Tootfinder

Enabling MoE on the Edge via Importance-Driven Expert Scheduling
Guoying Zhu, Meng Li, Haipeng Dai, Xuechen Liu, Weijun Wang, Keran Li, Jun xiao, Ligeng Chen, Wei Wang
https://arxiv.org/abs/2508.18983 …

Enabling MoE on the Edge via Importance-Driven Expert Scheduling
The Mixture of Experts (MoE) architecture has emerged as a key technique for scaling Large Language Models by activating only a subset of experts per query. Deploying MoE on consumer-grade edge hardware, however, is constrained by limited device memory, making dynamic expert offloading essential. Unlike prior work that treats offloading purely as a scheduling problem, we leverage expert importance to guide decisions, substituting low-importance activated experts with functionally similar ones a…

Harnessing Rule-Based Reinforcement Learning for Enhanced Grammatical Error Correction
Yilin Li, Xunjian Yin, Yilin Chen, Xiaojun Wan
https://arxiv.org/abs/2508.18780 https://…

Harnessing Rule-Based Reinforcement Learning for Enhanced Grammatical Error Correction
Grammatical error correction is a significant task in NLP. Traditional methods based on encoder-decoder models have achieved certain success, but the application of LLMs in this field is still underexplored. Current research predominantly relies on supervised fine-tuning to train LLMs to directly generate the corrected sentence, which limits the model's powerful reasoning ability. To address this limitation, we propose a novel framework based on Rule-Based RL. Through experiments on the Chinese…

Impact of Target and Tool Visualization on Depth Perception and Usability in Optical See-Through AR
Yue Yang, Xue Xie, Xinkai Wang, Hui Zhang, Chiming Yu, Xiaoxian Xiong, Lifeng Zhu, Yuanyi Zheng, Jue Cen, Bruce Daniel, Fred Baik
https://arxiv.org/abs/2508.18481

Impact of Target and Tool Visualization on Depth Perception and Usability in Optical See-Through AR
Optical see-through augmented reality (OST-AR) systems like Microsoft HoloLens 2 hold promise for arm's distance guidance (e.g., surgery), but depth perception of the hologram and occlusion of real instruments remain challenging. We present an evaluation of how visualizing the target object with different transparencies and visualizing a tracked tool (virtual proxy vs. real tool vs. no tool tracking) affects depth perception and system usability. Ten participants performed two experiments on Ho…

Optimal Control of ODE Car-Following Models: Applications to Mixed-Autonomy Platoon Control via Coupled Autonomous Vehicles
Arwa Alanqary, Alexandre M. Bayen, Xiaoqian Gong, Anish Gollakota, Alexander Keimer, Ashish Pandian
https://arxiv.org/abs/2508.19417

Optimal Control of ODE Car-Following Models: Applications to Mixed-Autonomy Platoon Control via Coupled Autonomous Vehicles
In this paper, we study the optimal control of a mixed-autonomy platoon driving on a single lane to smooth traffic flow. The platoon consists of autonomous vehicles, whose acceleration is controlled, and human-driven vehicles, whose behavior is described using a microscopic car-following model. We formulate the optimal control problem where the dynamics of the platoon are describing through a system of non-linear ODEs, with explicit constraints on both the state and the control variables. Theor…

DTC: Real-Time and Accurate Distributed Triangle Counting in Fully Dynamic Graph Streams
Wei Xuan, Yan Liang, Huawei Cao, Ning Lin, Xiaochun Ye, Dongrui Fan
https://arxiv.org/abs/2508.19057

DTC: Real-Time and Accurate Distributed Triangle Counting in Fully Dynamic Graph Streams
Triangle counting is a fundamental problem in graph mining, essential for analyzing graph streams with arbitrary edge orders. However, exact counting becomes impractical due to the massive size of real-world graph streams. To address this, approximate algorithms have been developed, but existing distributed streaming algorithms lack adaptability and struggle with edge deletions. In this article, we propose DTC, a novel family of single-pass distributed streaming algorithms for global and local …

Pressure-mediated crystalline g-C3N4 with enhanced spatial charge transport for solar H2 evolution and photocathodic protection of 304 stainless steels
Xiaochun Gao, Shaoqi Hou, Dawei Su
https://arxiv.org/abs/2507.18914

Pressure-mediated crystalline g-C3N4 with enhanced spatial charge transport for solar H2 evolution and photocathodic protection of 304 stainless steels
Conjugated polymeric g-C3N4 has emerged as a leading semiconductor for solar-to-chemical energy conversion due to its unique electronic band structure, robust physicochemical stability, and environmental benignity. However, defect engineering-while effective at enhancing visible-light absorption and charge separation-often introduces excessive dangling bonds and lattice disorder, which exacerbate carrier recombination and impair light harvesting. High crystallinity offers a complementary route …

Decoding the Single-peaked HI Spectra of Low Redshift Post-starburst Galaxies
Qifeng Huang, Jing Wang, Sara L. Ellison, Zezhong Liang, Xuchen Lin, Dong Yang
https://arxiv.org/abs/2508.19545

Decoding the Single-peaked HI Spectra of Low Redshift Post-starburst Galaxies
Recent observations with the Five-hundred-meter Aperture Spherical Telescope (FAST) have revealed abundant reservoirs of neutral hydrogen (HI) in low redshift post-starburst galaxies (PSBs), raising the question of why star formation ceases rapidly in these systems. In this study, we present a detailed analysis of the shape of the integrated HI spectra of 67 PSBs. We find that PSBs exhibit significantly higher HI spectral concentration values ($K$) compared to a matched sample from xGASS, and a…

NewtonGen: Physics-Consistent and Controllable Text-to-Video Generation via Neural Newtonian Dynamics
Yu Yuan, Xijun Wang, Tharindu Wickremasinghe, Zeeshan Nadir, Bole Ma, Stanley H. Chan
https://arxiv.org/abs/2509.21309

NewtonGen: Physics-Consistent and Controllable Text-to-Video Generation via Neural Newtonian Dynamics
A primary bottleneck in large-scale text-to-video generation today is physical consistency and controllability. Despite recent advances, state-of-the-art models often produce unrealistic motions, such as objects falling upward, or abrupt changes in velocity and direction. Moreover, these models lack precise parameter control, struggling to generate physically consistent dynamics under different initial conditions. We argue that this fundamental limitation stems from current models learning moti…

Attention2Probability: Attention-Driven Terminology Probability Estimation for Robust Speech-to-Text System
Yanfan Du, Jun Zhang, Bin Wang, Jin Qiu, Lu Huang, Yuan Ge, Xiaoqian Liu, Tong Xiao, Jingbo Zhu
https://arxiv.org/abs/2508.18701

Attention2Probability: Attention-Driven Terminology Probability Estimation for Robust Speech-to-Text System
Recent advances in speech large language models (SLMs) have improved speech recognition and translation in general domains, but accurately generating domain-specific terms or neologisms remains challenging. To address this, we propose Attention2Probability: attention-driven terminology probability estimation for robust speech-to-text system, which is lightweight, flexible, and accurate. Attention2Probability converts cross-attention weights between speech and terminology into presence probabili…

LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generation
Yang Sun, Lixin Zou, Dan Luo, Zhiyong Xie, Long Zhang, Liming Dong, Yunwei Zhao, Xixun Lin, Yanxiong Lu, Chenliang Li
https://arxiv.org/abs/2508.19614

LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) incorporates external knowledge into large language models (LLMs), improving their adaptability to downstream tasks and enabling information updates. Surprisingly, recent empirical evidence demonstrates that injecting noise into retrieved relevant documents paradoxically facilitates exploitation of external knowledge and improves generation quality. Although counterintuitive and challenging to apply in practice, this phenomenon enables granular control and r…