Papers
Research papers from arXiv and related sources
AnimeAgent: Is the Multi-Agent via Image-to-Video models a Good Disney Storytelling Artist?
Custom Storyboard Generation (CSG) aims to produce high-quality, multi-character consistent storytelling. Current approaches based on static diffusion models, whether used in a one-shot manner or w...
Hailong Yan, Shice Liu, Tao Wang, Xiangtao Zhang, Yijie Zhong, Jinwei Chen, Le Zhang, Bo Li
ICSSPulse: A Modular LLM-Assisted Platform for Industrial Control System Penetration Testing
It is well established that industrial control systems comprise the operational backbone of modern critical infrastructures, yet their increasing connectivity exposes them to cyber threats that are...
Michail Takaronis, Athanasia Kollarou, Vyron Kampourakis, Vasileios Gkioulos, Sokratis Katsikas
TOM: A Ternary Read-only Memory Accelerator for LLM-powered Edge Intelligence
The deployment of Large Language Models (LLMs) for real-time intelligence on edge devices is rapidly growing. However, conventional hardware architectures face a fundamental memory wall challenge, ...
Hongyi Guan, Yijia Zhang, Wenqiang Wang, Yizhao Gao, Shijie Cao, Chen Zhang, Ningyi Xu
Lagom: Unleashing the Power of Communication and Computation Overlapping for Distributed LLM Training
Overlapping communication with computation is crucial for distributed large-model training, yet optimizing it - especially when computation becomes the bottleneck-remains challenging. We present La...
Guanbin Xu, ZhenGuo Xu, Yuzhe Li, Youhui Bai, Ping Gong, Chaoyi Ruan, Cheng Li
PLATOSpec's first results: Three new transiting warm Jupiters from the WINE survey TIC 147027702, TIC 245076932 and TIC 87422071
We report the discovery and characterisation of three transiting warm Jupiters: TIC 147027702b, TIC 245076932b and TIC 87422071b. These systems were initially identified as transiting candidates us...
Pavol Gajdoš, Rafael Brahm, Lorena Acuña-Aguirre, Matías I. Jones, Helem Salinas, Jozef Lipták, A...
DANCE: Doubly Adaptive Neighborhood Conformal Estimation
The recent developments of complex deep learning models have led to unprecedented ability to accurately predict across multiple data representation types. Conformal prediction for uncertainty quant...
Brandon R. Feng, Brian J. Reich, Daniel Beaglehole, Xihaier Luo, David Keetae Park, Shinjae Yoo, ...
CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Models
Client perceptions of the therapeutic alliance are critical for counseling effectiveness. Accurately capturing these perceptions remains challenging, as traditional post-session questionnaires are ...
Anqi Li, Chenxiao Wang, Yu Lu, Renjun Xu, Lizhi Ma, Zhenzhong Lan
An LLM-driven Scenario Generation Pipeline Using an Extended Scenic DSL for Autonomous Driving Safety Validation
Real-world crash reports, which combine textual summaries and sketches, are valuable for scenario-based testing of autonomous driving systems (ADS). However, current methods cannot effectively tran...
Fida Khandaker Safa, Yupeng Jiang, Xi Zheng
Grounding LLMs in Scientific Discovery via Embodied Actions
Large Language Models (LLMs) have shown significant potential in scientific discovery but struggle to bridge the gap between theoretical reasoning and verifiable physical simulation. Existing solut...
Bo Zhang, Jinfeng Zhou, Yuxuan Chen, Jianing Yin, Minlie Huang, Hongning Wang
AI Combines, Humans Socialise: A SECI-based Experience Report on Business Simulation Games
Background. Business Simulation Games (BSG) are widely used to foster experiential learning in complex managerial and organisational contexts by exposing students to decision-making under uncertain...
Nordine Benkeltoum
QEDBENCH: Quantifying the Alignment Gap in Automated Evaluation of University-Level Mathematical Proofs
As Large Language Models (LLMs) saturate elementary benchmarks, the research frontier has shifted from generation to the reliability of automated evaluation. We demonstrate that standard "LLM-as-a-...
Santiago Gonzalez, Alireza Amiri Bavandpour, Peter Ye, Edward Zhang, Ruslans Aleksejevs, Todor An...
When can we trust untrusted monitoring? A safety case sketch across collusion strategies
AIs are increasingly being deployed with greater autonomy and capabilities, which increases the risk that a misaligned AI may be able to cause catastrophic harm. Untrusted monitoring -- using one u...
Nelson Gardner-Challis, Jonathan Bostock, Georgiy Kozhevnikov, Morgan Sinclaire, Joan Velja, Ales...
Physics-based phenomenological characterization of cross-modal bias in multimodal models
The term 'algorithmic fairness' is used to evaluate whether AI models operate fairly in both comparative (where fairness is understood as formal equality, such as "treat like cases as like") and no...
Hyeongmo Kim, Sohyun Kang, Yerin Choi, Seungyeon Ji, Junhyuk Woo, Hyunsuk Chung, Soyeon Caren Han...
A Low Cost Picoseconds Precision Timing and Synchronization Over A Hundred Kilometer
Large-scale systems, such as very large accelerators used for fundamental research, require the implementation of precise timing and synchronization systems over distances of several tens of kilome...
Alice Renaux, Ronic Chiche, A. Martens, Antoine Back, Paul-Éric Pottie, Daniel Charlet
RecoverMark: Robust Watermarking for Localization and Recovery of Manipulated Faces
The proliferation of AI-generated content has facilitated sophisticated face manipulation, severely undermining visual integrity and posing unprecedented challenges to intellectual property. In res...
Haonan An, Xiaohui Ye, Guang Hua, Yihang Tao, Hangcheng Cao, Xiangyu Yu, Yuguang Fang
Amortized Bayesian inference for actigraph time sheet data from mobile devices
Mobile data technologies use ``actigraphs'' to furnish information on health variables as a function of a subject's movement. The advent of wearable devices and related technologies has propelled t...
Daniel Zhou, Sudipto Banerjee
SpecMind: Cognitively Inspired, Interactive Multi-Turn Framework for Postcondition Inference
Specifications are vital for ensuring program correctness, yet writing them manually remains challenging and time-intensive. Recent large language model (LLM)-based methods have shown successes in ...
Cuong Chi Le, Minh V. T Pham, Tung Vu Duy, Cuong Duc Van, Huy N. Phan, Hoang N. Phan, Tien N. Nguyen
Correlator-Level Verification of Mass and Current Maps in Abelian Chern-Simons Dualities
We construct an explicit local operator realization that reproduces Dirac fermion correlation functions in three spacetime dimensions within an Abelian Chern-Simons framework and use it to examine ...
Vaibhav Wasnik
A Case Study on Runtime Verification of a Continuous Deployment Process
We report our experience in applying runtime monitoring to a FluxCD-based continuous deployment (CD) process. Our target system consists of GitHub Actions, GitHub Container Registry (GHCR), FluxCD,...
Shoma Ansai, Masaki Waga
OptiLeak: Efficient Prompt Reconstruction via Reinforcement Learning in Multi-tenant LLM Services
Multi-tenant LLM serving frameworks widely adopt shared Key-Value caches to enhance efficiency. However, this creates side-channel vulnerabilities enabling prompt leakage attacks. Prior studies ide...
Longxiang Wang, Xiang Zheng, Xuhao Zhang, Yao Zhang, Ye Wu, Cong Wang