Tootfinder

RefGrader: Automated Grading of Mathematical Competition Proofs using Agentic Workflows
Hamed Mahdavi (Pennsylvania State University), Pouria Mahdavinia (Pennsylvania State University), Samira Malek (Pennsylvania State University), Pegah Mohammadipour (Pennsylvania State University), Alireza Hashemi (City University of New York), Majid Daliri (New York University), Alireza Farhadi (Amirkabir University of Technology), Amir Khasahmadi (Autodesk), Niloofar Mireshghallah (Carnegie Mellon …

RefGrader: Automated Grading of Mathematical Competition Proofs using Agentic Workflows
State-of-the-art (SOTA) LLMs have progressed from struggling on proof-based Olympiad problems to solving most of the IMO 2025 problems, with leading systems reportedly handling 5 of 6 problems. Given this progress, we assess how well these models can grade proofs: detecting errors, judging their severity, and assigning fair scores beyond binary correctness. We study proof-analysis capabilities using a corpus of 90 Gemini 2.5 Pro-generated solutions that we grade on a 1-4 scale with detailed err…

IntentionVLA: Generalizable and Efficient Embodied Intention Reasoning for Human-Robot Interaction
Yandu Chen, Kefan Gu, Yuqing Wen, Yucheng Zhao, Tiancai Wang, Liqiang Nie
https://arxiv.org/abs/2510.07778

IntentionVLA: Generalizable and Efficient Embodied Intention Reasoning for Human-Robot Interaction
Vision-Language-Action (VLA) models leverage pretrained vision-language models (VLMs) to couple perception with robotic control, offering a promising path toward general-purpose embodied intelligence. However, current SOTA VLAs are primarily pretrained on multimodal tasks with limited relevance to embodied scenarios, and then finetuned to map explicit instructions to actions. Consequently, due to the lack of reasoning-intensive pretraining and reasoning-guided manipulation, these models are una…

VelLMes: A high-interaction AI-based deception framework
Muris Sladi\'c (Czech Technical University in Prague), Veronica Valeros (Czech Technical University in Prague), Carlos Catania (CONICET, UNCuyo), Sebastian Garcia (Czech Technical University in Prague)
https://arxiv.org/abs/2510.06975

VelLMes: A high-interaction AI-based deception framework
There are very few SotA deception systems based on Large Language Models. The existing ones are limited only to simulating one type of service, mainly SSH shells. These systems - but also the deception technologies not based on LLMs - lack an extensive evaluation that includes human attackers. Generative AI has recently become a valuable asset for cybersecurity researchers and practitioners, and the field of cyber-deception is no exception. Researchers have demonstrated how LLMs can be leverage…

F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data
Ziyin Zhang, Zihan Liao, Hang Yu, Peng Di, Rui Wang
https://arxiv.org/abs/2510.02294 …

F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data
We introduce F2LLM - Foundation to Feature Large Language Models, a suite of state-of-the-art embedding models in three sizes: 0.6B, 1.7B, and 4B. Unlike previous top-ranking embedding models that require massive contrastive pretraining, sophisticated training pipelines, and costly synthetic training data, F2LLM is directly finetuned from foundation models on 6 million query-document-negative tuples curated from open-source, non-synthetic datasets, striking a strong balance between training cos…

Adversarial Attacks on Audio Deepfake Detection: A Benchmark and Comparative Study
Kutub Uddin, Muhammad Umar Farooq, Awais Khan, Khalid Mahmood Malik
https://arxiv.org/abs/2509.07132

Adversarial Attacks on Audio Deepfake Detection: A Benchmark and Comparative Study
The widespread use of generative AI has shown remarkable success in producing highly realistic deepfakes, posing a serious threat to various voice biometric applications, including speaker verification, voice biometrics, audio conferencing, and criminal investigations. To counteract this, several state-of-the-art (SoTA) audio deepfake detection (ADD) methods have been proposed to identify generative AI signatures to distinguish between real and deepfake audio. However, the effectiveness of thes…

Integrated Communication and Computing in Time-Varying mmWave Channels
Joan \c{C}ollaku, Kuranage Roche Rayan Ranasinghe, Giuseppe Thadeu Freitas de Abreu, Takumi Takahashi
https://arxiv.org/abs/2509.07482

Integrated Communication and Computing in Time-Varying mmWave Channels
We propose a novel framework for integrated communication and computing (ICC) transceiver design in time-varying millimeter-wave (mmWave) channels. In particular, in order to cope with the dynamics of time-varying mmWave channels, the detection of communication symbols and the execution of an over-the-air computing (AirComp) operation are performed in parallel with channel tracking, as opposed to existing state-of-the-art (SotA) on ICC where perfect knowledge of the channel at all time instance…

Real Time FPGA Based Transformers & VLMs for Vision Tasks: SOTA Designs and Optimizations
Safa Mohammed Sali, Mahmoud Meribout, Ashiyana Abdul Majeed
https://arxiv.org/abs/2509.04162

Real Time FPGA Based Transformers & VLMs for Vision Tasks: SOTA Designs and Optimizations
Transformers and vision-language models (VLMs) have emerged as dominant architectures in computer vision and multimodal AI, offering state-of-the-art performance in tasks such as image classification, object detection, visual question answering, and caption generation. However, their high computational complexity, large memory footprints, and irregular data access patterns present significant challenges for deployment in latency- and power-constrained environments. Field-programmable gate array…

Magneto-optical Kerr-effect measurements under pulsed magnetic fields over 40 T using a compact sample fixture
Atsutoshi Ikeda, Sota Nakamura, Soichiro Yamane, Kosuke Noda, Akihiko Ikeda, Shingo Yonezawa
https://arxiv.org/abs/2509.07383

Magneto-optical Kerr-effect measurements under pulsed magnetic fields over 40 T using a compact sample fixture
Magnetic field is one of the most fundamental control parameters in materials science. Pulsed magnetic-field apparatus can generate high magnetic fields that are inaccessible by conventional DC-field magnets. One important issue is that measurement techniques compatible with pulsed fields are rather limited due to short pulse duration and large electromagnetic or mechanical noises originating from field pulses. Magneto-optical Kerr effect (MOKE), change in states of light polarization upon refl…

FASL-Seg: Anatomy and Tool Segmentation of Surgical Scenes
Muraam Abdel-Ghani, Mahmoud Ali, Mohamed Ali, Fatmaelzahraa Ahmed, Mohamed Arsalan, Abdulaziz Al-Ali, Shidin Balakrishnan
https://arxiv.org/abs/2509.06159

FASL-Seg: Anatomy and Tool Segmentation of Surgical Scenes
The growing popularity of robotic minimally invasive surgeries has made deep learning-based surgical training a key area of research. A thorough understanding of the surgical scene components is crucial, which semantic segmentation models can help achieve. However, most existing work focuses on surgical tools and overlooks anatomical objects. Additionally, current state-of-the-art (SOTA) models struggle to balance capturing high-level contextual features and low-level edge features. We propose …

Cognition releases SWE-1.5, a new coding model in Windsurf, saying it partnered with Cerebras to serve SWE-1.5 at speeds up to 13x faster than Claude Sonnet 4.5 (Cognition)
https://cognition.ai/blog/swe-1-5

Cognition | Introducing SWE-1.5: Our Fast Agent Model
Today we’re releasing SWE-1.5, the latest in our family of models optimized for software engineering. It is a frontier-size model with hundreds of billions of parameters that achieves near-SOTA coding performance. It also sets a new standard for speed: we partnered with Cerebras to serve it at up to 950 tok/s – 6x faster than Haiku 4.5 and 13x faster than Sonnet 4.5. SWE-1.5 is now available in Windsurf!

Bridging integrated information theory and the free-energy principle in living neuronal networks
Teruki Mayama, Sota Shimizu, Yuki Takano, Dai Akita, Hirokazu Takahashi
https://arxiv.org/abs/2510.04084

Bridging integrated information theory and the free-energy principle in living neuronal networks
The relationship between Integrated Information Theory (IIT) and the Free-Energy Principle (FEP) remains unresolved, particularly with respect to how integrated information, proposed as the intrinsic substrate of consciousness, behaves within variational Bayesian inference. We investigated this issue using dissociated neuronal cultures, previously shown to perform perceptual inference consistent with the FEP. Repeated stimulation from hidden sources induced robust source selectivity: variationa…

Precise Dynamics of Diagonal Linear Networks: A Unifying Analysis by Dynamical Mean-Field Theory
Sota Nishiyama, Masaaki Imaizumi
https://arxiv.org/abs/2510.01930 https://

Precise Dynamics of Diagonal Linear Networks: A Unifying Analysis by Dynamical Mean-Field Theory
Diagonal linear networks (DLNs) are a tractable model that captures several nontrivial behaviors in neural network training, such as initialization-dependent solutions and incremental learning. These phenomena are typically studied in isolation, leaving the overall dynamics insufficiently understood. In this work, we present a unified analysis of various phenomena in the gradient flow dynamics of DLNs. Using Dynamical Mean-Field Theory (DMFT), we derive a low-dimensional effective process that …

Qwen3-Omni Technical Report
Jin Xu, Zhifang Guo, Hangrui Hu, Yunfei Chu, Xiong Wang, Jinzheng He, Yuxuan Wang, Xian Shi, Ting He, Xinfa Zhu, Yuanjun Lv, Yongqi Wang, Dake Guo, He Wang, Linhan Ma, Pei Zhang, Xinyu Zhang, Hongkun Hao, Zishan Guo, Baosong Yang, Bin Zhang, Ziyang Ma, Xipin Wei, Shuai Bai, Keqin Chen, Xuejing Liu, Peng Wang, Mingkun Yang, Dayiheng Liu, Xingzhang Ren, Bo Zheng, Rui Men, Fan Zhou, Bowen Yu, Jianxin Yang, Le Yu, Jingren Zhou, Junyang Lin

Qwen3-Omni Technical Report
We present Qwen3-Omni, a single multimodal model that, for the first time, maintains state-of-the-art performance across text, image, audio, and video without any degradation relative to single-modal counterparts. Qwen3-Omni matches the performance of same-sized single-modal models within the Qwen series and excels particularly on audio tasks. Across 36 audio and audio-visual benchmarks, Qwen3-Omni achieves open-source SOTA on 32 benchmarks and overall SOTA on 22, outperforming strong closed-so…

Timestep-Compressed Attack on Spiking Neural Networks through Timestep-Level Backpropagation
Donghwa Kang, Doohyun Kim, Sang-Ki Ko, Jinkyu Lee, Hyeongboo Baek, Brent ByungHoon Kang
https://arxiv.org/abs/2508.13812

Timestep-Compressed Attack on Spiking Neural Networks through Timestep-Level Backpropagation
State-of-the-art (SOTA) gradient-based adversarial attacks on spiking neural networks (SNNs), which largely rely on extending FGSM and PGD frameworks, face a critical limitation: substantial attack latency from multi-timestep processing, rendering them infeasible for practical real-time applications. This inefficiency stems from their design as direct extensions of ANN paradigms, which fail to exploit key SNN properties. In this paper, we propose the timestep-compressed attack (TCA), a novel fr…

Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Yifan Hu, Jie Yang, Tian Zhou, Peiyuan Liu, Yujin Tang, Rong Jin, Liang Sun
https://arxiv.org/abs/2509.14181

Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Representation learning techniques like contrastive learning have long been explored in time series forecasting, mirroring their success in computer vision and natural language processing. Yet recent state-of-the-art (SOTA) forecasters seldom adopt these representation approaches because they have shown little performance advantage. We challenge this view and demonstrate that explicit representation alignment can supply critical information that bridges the distributional gap between input hist…

Anomalous Correction to Dynamical Response of Relativistic Plasma in a Magnetic Field
Sota Hanai
https://arxiv.org/abs/2509.16652 https://arxiv.org/pdf/250…

Anomalous Correction to Dynamical Response of Relativistic Plasma in a Magnetic Field
We study collective excitations in a relativistic collisionless plasma composed of massless fermions subject to an external magnetic field. We include dynamical electromagnetism and fluctuations of the chiral charge, while maintaining a vanishing net chiral charge. Within the framework of chiral kinetic theory, we find that the chiral anomaly gives a correction to the dispersion relation of the transverse mode. In a strong magnetic field, we also show that the collective mode has a gap using th…

R-Net: A Reliable and Resource-Efficient CNN for Colorectal Cancer Detection with XAI Integration
Rokonozzaman Ayon, Md Taimur Ahad, Bo Song, Yan Li
https://arxiv.org/abs/2509.16251

R-Net: A Reliable and Resource-Efficient CNN for Colorectal Cancer Detection with XAI Integration
State-of-the-art (SOTA) Convolutional Neural Networks (CNNs) are criticized for their extensive computational power, long training times, and large datasets. To overcome this limitation, we propose a reasonable network (R-Net), a lightweight CNN only to detect and classify colorectal cancer (CRC) using the Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset (EBHI). Furthermore, six SOTA CNNs, including Multipath-based CNNs (DenseNet121, ResNet50), Depth-based CNNs (Inceptio…

MARS: A Malignity-Aware Backdoor Defense in Federated Learning
Wei Wan, Yuxuan Ning, Zhicong Huang, Cheng Hong, Shengshan Hu, Ziqi Zhou, Yechao Zhang, Tianqing Zhu, Wanlei Zhou, Leo Yu Zhang
https://arxiv.org/abs/2509.20383

MARS: A Malignity-Aware Backdoor Defense in Federated Learning
Federated Learning (FL) is a distributed paradigm aimed at protecting participant data privacy by exchanging model parameters to achieve high-quality model training. However, this distributed nature also makes FL highly vulnerable to backdoor attacks. Notably, the recently proposed state-of-the-art (SOTA) attack, 3DFed (SP2023), uses an indicator mechanism to determine whether the backdoor models have been accepted by the defender and adaptively optimizes backdoor models, rendering existing def…

Is attention truly all we need? An empirical study of asset pricing in pretrained RNN sparse and global attention models
Shanyan Lai
https://arxiv.org/abs/2508.19006 https://

Is attention truly all we need? An empirical study of asset pricing in pretrained RNN sparse and global attention models
This study investigates the pretrained RNN attention models with the mainstream attention mechanisms such as additive attention, Luong's three attentions, global self-attention (Self-att) and sliding window sparse attention (Sparse-att) for the empirical asset pricing research on top 420 large-cap US stocks. This is the first paper on the large-scale state-of-the-art (SOTA) attention mechanisms applied in the asset pricing context. They overcome the limitations of the traditional machine learni…

Leveraging OS-Level Primitives for Robotic Action Management
Wenxin Zheng, Boyang Li, Bin Xu, Erhu Feng, Jinyu Gu, Haibo Chen
https://arxiv.org/abs/2508.10259 https://

Leveraging OS-Level Primitives for Robotic Action Management
End-to-end imitation learning frameworks (e.g., VLA) are increasingly prominent in robotics, as they enable rapid task transfer by learning directly from perception to control, eliminating the need for complex hand-crafted features. However, even when employing SOTA VLA-based models, they still exhibit limited generalization capabilities and suboptimal action efficiency, due to the constraints imposed by insufficient robotic training datasets. In addition to addressing this problem using model-…

Direct Preference Optimization for Speech Autoregressive Diffusion Models
Zhijun Liu, Dongya Jia, Xiaoqiang Wang, Chenpeng Du, Shuai Wang, Zhuo Chen, Haizhou Li
https://arxiv.org/abs/2509.18928

Direct Preference Optimization for Speech Autoregressive Diffusion Models
Autoregressive diffusion models (ARDMs) have recently been applied to speech generation, achieving state-of-the-art (SOTA) performance in zero-shot text-to-speech. By autoregressively generating continuous speech tokens with next-token diffusion, these models offer a promising alternative to next-token prediction, avoiding the technical complexities associated with discrete speech tokenization. As a relatively new paradigm, research on reinforcement learning (RL)-based fine-tuning of speech ARD…

Bridging Quantum Computing and Nuclear Structure: Atomic Nuclei on a Trapped-Ion Quantum Computer
Sota Yoshida, Takeshi Sato, Takumi Ogata, Masaaki Kimura
https://arxiv.org/abs/2509.20642

Bridging Quantum Computing and Nuclear Structure: Atomic Nuclei on a Trapped-Ion Quantum Computer
We report accurate quantum simulations of medium-mass atomic nuclei -including oxygen, calcium, and nickel- on the RIKEN-Quantinuum Reimei trapped-ion quantum computer, achieving sub-percent accuracy. Using a symmetry-aware pair-unitary coupled-cluster doubles (pUCCD) ansatz implemented with a hard-core-boson mapping, and with particle-number-restoring post-selection, our ground-state energy estimates agree with noise-free statevector simulations and with exact diagonalization results on the or…

Low-energy photoexcitation inside the Mott gap in doped Hubbard and t-J ladders
Sumal Chandra, Kazuya Shinjo, Shigetoshi Sota, Seiji Yunoki, Takami Tohyama
https://arxiv.org/abs/2509.21813

Low-energy photoexcitation inside the Mott gap in doped Hubbard and t-J ladders
We investigate changes in the optical conductivity of doped Mott insulators by tuning ultrashort pump pulses to target either the Drude or low-energy absorption regions. Using a hole-doped two-leg Hubbard ladder and a four-leg t-J ladders, we calculate the optical conductivity after pump by employing the time-dependent density matrix renormalization group. We find that a monocycle electric field pulse tuned to the Drude absorption reduces the Drude weight, accompanied by a slight enhancement in…

ArgCMV: An Argument Summarization Benchmark for the LLM-era
Omkar Gurjar, Agam Goyal, Eshwar Chandrasekharan
https://arxiv.org/abs/2508.19580 https://arxiv…

ArgCMV: An Argument Summarization Benchmark for the LLM-era
Key point extraction is an important task in argument summarization which involves extracting high-level short summaries from arguments. Existing approaches for KP extraction have been mostly evaluated on the popular ArgKP21 dataset. In this paper, we highlight some of the major limitations of the ArgKP21 dataset and demonstrate the need for new benchmarks that are more representative of actual human conversations. Using SoTA large language models (LLMs), we curate a new argument key point extr…

A Statistical Mixture-of-Experts Framework for EMG Artifact Removal in EEG: Empirical Insights and a Proof-of-Concept Application
Benjamin J. Choi, Griffin Milsap, Clara A. Scholl, Francesco Tenore, Mattson Ogg
https://arxiv.org/abs/2509.19385

A Statistical Mixture-of-Experts Framework for EMG Artifact Removal in EEG: Empirical Insights and a Proof-of-Concept Application
Effective control of neural interfaces is limited by poor signal quality. While neural network-based electroencephalography (EEG) denoising methods for electromyogenic (EMG) artifacts have improved in recent years, current state-of-the-art (SOTA) models perform suboptimally in settings with high noise. To address the shortcomings of current machine learning (ML)-based denoising algorithms, we present a signal filtration algorithm driven by a new mixture-of-experts (MoE) framework. Our algorithm…

UI-Venus Technical Report: Building High-performance UI Agents with RFT
Zhangxuan Gu, Zhengwen Zeng, Zhenyu Xu, Xingran Zhou, Shuheng Shen, Yunfei Liu, Beitong Zhou, Changhua Meng, Tianyu Xia, Weizhi Chen, Yue Wen, Jingya Dou, Fei Tang, Jinzhen Lin, Yulin Liu, Zhenlin Guo, Yichen Gong, Heng Jia, Changlong Gao, Yuan Guo, Yong Deng, Zhenyu Guo, Liang Chen, Weiqiang Wang

UI-Venus Technical Report: Building High-performance UI Agents with RFT
We present UI-Venus, a native UI agent that takes only screenshots as input based on a multimodal large language model. UI-Venus achieves SOTA performance on both UI grounding and navigation tasks using only several hundred thousand high-quality training samples through reinforcement finetune (RFT) based on Qwen2.5-VL. Specifically, the 7B and 72B variants of UI-Venus obtain 94.1% / 50.8% and 95.3% / 61.9% on the standard grounding benchmarks, i.e., Screenspot-V2 / Pro, surpassing the previous …

Mamba-2 audio captioning: design space exploration and analysis
Taehan Lee, Jaehan Jung, Hyukjun Lee
https://arxiv.org/abs/2509.15680 https://arxiv.org/pdf…

Mamba-2 audio captioning: design space exploration and analysis
We present an audio captioning model built on the Mamba-2 large language model backbone, which is a state-of-the-art (SOTA) state-space model (SSM). We systematically explore the design space: LLM sizes, LoRA ranks, and connector designs leveraging Mamba-2's linear-time complexity with respect to sequence length. Across benchmarks, our models achieve strong captioning performance compared with larger language models trained on the same dataset, despite using fewer parameters. For the first time…

Operand Quant: A Single-Agent Architecture for Autonomous Machine Learning Engineering
Arjun Sahney, Ram Gorthi, Cezary {\L}astowski, Javier Vega
https://arxiv.org/abs/2510.11694

Operand Quant: A Single-Agent Architecture for Autonomous Machine Learning Engineering
We present Operand Quant, a single-agent, IDE-based architecture for autonomous machine learning engineering (MLE). Operand Quant departs from conventional multi-agent orchestration frameworks by consolidating all MLE lifecycle stages -- exploration, modeling, experimentation, and deployment -- within a single, context-aware agent. On the MLE-Benchmark (2025), Operand Quant achieved a new state-of-the-art (SOTA) result, with an overall medal rate of 0.3956 +/- 0.0565 across 75 problems -- the h…

Impact of a Sharpness Based Loss Function for Removing Out-of-Focus Blur
Uditangshu Aurangabadkar, Darren Ramsook, Anil Kokaram
https://arxiv.org/abs/2509.11735 https://

Impact of a Sharpness Based Loss Function for Removing Out-of-Focus Blur
Recent research has explored complex loss functions for deblurring. In this work, we explore the impact of a previously introduced loss function - Q which explicitly addresses sharpness and employ it to fine-tune State-of-the-Art (SOTA) deblurring models. Standard image quality metrics such as PSNR or SSIM do not distinguish sharpness from ringing. Therefore, we propose a novel full-reference image quality metric Omega that combines PSNR with Q. This metric is sensitive to ringing artefacts, bu…

ArtPerception: ASCII Art-based Jailbreak on LLMs with Recognition Pre-test
Guan-Yan Yang, Tzu-Yu Cheng, Ya-Wen Teng, Farn Wanga, Kuo-Hui Yeh
https://arxiv.org/abs/2510.10281 htt…

ArtPerception: ASCII Art-based Jailbreak on LLMs with Recognition Pre-test
The integration of Large Language Models (LLMs) into computer applications has introduced transformative capabilities but also significant security challenges. Existing safety alignments, which primarily focus on semantic interpretation, leave LLMs vulnerable to attacks that use non-standard data representations. This paper introduces ArtPerception, a novel black-box jailbreak framework that strategically leverages ASCII art to bypass the security measures of state-of-the-art (SOTA) LLMs. Unlik…

EyeNexus: Adaptive Gaze-Driven Quality and Bitrate Streaming for Seamless VR Cloud Gaming Experiences
Ze Wu, Ahmad Alhilal, Yuk Hang Tsui, Matti Siekkinen, Pan Hui
https://arxiv.org/abs/2509.11807

EyeNexus: Adaptive Gaze-Driven Quality and Bitrate Streaming for Seamless VR Cloud Gaming Experiences
Virtual Reality (VR) cloud gaming systems render the 3D graphics on cloud servers for playing graphically demanding games on VR headsets. Delivering high-resolution game scenes is challenging due to variation in network performance. By leveraging the non-uniform human vision perception, foveated rendering and encoding have proven effective for optimized streaming in constrained networks. SoTA foveation methods either do not incorporate real-time gaze data or are unable to handle variations in n…

A study on Deep Convolutional Neural Networks, transfer learning, and Mnet model for Cervical Cancer Detection
Saifuddin Sagor, Md Taimur Ahad, Faruk Ahmed, Rokonozzaman Ayon, Sanzida Parvin
https://arxiv.org/abs/2509.16250

A study on Deep Convolutional Neural Networks, transfer learning, and Mnet model for Cervical Cancer Detection
Early and accurate detection through Pap smear analysis is critical to improving patient outcomes and reducing mortality of Cervical cancer. State-of-the-art (SOTA) Convolutional Neural Networks (CNNs) require substantial computational resources, extended training time, and large datasets. In this study, a lightweight CNN model, S-Net (Simple Net), is developed specifically for cervical cancer detection and classification using Pap smear images to address these limitations. Alongside S-Net, six…

Crosslisted article(s) found for nucl-th. https://arxiv.org/list/nucl-th/new
[1/1]:
- Anomalous Correction to Dynamical Response of Relativistic Plasma in a Magnetic Field
Sota Hanai