such as education, marketing, entertainment, and assistive technologies for individuals with visual or reading comprehension challenges, by creating coherent visual content from natural language prompts. From its inception, the field has advanced from adversarial models to diffusion-based models, yielding higher-fidelity, temporally consistent outputs. Yet challenges persist, such as alignment, long-range co…

Grouped Satisficing Paths in Pure Strategy Games: a Topological Perspective
Yanqing Fu, Chao Huang, Chenrun Wang, Zhuping Wang
https://arxiv.org/abs/2509.23157 https://

Grouped Satisficing Paths in Pure Strategy Games: a Topological Perspective
In game theory and multi-agent reinforcement learning (MARL), each agent selects a strategy, interacts with the environment and other agents, and subsequently updates its strategy based on the received payoff. This process generates a sequence of joint strategies $(s^t)_{t \geq 0}$, where $s^t$ represents the strategy profile of all agents at time step $t$. A widely adopted principle in MARL algorithms is "win-stay, lose-shift", which dictates that an agent retains its current strategy if it ac…