Papers
Research papers from arXiv and related sources
ROSE: Reordered SparseGPT for More Accurate One-Shot Large Language Models Pruning
Pruning is widely recognized as an effective method for reducing the parameters of large language models (LLMs), potentially leading to more efficient deployment and inference. One classic and prom...
Mingluo Su, Huan Wang
Shifting Adaptation from Weight Space to Memory Space: A Memory-Augmented Agent for Medical Image Segmentation
Medical image segmentation is fundamental to clinical workflows, yet models trained on a single dataset often fail to generalize across institutions, scanners, or patient populations. While vision ...
Bowen Chen, Qiaohui Gao, Shaowen Wan, Shanhui Sun, Wei Liu, Xiang Li, Tianming Liu, Lin Zhao
Evolving Deception: When Agents Evolve, Deception Wins
Self-evolving agents offer a promising path toward scalable autonomy. However, in this work, we show that in competitive environments, self-evolution can instead give rise to a serious and previous...
Zonghao Ying, Haowen Dai, Tianyuan Zhang, Yisong Xiao, Quanchen Zou, Aishan Liu, Jian Yang, Yaodo...
Challenges in Synchronous & Remote Collaboration Around Visualization
We characterize 16 challenges faced by those investigating and developing remote and synchronous collaborative experiences around visualization. Our work reflects the perspectives and prior researc...
Matthew Brehmer, Maxime Cordeil, Christophe Hurter, Takayuki Itoh, Wolfgang Büschel, Mahmood Jasi...
AnyCamVLA: Zero-Shot Camera Adaptation for Viewpoint Robust Vision-Language-Action Models
Despite remarkable progress in Vision-Language-Action models (VLAs) for robot manipulation, these large pre-trained models require fine-tuning to be deployed in specific environments. These fine-tu...
Hyeongjun Heo, Seungyeon Woo, Sang Min Kim, Junho Kim, Junho Lee, Yonghyeon Lee, Young Min Kim
ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning
While Large Language Models (LLMs) have revolutionized code generation, standard "System 1" approaches, generating solutions in a single forward pass, often hit a performance ceiling when faced wit...
Juyong Jiang, Jiasi Shen, Sunghun Kim, Kang Min Yoo, Jeonghoon Kim, Sungju Kim
Evolving Medical Imaging Agents via Experience-driven Self-skill Discovery
Clinical image interpretation is inherently multi-step and tool-centric: clinicians iteratively combine visual evidence with patient context, quantify findings, and refine their decisions through a...
Lin Fan, Pengyu Dai, Zhipeng Deng, Haolin Wang, Xun Gong, Yefeng Zheng, Yafei Ou
How Well Do Current Speech Deepfake Detection Methods Generalize to the Real World?
Recent advances in speech synthesis and voice conversion have greatly improved the naturalness and authenticity of generated audio. Meanwhile, evolving encoding, compression, and transmission mecha...
Daixian Li, Jun Xue, Yanzhen Ren, Zhuolin Yi, Yihuan Huang, Guanxiang Feng, Yi Chai
The Values of Value in AI Adoption: Rethinking Efficiency in UX Designers' Workplaces
Although organizations increasingly position AI adoption as a pathway to competitiveness and innovation, organizations' perspectives on productivity and efficiency often clash with workers' perspec...
Inha Cha, Catherine Wieczorek, Richmond Y. Wong
Evaluating LLM Alignment With Human Trust Models
Trust plays a pivotal role in enabling effective cooperation, reducing uncertainty, and guiding decision-making in both human interactions and multi-agent systems. Although it is significant, there...
Anushka Debnath, Stephen Cranefield, Bastin Tony Roy Savarimuthu, Emiliano Lorini
Multi-Segment Consistency Tests of General Relativity
As the LIGO-VIRGO-KAGRA Network of gravitational-wave detectors improves in sensitivity, accumulating hundreds of gravitational-wave detections per year, it becomes imperative to improve tests of g...
Vaishak Prasad
Lexara: A User-Centered Toolkit for Evaluating Large Language Models for Conversational Visual Analytics
Large Language Models (LLMs) are transforming Conversational Visual Analytics (CVA) by enabling data analysis through natural language. However, evaluating LLMs for CVA remains a challenge: requiri...
Srishti Palani, Vidya Setlur
Knowledge-driven Reasoning for Mobile Agentic AI: Concepts, Approaches, and Directions
Mobile agentic AI is extending autonomous capabilities to resource-constrained platforms such as edge robots and unmanned aerial vehicles (UAVs), where strict size, weight, power, and cost (SWAP-C)...
Guangyuan Liu, Changyuan Zhao, Yinqiu Liu, Dusit Niyato, Biplab Sikdar
Test-Time Adaptation via Many-Shot Prompting: Benefits, Limits, and Pitfalls
Test-time adaptation enables large language models (LLMs) to modify their behavior at inference without updating model parameters. A common approach is many-shot prompting, where large numbers of i...
Shubhangi Upasani, Chen Wu, Jay Rainton, Bo Li, Changran Hu, Qizheng Zhang, Urmish Thakker
HART: Data-Driven Hallucination Attribution and Evidence-Based Tracing for Large Language Models
Large language models (LLMs) have demonstrated remarkable performance in text generation and knowledge-intensive question answering. Nevertheless, they are prone to producing hallucinated content, ...
Shize Liang, Hongzhi Wang
Self-Auditing Parameter-Efficient Fine-Tuning for Few-Shot 3D Medical Image Segmentation
Adapting foundation models to new clinical sites remains challenging in practice. Domain shift and scarce annotations must be handled by experts, yet many clinical groups do not have ready access t...
Son Thai Ly, Hien V. Nguyen
ImKWS: Test-Time Adaptation for Keyword Spotting with Class Imbalance
Keyword spotting (KWS) identifies words for voice assistants, but environmental noise frequently reduces accuracy. Standard adaptation fixes this issue and strictly requires original or labeled aud...
Hanyu Ding, Yang Xiao, Jiaheng Dong, Ting Dang
Which Data Matter? Embedding-Based Data Selection for Speech Recognition
Modern ASR systems are typically trained on large-scale pseudo-labeled, in-the-wild data spanning multiple domains. While such heterogeneous data benefit generalist models designed for broad deploy...
Zakaria Aldeneh, Skyler Seto, Maureen de Seyssel, Jie Chi, Zijin Gu, Takuya Higuchi, Jee-weon Jun...
Two Localization Strategies for Sequential MCMC Data Assimilation with Applications to Nonlinear Non-Gaussian Geophysical Models
We present a localized data assimilation (DA) scheme based on the sequential Markov Chain Monte Carlo (SMCMC) technique [Ruzayqat et al., 2024], a provably convergent method for filtering high-dime...
Hamza Ruzayqat, Hristo G. Chipilski, Omar Knio
Nonlinear Conjugate Gradient Method for Multiobjective Optimization Problems of Interval-Valued Maps
In this article, we propose an algorithm for the nonlinear conjugate gradient method to find a Pareto critical point of unconstrained multiobjective interval optimization problems. In this algorith...
Tapas Mondal, Debdas Ghosh, Jingxin Liu, Jie Li