Tootfinder

Isaiah Pola-Mao picks off Trevor Lawrence in the end zone! https://www.raiders.com/video/isaiah-pola-mao-picks-off-trevor-lawrence-end-zone-nfl

Älskar AF. Den äldsta kunde få en lärlingsplats givet att ägarna fick stöd de första 6 månaderna när de lär upp henne innan de anställer - ett fast jobb i horizonten mao. Vill AF hjälpa till? Nix, de vill att hon ska gå söka-jobb-kurs istället så att jobbet är borta när hon är färdig. Hur kan systemet vara så jävla trasigt?

Raiders report: Team’s ‘X-men’ embrace different roles on defense https://www.reviewjournal.com/sports/raiders/raiders-report-teams-x-men-embrace-different-roles-on-defense-3532852/

Älskar AF. Den äldsta kunde få en lärlingsplats givet att ägarna fick stöd de första 6 månaderna när de lär upp henne innan de anställer - ett fast jobb i horizonten mao. Vill AF hjälpa till? Nix, de vill att hon ska gå söka-jobb-kurs istället så att jobbet är borta när hon är färdig. Hur kan systemet vara så jävla trasigt?

Trump is starving the population. Nothing new here. Stalin did it, Hitler did it, Netanyahu did it, Mao did it, and there are more examples in history; it's quite a common tactic amongst certain kinds of politicians against their opponents.
https://open.substack.com/pub…

October 25, 2025
Yesterday the Trump administration said it would not use any of the approximately $6 billion the U.S.

Sub-Ensemble Correlations as a Covariance Geometry
Zuoxian Wang, Yuhao Zhang, Gaopu Hou, Zihua Liang, Gen Hu, Lu Liu, Yuan Sun, Feilong Xu, Mao Ye
https://arxiv.org/abs/2512.24451

Replaced article(s) found for physics.optics. https://arxiv.org/list/physics.optics/new
[1/1]:
- LLM4Laser: Large Language Models Automate the Design of Lasers
Renjie Li, Ceyao Zhang, Sixuan Mao, Xiyuan Zhou, Feng Yin, Sergios Theodoridis, Zhaoyu Zhang
https://arxiv.org/abs/2104.12145
- Room-temperature valley-selective emission in Si-MoSe2 heterostructures enabled by high-quality-f...
Feng Pan, et al.
https://arxiv.org/abs/2409.09806 https://mastoxiv.page/@arXiv_physicsoptics_bot/113152185040115763
- 1T'-MoTe$_2$ as an integrated saturable absorber for photonic machine learning
Maria Carolina Volpato, Henrique G. Rosa, Tom Reep, Pierre-Louis de Assis, Newton Cesario Frateschi
https://arxiv.org/abs/2507.16140 https://mastoxiv.page/@arXiv_physicsoptics_bot/114901571498004090
- NeOTF: Guidestar-free neural representation for broadband dynamic imaging through scattering
Yunong Sun, Fei Xia
https://arxiv.org/abs/2507.22328 https://mastoxiv.page/@arXiv_physicsoptics_bot/114947052118796753
- Structured Random Models for Phase Retrieval with Optical Diffusers
Zhiyuan Hu, Fakhriyya Mammadova, Juli\'an Tachella, Michael Unser, Jonathan Dong
https://arxiv.org/abs/2510.14490 https://mastoxiv.page/@arXiv_physicsoptics_bot/115388901264416806
- Memory Effects in Time-Modulated Radiative Heat Transfer
Riccardo Messina, Philippe Ben-Abdallah
https://arxiv.org/abs/2510.19378 https://mastoxiv.page/@arXiv_physicsoptics_bot/115422659227231796
- Mie-tronics supermodes and symmetry breaking in nonlocal metasurfaces
Thanh Xuan Hoang, Ayan Nussupbekov, Jie Ji, Daniel Leykam, Jaime Gomez Rivas, Yuri Kivshar
https://arxiv.org/abs/2511.03560 https://mastoxiv.page/@arXiv_physicsoptics_bot/115502066008543828
- Integrated soliton microcombs beyond the turnkey limit
Wang, Xu, Wang, Zhu, Luo, Luo, Wang, Ni, Yang, Gong, Xiao, Li, Yang
https://arxiv.org/abs/2511.06909 https://mastoxiv.page/@arXiv_physicsoptics_bot/115530791701071777
- Ising accelerator with a reconfigurable interferometric photonic processor
Rausell-Campo, Al Kayed, P\'erez-L\'ppez, Aadhi, Shastri, Francoy
https://arxiv.org/abs/2511.13284 https://mastoxiv.page/@arXiv_physicsoptics_bot/115570439939074488
- Superradiance in dense atomic samples
I. M. de Ara\'ujo, H. Sanchez, L. F. Alves da Silva, M. H. Y. Moussa
https://arxiv.org/abs/2504.20242 https://mastoxiv.page/@arXiv_quantph_bot/114425762810828336
- Fluctuation-induced Hall-like lateral forces in a chiral-gain environment
Daigo Oue, M\'ario G. Silveirinha
https://arxiv.org/abs/2507.14754 https://mastoxiv.page/@arXiv_condmatmeshall_bot/114896308178114535
- Tensor-network approach to quantum optical state evolution beyond the Fock basis
Nikolay Kapridov, Egor Tiunov, Dmitry Chermoshentsev
https://arxiv.org/abs/2511.15295 https://mastoxiv.page/@arXiv_quantph_bot/115581390666689204
- OmniLens : Blind Lens Aberration Correction via Large LensLib Pre-Training and Latent PSF Repres...
Jiang, Qian, Gao, Sun, Yang, Yi, Li, Yang, Van Gool, Wang
https://arxiv.org/abs/2511.17126 https://mastoxiv.page/@arXiv_eessIV_bot/115603729319581340
toXiv_bot_toot

QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
Wei Huang, Yi Ge, Shuai Yang, Yicheng Xiao, Huizi Mao, Yujun Lin, Hanrong Ye, Sifei Liu, Ka Chun Cheung, Hongxu Yin, Yao Lu, Xiaojuan Qi, Song Han, Yukang Chen
https://arxiv.org/abs/2510.11696

QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
We propose QeRL, a Quantization-enhanced Reinforcement Learning framework for large language models (LLMs). While RL is essential for LLMs' reasoning capabilities, it is resource-intensive, requiring substantial GPU memory and long rollout durations. QeRL addresses these issues by combining NVFP4 quantization with Low-Rank Adaptation (LoRA), accelerating rollout phase of RL while reducing memory overhead. Beyond efficiency, our findings show that quantization noise increases policy entropy, enh…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[12/14]:
- Clip Your Sequences Fairly: Enforcing Length Fairness for Sequence-Level RL
Hanyi Mao, Quanjia Xiao, Lei Pang, Haixiao Liu

Detection of quantum information masking via machine learning
Sheng-Ao Mao, Lin Zhang, Bo Li
https://arxiv.org/abs/2510.12507 https://arxiv.org/pdf/2510.12…

Detection of quantum information masking via machine learning
Recently, machine learning has been widely applied in the field of quantum information, notably in tasks such as entanglement detection, steering characterization, and nonlocality verification. However, few studies have focused on utilizing machine learning to detect quantum information masking. In this work, we investigate supervised machine learning for detecting quantum information masking in both pure and mixed qubit states. For pure qubit states, we randomly generate the corresponding dens…

Mull-Tokens: Modality-Agnostic Latent Thinking
Arijit Ray, Ahmed Abdelkader, Chengzhi Mao, Bryan A. Plummer, Kate Saenko, Ranjay Krishna, Leonidas Guibas, Wen-Sheng Chu
https://arxiv.org/abs/2512.10941

Towards Engineering Multi-Agent LLMs: A Protocol-Driven Approach
Zhenyu Mao, Jacky Keung, Fengji Zhang, Shuo Liu, Yifei Wang, Jialong Li
https://arxiv.org/abs/2510.12120 https:/…

Towards Engineering Multi-Agent LLMs: A Protocol-Driven Approach
The increasing demand for software development has driven interest in automating software engineering (SE) tasks using Large Language Models (LLMs). Recent efforts extend LLMs into multi-agent systems (MAS) that emulate collaborative development workflows, but these systems often fail due to three core deficiencies: under-specification, coordination misalignment, and inappropriate verification, arising from the absence of foundational SE structuring principles. This paper introduces Software En…

MaP: A Unified Framework for Reliable Evaluation of Pre-training Dynamics
Jiapeng Wang, Changxin Tian, Kunlong Chen, Ziqi Liu, Jiaxin Mao, Wayne Xin Zhao, Zhiqiang Zhang, Jun Zhou
https://arxiv.org/abs/2510.09295

MaP: A Unified Framework for Reliable Evaluation of Pre-training Dynamics
Reliable evaluation is fundamental to the progress of Large Language Models (LLMs), yet the evaluation process during pre-training is plagued by significant instability that obscures true learning dynamics. In this work, we systematically diagnose this instability, attributing it to two distinct sources: \textit{Parameter Instability} from training stochasticity and \textit{Evaluation Instability} from noisy measurement protocols. To counteract both sources of noise, we introduce \textbf{MaP}, …

CDE: Concept-Driven Exploration for Reinforcement Learning
Le Mao, Andrew H. Liu, Renos Zabounidis, Zachary Kingston, Joseph Campbell
https://arxiv.org/abs/2510.08851 https://…

CDE: Concept-Driven Exploration for Reinforcement Learning
Intelligent exploration remains a critical challenge in reinforcement learning (RL), especially in visual control tasks. Unlike low-dimensional state-based RL, visual RL must extract task-relevant structure from raw pixels, making exploration inefficient. We propose Concept-Driven Exploration (CDE), which leverages a pre-trained vision-language model (VLM) to generate object-centric visual concepts from textual task descriptions as weak, potentially noisy supervisory signals. Rather than direct…

"Can I Decorate My Teeth With Diamonds?": Exploring Multi-Stakeholder Perspectives on Using VR to Reduce Children's Dental Anxiety
Yaxuan Mao, Yanheng Li, Duo Gong, Pengcheng An, Yuhan Luo
https://arxiv.org/abs/2510.10019

"Can I Decorate My Teeth With Diamonds?": Exploring Multi-Stakeholder Perspectives on Using VR to Reduce Children's Dental Anxiety
Dental anxiety is prevalent among children, often leading to missed treatment and potential negative effects on their mental well-being. While several interventions (e.g., pharmacological and psychotherapeutic techniques) have been introduced for anxiety alleviation, the recently emerged virtual reality (VR) technology, with its immersive and playful nature, opened new opportunities for complementing and enhancing the therapeutic effects of existing interventions. In this light, we conducted a …

Rethinking Provenance Completeness with a Learning-Based Linux Scheduler
Jinsong Mao, Benjamin E. Ujcich, Shiqing Ma
https://arxiv.org/abs/2510.08479 https://

Rethinking Provenance Completeness with a Learning-Based Linux Scheduler
Provenance plays a critical role in maintaining traceability of a system's actions for root cause analysis of security threats and impacts. Provenance collection is often incorporated into the reference monitor of systems to ensure that an audit trail exists of all events, that events are completely captured, and that logging of such events cannot be bypassed. However, recent research has questioned whether existing state-of-the-art provenance collection systems fail to ensure the security guar…

An inverse theorem on sets with rich additive structure modulo primes
Ernie Croot, Junzhe Mao, Chi Hoi Yip
https://arxiv.org/abs/2510.08862 https://arxiv.o…

An inverse theorem on sets with rich additive structure modulo primes
In this paper, we prove several results on the structure of maximal sets $S \subseteq [N]$ such that $S$ mod $p$ is contained in a short arithmetic progression, or the union of short progressions, where $p$ ranges over a subset of primes in an interval $[y,2y]$, where $(\log N)^C < y \leq N$. We also provide several constructions showing that our results cannot be improved. As an application, we provide several improvements on the larger sieve bound for $|S|$ when $S$ mod $p$ has stro…

Crosslisted article(s) found for math.CO. https://arxiv.org/list/math.CO/new
[1/1]:
- An inverse theorem on sets with rich additive structure modulo primes
Ernie Croot, Junzhe Mao, Chi Hoi Yip

CardRewriter: Leveraging Knowledge Cards for Long-Tail Query Rewriting on Short-Video Platforms
Peiyuan Gong, Feiran Zhu, Yaqi Yin, Chenglei Dai, Chao Zhang, Kai Zheng, Wentian Bao, Jiaxin Mao, Yi Zhang
https://arxiv.org/abs/2510.10095

CardRewriter: Leveraging Knowledge Cards for Long-Tail Query Rewriting on Short-Video Platforms
Short-video platforms have rapidly become a new generation of information retrieval systems, where users formulate queries to access desired videos. However, user queries, especially long-tail ones, often suffer from spelling errors, incomplete phrasing, and ambiguous intent, resulting in mismatches between user expectations and retrieved results. While large language models (LLMs) have shown success in long-tail query rewriting within e-commerce, they struggle on short-video platforms, where p…

Possible Quasi-Period Oscillation Signals in the Unique Event of GRB 250702DBE/EP250702a?
Fei-Fan Song, Jirong Mao
https://arxiv.org/abs/2510.10139 https://

Possible Quasi-Period Oscillation Signals in the Unique Event of GRB 250702DBE/EP250702a?
GRB 250702DBE was time-consequently triggered by GBM onboard the Fermi satellite. It is uncertain which celestial catalog is suitable for this special ultra-long event to belong to. In this paper, we comprehensively investigate the lightcurves obtained by Fermi-GBM detectors. In the energy band of 8-1000 keV, no Quasi-Period Oscillation (QPO) signals are found in the lightcurve of the first burst 250702D, a possible QPO signal of 0.046 Hz corresponding to a period of 21.7 s is found in the ligh…

Semisimple module categories with fusion rules of the compact full flag manifold type
Mao Hoshino
https://arxiv.org/abs/2510.12057 https://arxiv.org/pdf/25…

Semisimple module categories with fusion rules of the compact full flag manifold type
We classify semisimple left module categories over the representation category of a type A quantum group whose fusion rules arise from the maximal torus. The classification is connected to equivariant Poisson structures on compact full flag manifolds in the operator-algebraic setting, and on semisimple coadjoint orbits in the algebraic setting. We also provide an explicit construction based on the BGG categories of deformed quantum enveloping algebras, whose unitarizability corresponds to being…

scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction
Zhaokang Liang, Shuyang Zhuang, Xiaoran Jiao, Weian Mao, Hao Chen, Chunhua Shen
https://arxiv.org/abs/2510.11726

scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction
This paper introduces the Single-Cell Perturbation Prediction Diffusion Model (scPPDM), the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data. scPPDM couples two condition channels, pre-perturbation state and drug with dose, in a unified latent space via non-concatenative GD-Attn. During inference, factorized classifier-free guidance exposes two interpretable controls for state preservation and drug-response strength and maps dose to guidance magnitude…

Nonlinear Hodge correspondence in positive characteristic
Mao Sheng
https://arxiv.org/abs/2510.05578 https://arxiv.org/pdf/2510.05578

Nonlinear Hodge correspondence in positive characteristic
In this article, we extend the nonabelian Hodge correspondence in positive characteristic to the nonlinear setting.

Hayden--Preskill Model via Local Quenches
Weibo Mao, Tadashi Takayanagi
https://arxiv.org/abs/2510.05853 https://arxiv.org/pdf/2510.05853

Hayden--Preskill Model via Local Quenches
We model the Hayden--Preskill (HP) information recovery protocol in 2d CFTs via local joining quenches. Euclidean path integrals with slits prepare the HP subsystems: the message $M$, its reference $N$, the Page-time black hole $B$, the early radiation $E$, and the late radiation $R$; the remaining black hole after emitting $R$ is denoted as $B'$. The single-slit geometry provides an analytically tractable toy model, while the bounded-slit geometry more closely captures the HP setup. In the fre…

The low-lying light tetraquark states with quantum numbers $J^{P}=0^{ }$, $1^{ }$ and $2^{ }$
Hao Wu, Mao-Jun Yan, Chun-Sheng An, Cheng-Rong Deng
https://arxiv.org/abs/2510.07836

The low-lying light tetraquark states with quantum numbers $J^{P}=0^{+ }$, $1^{+}$ and $2^{+}$
The low-lying light tetraquark states are investigated in the non-relativistic quark model (NRQM) including the pseudoscalar meson exchange, where two different confinement potential schemes, the Cornell potential and the linear potential, are employed, along with the instanton-induced interaction serving as the residual spin-dependent interaction. The numerical results show agreement with masses of $f_{0}(500)$, $f_{0}(1370)$, $f_{0}(1500)$, $f_{0}(2020)$, $f_{0}(2200)$, $h_{1}(1170)$, $h_{1}(…

Benchmarking Fake Voice Detection in the Fake Voice Generation Arms Race
Xutao Mao, Ke Li, Cameron Baird, Ezra Xuanru Tao, Dan Lin
https://arxiv.org/abs/2510.06544 https://

Benchmarking Fake Voice Detection in the Fake Voice Generation Arms Race
As advances in synthetic voice generation accelerate, an increasing variety of fake voice generators have emerged, producing audio that is often indistinguishable from real human speech. This evolution poses new and serious threats across sectors where audio recordings serve as critical evidence. Although fake voice detectors are also advancing, the arms race between fake voice generation and detection has become more intense and complex. In this work, we present the first large-scale, cross-do…

Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learning
Simin Li, Zihao Mao, Hanxiao Li, Zonglei Jing, Zhuohang bian, Jun Guo, Li Wang, Zhuoran Han, Ruixiao Xu, Xin Yu, Chengdong Ma, Yuqing Ma, Bo An, Yaodong Yang, Weifeng Lv, Xianglong Liu
https://arxiv.org/abs/2510.11824

Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learning
In cooperative Multi-Agent Reinforcement Learning (MARL), it is a common practice to tune hyperparameters in ideal simulated environments to maximize cooperative performance. However, policies tuned for cooperation often fail to maintain robustness and resilience under real-world uncertainties. Building trustworthy MARL systems requires a deep understanding of robustness, which ensures stability under uncertainties, and resilience, the ability to recover from disruptions--a concept extensively …

Crosslisted article(s) found for math.RT. https://arxiv.org/list/math.RT/new
[1/1]:
- Semisimple module categories with fusion rules of the compact full flag manifold type
Mao Hoshino
…

Distributed Detection and Bandwidth Allocation with Hybrid Quantized and Full-Precision Observations over Multiplicative Fading Channels
Linlin Mao, Zeping Sui, Michail Matthaiou, Hongbin Li
https://arxiv.org/abs/2510.06429

Distributed Detection and Bandwidth Allocation with Hybrid Quantized and Full-Precision Observations over Multiplicative Fading Channels
A hybrid detector that fuses both quantized and full-precision observations is proposed for weak signal detection under additive and multiplicative Gaussian noise. We first derive a locally most powerful test (LMPT)--based hybrid detector from the composite probability distribution of the compound observations received by the fusion center, and then analyze its asymptotic detection performance. Subsequently, we optimize the sensor-wise quantization thresholds to achieve near-optimal asymptotic …

A Vertically Orientated Dark Matter Halo Marks a Flip of the Galactic Disk
Ling Zhu, Runsheng Cai, Xi Kang, Xiang-Xiang Xue, Chengqun Yang, Lan Zhang, Shude Mao, Chao Liu
https://arxiv.org/abs/2510.08684

A Vertically Orientated Dark Matter Halo Marks a Flip of the Galactic Disk
Unveiling the 3D shape of the Milky Way's dark-matter halo is critical to understanding its formation history. We created an innovative dynamical model with minimal assumptions on the internal dynamical structures and accommodates a highly flexible triaxial DM halo. By applying the method to 6D phase-space data of K-giant stars from LAMOST + Gaia, we robustly determine the 3D dark-matter distribution of the Milky Way out to approximately 50 kpc. We discover a triaxial, nearly oblate dark-matter…

Crosslisted article(s) found for math.OA. https://arxiv.org/list/math.OA/new
[1/1]:
- Semisimple module categories with fusion rules of the compact full flag manifold type
Mao Hoshino
…

Crosslisted article(s) found for cs.OS. https://arxiv.org/list/cs.OS/new
[1/1]:
- Rethinking Provenance Completeness with a Learning-Based Linux Scheduler
Jinsong Mao, Benjamin E. Ujcich, Shiqing Ma

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[12/14]:
- Can Prompt Difficulty be Online Predicted for Accelerating RL Finetuning of Reasoning Models?
Yun Qu, Qi Wang, Yixiu Mao, Vincent Tao Hu, Bj\"orn Ommer, Xiangyang Ji

Sharp Lower Bounds for Linearized ReLU^k Approximation on the Sphere
Tong Mao, Jinchao Xu
https://arxiv.org/abs/2510.04060 https://arxiv.org/pdf/2510.04060…

Sharp Lower Bounds for Linearized ReLU^k Approximation on the Sphere
We prove a saturation theorem for linearized shallow ReLU$^k$ neural networks on the unit sphere $\mathbb S^d$. For any antipodally quasi-uniform set of centers, if the target function has smoothness $r>\tfrac{d+2k+1}{2}$, then the best $\mathcal{L}^2(\mathbb S^d)$ approximation cannot converge faster than order $n^{-\frac{d+2k+1}{2d}}$. This lower bound matches existing upper bounds, thereby establishing the exact saturation order $\tfrac{d+2k+1}{2d}$ for such networks. Our results place linea…

TC-LoRA: Temporally Modulated Conditional LoRA for Adaptive Diffusion Control
Minkyoung Cho, Ruben Ohana, Christian Jacobsen, Adityan Jothi, Min-Hung Chen, Z. Morley Mao, Ethem Can
https://arxiv.org/abs/2510.09561

TC-LoRA: Temporally Modulated Conditional LoRA for Adaptive Diffusion Control
Current controllable diffusion models typically rely on fixed architectures that modify intermediate activations to inject guidance conditioned on a new modality. This approach uses a static conditioning strategy for a dynamic, multi-stage denoising process, limiting the model's ability to adapt its response as the generation evolves from coarse structure to fine detail. We introduce TC-LoRA (Temporally Modulated Conditional LoRA), a new paradigm that enables dynamic, context-aware control by c…

Replaced article(s) found for cs.DL. https://arxiv.org/list/cs.DL/new
[1/1]:
- Language Models Surface the Unwritten Code of Science and Society
Honglin Bao, Siyang Wu, Jiwoong Choi, Yingrong Mao, James A. Evans

Interlayer coupling enhanced superconductivity near 100 K in La$_{3-x}$Nd$_x$Ni$_2$O$_7$
Zhengyang Qiu, Junfeng Chen, Dmitrii V. Semenok, Qingyi Zhong, Di Zhou, Jingyuan Li, Peiyue Ma, Xing Huang, Mengwu Huo, Tao Xie, Xiang Chen, Ho-kwang Mao, Viktor Struzhkin, Hualei Sun, Meng Wang
https://arxiv.org/abs/2510.12359

Interlayer coupling enhanced superconductivity near 100 K in La$_{3-x}$Nd$_x$Ni$_2$O$_7$
Systematically controlling the superconducting transition temperature ($T_\text{c}$) in the bilayer Ruddlesden-Popper nickelate La$_3$Ni$_2$O$_7$ remains a significant challenge. Here, we address this by synthesizing high-quality polycrystalline La$_{3-x}$Nd$_x$Ni$_2$O$_7$ ($0 \leq x \leq 2.4$) with record-level rare-earth substitution. Nd doping compresses the lattice, particularly along the $c$ axis, enhances the spin density wave transition temperature, and elevates the pressure required for…

Gravitational lensing by a spiral galaxy I: the influence from bar's structure to the flux ratio anomaly
Xikai Shan, Yunpeng Jin, Shude Mao
https://arxiv.org/abs/2510.02805 …

Gravitational lensing by a spiral galaxy I: the influence from bar's structure to the flux ratio anomaly
Gravitational lens flux ratio anomalies are a powerful probe of small-scale mass structures within lens galaxies. These anomalies are often attributed to dark matter subhalos, but the baryonic components of the lens can also play a significant role. This study investigates the impact of galactic bars, a common feature in spiral galaxies, on flux ratio anomalies. We conduct a systematic analysis using a sample of 21 barred galaxies from the high-resolution Auriga cosmological simulations. First,…

Crosslisted article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[3/3]:
- QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
Huang, Ge, Yang, Xiao, Mao, Lin, Ye, Liu, Cheung, Yin, Lu, Qi, Han, Chen

Sliding multiferroicity in hexagonal stacked CrI3
Carter Fox, Jose D. Mella, Jack Rollins, Yangchen He, Yulu Mao, Haotian Jiang, Alaina Drew, Hongrui Ma, Takashi Taniguchi, Kenji Watanabe, Ying Wang, Daniel Rhodes, Salvador Barraza-Lopez, Jun Xiao
https://arxiv.org/abs/2510.03220

Sliding multiferroicity in hexagonal stacked CrI3
Developing new multiferroics at the two-dimensional (2D) limit with energy-efficient magnetoelectric coupling can inform the interplay physics of novel orders and advance on-chip high-performance computing applications. Here we apply stacking order engineering to create a new type of 2D multiferroics, namely sliding multiferroics, based on polar hexagonal stacked (H-stacked) CrI3. This new stacking order removes structural inversion symmetry and gives rise to room temperature sliding ferroelect…

Spectral Prefiltering of Neural Fields
Mustafa B. Yaldiz, Ishit Mehta, Nithin Raghavan, Andreas Meuleman, Tzu-Mao Li, Ravi Ramamoorthi
https://arxiv.org/abs/2510.08394 https://

Spectral Prefiltering of Neural Fields
Neural fields excel at representing continuous visual signals but typically operate at a single, fixed resolution. We present a simple yet powerful method to optimize neural fields that can be prefiltered in a single forward pass. Key innovations and features include: (1) We perform convolutional filtering in the input domain by analytically scaling Fourier feature embeddings with the filter's frequency response. (2) This closed-form modulation generalizes beyond Gaussian filtering and supports…

Project-Level C-to-Rust Translation via Synergistic Integration of Knowledge Graphs and Large Language Models
Zhiqiang Yuan, Wenjun Mao, Zhuo Chen, Xiyue Shang, Chong Wang, Yiling Lou, Xin Peng
https://arxiv.org/abs/2510.10956

Project-Level C-to-Rust Translation via Synergistic Integration of Knowledge Graphs and Large Language Models
Translating C code into safe Rust is an effective way to ensure its memory safety. Compared to rule-based translation which produces Rust code that remains largely unsafe, LLM-based methods can generate more idiomatic and safer Rust code because LLMs have been trained on vast amount of human-written idiomatic code. Although promising, existing LLM-based methods still struggle with project-level C-to-Rust translation. They typically partition a C project into smaller units (\eg{} functions) base…

Explicit formulae and topological descriptions of action-minimizing sets for 2-locally potentials of the XY model
Yuika Kajihara, Shoya Motonaga, Mao Shinoda
https://arxiv.org/abs/2510.02678

Explicit formulae and topological descriptions of action-minimizing sets for 2-locally potentials of the XY model
We consider ergodic optimization of a symbolic dynamics with uncountable alphabet $[0,1]$ for 2-locally constant potentials with the twist condition, and give explicit formulae of the associated Mather set and the Aubry set. Moreover, we investigate the total disconnectedness of the (quotient) Aubry set, in which case the differentiability of the potential function makes a remarkable difference. Although these results imply that the (quotient) Aubry set is small enough, we give a complete chara…

Phase-controlled quantum transport signatures in a quantum dot-Majorana hybrid ring system
Sirui Yu, Junrong Wang, Huajin Zhao, Hong Mao, Jinshuang Jin
https://arxiv.org/abs/2510.07826

Phase-controlled quantum transport signatures in a quantum dot-Majorana hybrid ring system
We investigate the quantum transport in a hybrid ring system consisting of a quantum dot (QD) coupled to two Majorana bound states (MBSs) hosted in a topological superconducting nanowire, threaded by a magnetic flux. Utilizing the dissipaton equation-of-motion approach, we demonstrate that the differential conductance shows periodic behavior and its periodicity depends on both the QD energy level and the MBS overlapping. A zero-bias peak (ZBP) emerges as a result of the balance between normal a…

Co-TAP: Three-Layer Agent Interaction Protocol Technical Report
Shunyu An, Miao Wang, Yongchao Li, Dong Wan, Lina Wang, Ling Qin, Liqin Gao, Congyao Fan, Zhiyong Mao, Jiange Pu, Wenji Xia, Dong Zhao, Rui Hu, Ji Lu, Guiyue Zhou, Baoyu Tang, Yanqin Gao, Yongsheng Du, Daigang Xu, Lingjun Huang, Baoli Wang, Xiwen Zhang, Luyao Wang, Shilong Liu
https://

Co-TAP: Three-Layer Agent Interaction Protocol Technical Report
This paper proposes Co-TAP (T: Triple, A: Agent, P: Protocol), a three-layer agent interaction protocol designed to address the challenges faced by multi-agent systems across the three core dimensions of Interoperability, Interaction and Collaboration, and Knowledge Sharing. We have designed and proposed a layered solution composed of three core protocols: the Human-Agent Interaction Protocol (HAI), the Unified Agent Protocol (UAP), and the Memory-Extraction-Knowledge Protocol (MEK). HAI focuse…

Decentralized Multi-Robot Relative Navigation in Unknown, Structurally Constrained Environments under Limited Communication
Zihao Mao, Yunheng Wang, Yunting Ji, Yi Yang, Wenjie Song
https://arxiv.org/abs/2510.09188

Decentralized Multi-Robot Relative Navigation in Unknown, Structurally Constrained Environments under Limited Communication
Multi-robot navigation in unknown, structurally constrained, and GPS-denied environments presents a fundamental trade-off between global strategic foresight and local tactical agility, particularly under limited communication. Centralized methods achieve global optimality but suffer from high communication overhead, while distributed methods are efficient but lack the broader awareness to avoid deadlocks and topological traps. To address this, we propose a fully decentralized, hierarchical rela…

Crosslisted article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- MECKD: Deep Learning-Based Fall Detection in Multilayer Mobile Edge Computing With Knowledge Dist...
Wei-Lung Mao, Chun-Chi Wang, Po-Heng Chou, Kai-Chun Liu, Yu Tsao

Crosslisted article(s) found for q-bio.BM. https://arxiv.org/list/q-bio.BM/new
[1/1]:
- Evolutionary Profiles for Protein Fitness Prediction
Fan, Jiao, Lin, Liang, Mao, Jing, Chen, Shen

Random unitaries that conserve energy
Liang Mao, Laura Cui, Thomas Schuster, Hsin-Yuan Huang
https://arxiv.org/abs/2510.08448 https://arxiv.org/pdf/2510.08…

Random unitaries that conserve energy
Random unitaries sampled from the Haar measure serve as fundamental models for generic quantum many-body dynamics. Under standard cryptographic assumptions, recent works have constructed polynomial-size quantum circuits that are computationally indistinguishable from Haar-random unitaries, establishing the concept of pseudorandom unitaries (PRUs). While PRUs have found broad implications in many-body physics, they fail to capture the energy conservation that governs physical systems. In this wo…

Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Calibration-free Rydberg Atomic Receiver for Sub-MHz Wireless Communications and Sensing
Chen, Mao, Xiao, Wu, Li, Cui, Zeng, Zheng, Huang, Wang

En tidning jag prenumrerar på har sparkat ut mig för att jag har ad guard. De vill mao så gärna ha mer betalt av mig via annonser så att de hellre mister mig som abonnent (varför betala för ngt som jag inte kan använda?). Tack för det Gefle dagblad

Multi-Channel Amplitude-Phase Asymmetric-Encrypted Janus Acoustic Meta-Holograms
Haohan Zeng, Zhenyu He, Tianxiang Zhang, Xiao Guo, Xinghao Hu, Youyu Mo, Tingting Li, Feilong Mao, Haiyan Fan, Xudong Fan, Weiwei Kan, Yifan Zhu, Hui Zhang, Guodong Yin, Badreddine Assouar
https://arxiv.org/abs/2510.05789

Multi-Channel Amplitude-Phase Asymmetric-Encrypted Janus Acoustic Meta-Holograms
Encrypted optical and acoustic meta-holograms only focus on the encrypted hologram in a single channel, viz. modulating spatial amplitude to project a holographic image. In this research, the unique concept of multi-channel amplitude-phase asymmetric-encrypted Janus acoustic meta-holograms is proposed, demonstrating remarkable capabilities of generating, encrypting, and decrypting both amplitude and phase holographic images on both sides of a metascreen. The flexible and decoupled manipulation …

PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations
Ruining He, Lukasz Heldt, Lichan Hong, Raghunandan Keshavan, Shifan Mao, Nikhil Mehta, Zhengyang Su, Alicia Tsai, Yueqi Wang, Shao-Chuan Wang, Xinyang Yi, Lexi Baugher, Baykal Cakici, Ed Chi, Cristos Goodrow, Ningren Han, He Ma, Romer Rosales, Abby Van Soest, Devansh Tandon, Su-Lin Wu, Weilong Yang, Yilin Zheng

PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations
Large Language Models (LLMs) pose a new paradigm of modeling and computation for information tasks. Recommendation systems are a critical application domain poised to benefit significantly from the sequence modeling capabilities and world knowledge inherent in these large models. In this paper, we introduce PLUM, a framework designed to adapt pre-trained LLMs for industry-scale recommendation tasks. PLUM consists of item tokenization using Semantic IDs, continued pre-training (CPT) on domain-sp…

🇺🇦 #NowPlaying on #BBC6Music's #CerysMatthews
Nino Ferrer:
🎵 Mao et Moa
#NinoFerrer
https://open.spotify.com/track/2Ws8Yijritx9XvNgLEJz35

FIRE 7: Automatic Reduction with Modular Approach
Alexander V. Smirnov, Mao Zeng
https://arxiv.org/abs/2510.07150 https://arxiv.org/pdf/2510.07150

FIRE 7: Automatic Reduction with Modular Approach
FIRE7 is a major update to the FIRE program for integration-by-parts (IBP) reduction of Feynman integrals. A large part of improvements is related to the automatic reduction and reconstruction with the modular arithmetic approach, while the performance of the classical rational polynomial approach is also significantly increased. An improved presolve algorithm performs Gaussian elimination to simplify IBP identities before substituting numerical indices as in the Laporta algorithm. Various new …

MoA-VR: A Mixture-of-Agents System Towards All-in-One Video Restoration
Lu Liu, Chunlei Cai, Shaocheng Shen, Jianfeng Liang, Weimin Ouyang, Tianxiao Ye, Jian Mao, Huiyu Duan, Jiangchao Yao, Xiaoyun Zhang, Qiang Hu, Guangtao Zhai
https://arxiv.org/abs/2510.08508

MoA-VR: A Mixture-of-Agents System Towards All-in-One Video Restoration
Real-world videos often suffer from complex degradations, such as noise, compression artifacts, and low-light distortions, due to diverse acquisition and transmission conditions. Existing restoration methods typically require professional manual selection of specialized models or rely on monolithic architectures that fail to generalize across varying degradations. Inspired by expert experience, we propose MoA-VR, the first \underline{M}ixture-\underline{o}f-\underline{A}gents \underline{V}ideo …

Evolutionary Profiles for Protein Fitness Prediction
Jigang Fan, Xiaoran Jiao, Shengdong Lin, Zhanming Liang, Weian Mao, Chenchen Jing, Hao Chen, Chunhua Shen
https://arxiv.org/abs/2510.07286

Evolutionary Profiles for Protein Fitness Prediction
Predicting the fitness impact of mutations is central to protein engineering but constrained by limited assays relative to the size of sequence space. Protein language models (pLMs) trained with masked language modeling (MLM) exhibit strong zero-shot fitness prediction; we provide a unifying view by interpreting natural evolution as implicit reward maximization and MLM as inverse reinforcement learning (IRL), in which extant sequences act as expert demonstrations and pLM log-odds serve as fitne…

Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation
Zhenyu Zhao, Hongyi Jing, Xiawei Liu, Jiageng Mao, Abha Jha, Hanwen Yang, Rong Xue, Sergey Zakharor, Vitor Guizilini, Yue Wang
https://arxiv.org/abs/2510.08807

Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation
From loco-motion to dextrous manipulation, humanoid robots have made remarkable strides in demonstrating complex full-body capabilities. However, the majority of current robot learning datasets and benchmarks mainly focus on stationary robot arms, and the few existing humanoid datasets are either confined to fixed environments or limited in task diversity, often lacking human-humanoid interaction and lower-body locomotion. Moreover, there are a few standardized evaluation platforms for benchmar…

Training-Free Group Relative Policy Optimization
Yuzheng Cai, Siqi Cai, Yuchen Shi, Zihan Xu, Lichao Chen, Yulei Qin, Xiaoyu Tan, Gang Li, Zongyi Li, Haojia Lin, Yong Mao, Ke Li, Xing Sun
https://arxiv.org/abs/2510.08191

Training-Free Group Relative Policy Optimization
Recent advances in Large Language Model (LLM) agents have demonstrated their promising general capabilities. However, their performance in specialized real-world domains often degrades due to challenges in effectively integrating external tools and specific prompting strategies. While methods like agentic reinforcement learning have been proposed to address this, they typically rely on costly parameter updates, for example, through a process that uses Supervised Fine-Tuning (SFT) followed by a …

From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions
Changyuan Zhao, Ruichen Zhang, Jiacheng Wang, Dusit Niyato, Geng Sun, Xianbin Wang, Shiwen Mao, Abbas Jamalipour
https://arxiv.org/abs/2510.05596

From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions
Self-evolving agentic artificial intelligence (AI) offers a new paradigm for future wireless systems by enabling autonomous agents to continually adapt and improve without human intervention. Unlike static AI models, self-evolving agents embed an autonomous evolution cycle that updates models, tools, and workflows in response to environmental dynamics. This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques,…

Warm absorber outflows in radio-loud active galactic nucleus 3C~59
Yijun Wang, Tao Wang, Junjie Mao, Yerong Xu, Zhicheng He, Zheng Zhou, Chen Li, Yongquan Xue, Jiayi Chen, Fangzheng Shi, Missagh Mehdipour
https://arxiv.org/abs/2510.06752

Warm absorber outflows in radio-loud active galactic nucleus 3C~59
Both jets and ionized outflows in active galactic nuclei (AGNs) are thought to play important roles in affecting the star formation and evolution of host galaxies, but their relationship is still unclear. As a pilot study, we performed a detailed spectral analysis for a radio-loud (RL) AGN 3C~59 ($z=0.1096$) by systematically considering various factors that may affect the fitting results, and thereby establishing a general spectral fitting strategy for subsequent research with larger sample. 3…

Crosslisted article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
- Evolutionary Profiles for Protein Fitness Prediction
Fan, Jiao, Lin, Liang, Mao, Jing, Chen, Shen

🇺🇦 #NowPlaying on BBCRadio3's #NightTracks
Akio Yashiro & Mao Fujita:
🎵 Prelude No. 18 in F minor
#AkioYashiro #MaoFujita
https://open.spotify.com/track/6vDlZ6PE1p1hGFEuxTqiCc

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/3]:
- Fraud detection in credit card transactions using Quantum-Assisted Restricted Boltzmann Machines
Jo\~ao Marcos Cavalcanti de Albuquerque Neto, Gustavo Castro do Amaral, Guilherme Penello Tempor\~ao
https://arxiv.org/abs/2512.17660 https://mastoxiv.page/@arXiv_quantph_bot/115762703945731580
- Vidarc: Embodied Video Diffusion Model for Closed-loop Control
Feng, Xiang, Mao, Tan, Zhang, Huang, Zheng, Liu, Su, Zhu
https://arxiv.org/abs/2512.17661 https://mastoxiv.page/@arXiv_csRO_bot/115762650859932523
- Imputation Uncertainty in Interpretable Machine Learning Methods
Pegah Golchian, Marvin N. Wright
https://arxiv.org/abs/2512.17689 https://mastoxiv.page/@arXiv_statML_bot/115762577479255577
- Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo ...
Shengdu Chai, Chen Lin, Xinyang Dong, Yuqiang Li, Wanli Ouyang, Lei Wang, X. C. Xie
https://arxiv.org/abs/2512.17703 https://mastoxiv.page/@arXiv_condmatstrel_bot/115762481116668454
- Breast Cancer Neoadjuvant Chemotherapy Treatment Response Prediction Using Aligned Longitudinal M...
Rahul Ravi, Ruizhe Li, Tarek Abdelfatah, Stephen Chan, Xin Chen
https://arxiv.org/abs/2512.17759 https://mastoxiv.page/@arXiv_eessIV_bot/115762481771898369
- MedNeXt-v2: Scaling 3D ConvNeXts for Large-Scale Supervised Representation Learning in Medical Im...
Roy, Kirchhoff, Ulrich, Rokuss, Wald, Isensee, Maier-Hein
https://arxiv.org/abs/2512.17774 https://mastoxiv.page/@arXiv_eessIV_bot/115762492258209812
- Domain-Aware Quantum Circuit for QML
Gurinder Singh, Thaddeus Pellegrini, Kenneth M. Merz, Jr
https://arxiv.org/abs/2512.17800 https://mastoxiv.page/@arXiv_quantph_bot/115762723607200478
- Visually Prompted Benchmarks Are Surprisingly Fragile
Feng, Lian, Dunlap, Shu, Wang, Wang, Darrell, Suhr, Kanazawa
https://arxiv.org/abs/2512.17875 https://mastoxiv.page/@arXiv_csCV_bot/115762781936221554
- Learning vertical coordinates via automatic differentiation of a dynamical core
Tim Whittaker, Seth Taylor, Elsa Cardoso-Bihlo, Alejandro Di Luca, Alex Bihlo
https://arxiv.org/abs/2512.17877 https://mastoxiv.page/@arXiv_physicsaoph_bot/115762405092703069
- RadarGen: Automotive Radar Point Cloud Generation from Cameras
Tomer Borreda, Fangqiang Ding, Sanja Fidler, Shengyu Huang, Or Litany
https://arxiv.org/abs/2512.17897 https://mastoxiv.page/@arXiv_csCV_bot/115762783246540528
- Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy
Gahlawat, Aboudonia, Banik, Hovakimyan, Matni, Ames, Zardini, Speranzon
https://arxiv.org/abs/2512.17899 https://mastoxiv.page/@arXiv_eessSY_bot/115762532257741954
- Re-Depth Anything: Test-Time Depth Refinement via Self-Supervised Re-lighting
Ananta R. Bhattarai, Helge Rhodin
https://arxiv.org/abs/2512.17908 https://mastoxiv.page/@arXiv_csCV_bot/115762785868778349
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Adaptive Dynamics Planning for Robot Navigation
Lu Yuanjie, Mao Mingyang, Xu Tong, Wang Linji, Lin Xiaomin, Xiao Xuesu
https://arxiv.org/abs/2510.05330 https://

Adaptive Dynamics Planning for Robot Navigation
Autonomous robot navigation systems often rely on hierarchical planning, where global planners compute collision-free paths without considering dynamics, and local planners enforce dynamics constraints to produce executable commands. This discontinuity in dynamics often leads to trajectory tracking failure in highly constrained environments. Recent approaches integrate dynamics within the entire planning process by gradually decreasing its fidelity, e.g., increasing integration steps and reduci…

AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering
Ziqing Wang, Chengsheng Mao, Xiaole Wen, Yuan Luo, Kaize Ding
https://arxiv.org/abs/2510.02328

AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering
Medical Multimodal Large Language Models (Med-MLLMs) have shown great promise in medical visual question answering (Med-VQA). However, when deployed in low-resource settings where abundant labeled data are unavailable, existing Med-MLLMs commonly fail due to their medical reasoning capability bottlenecks: (i) the intrinsic reasoning bottleneck that ignores the details from the medical image; (ii) the extrinsic reasoning bottleneck that fails to incorporate specialized medical knowledge. To addr…

Crosslisted article(s) found for cs.MA. https://arxiv.org/list/cs.MA/new
[1/1]:
- AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering
Ziqing Wang, Chengsheng Mao, Xiaole Wen, Yuan Luo, Kaize Ding

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/12]:
- MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE
Zongle Huang, Lei Zhu, Zongyuan Zhan, Ting Hu, Weikai Mao, Xianzhi Yu, Yongpan Liu, Tianyu Zhang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- The Diffusion Duality
Sahoo, Deschenaux, Gokaslan, Wang, Chiu, Kuleshov
https://arxiv.org/abs/2506.10892 https://mastoxiv.page/@arXiv_csLG_bot/114675526577078472
- Multimodal Representation Learning and Fusion
Jin, Ge, Xie, Luo, Song, Bi, Liang, Guan, Yeong, Song, Hao
https://arxiv.org/abs/2506.20494 https://mastoxiv.page/@arXiv_csLG_bot/114749113025183688
- The kernel of graph indices for vector search
Mariano Tepper, Ted Willke
https://arxiv.org/abs/2506.20584 https://mastoxiv.page/@arXiv_csLG_bot/114749118923266356
- OptScale: Probabilistic Optimality for Inference-time Scaling
Youkang Wang, Jian Wang, Rubing Chen, Xiao-Yong Wei
https://arxiv.org/abs/2506.22376 https://mastoxiv.page/@arXiv_csLG_bot/114771735361664528
- Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods
Fabian Akkerman, Julien Ferry, Christian Artigues, Emmanuel Hebrard, Thibaut Vidal
https://arxiv.org/abs/2507.18242 https://mastoxiv.page/@arXiv_csLG_bot/114913322736512937
- MolMark: Safeguarding Molecular Structures through Learnable Atom-Level Watermarking
Runwen Hu, Peilin Chen, Keyan Ding, Shiqi Wang
https://arxiv.org/abs/2508.17702 https://mastoxiv.page/@arXiv_csLG_bot/115095014405732247
- Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Protot...
Fatema Siddika, Md Anwar Hossen, Wensheng Zhang, Anuj Sharma, Juan Pablo Mu\~noz, Ali Jannesari
https://arxiv.org/abs/2508.19009 https://mastoxiv.page/@arXiv_csLG_bot/115100269482762688
- STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic
https://arxiv.org/abs/2508.19011 https://mastoxiv.page/@arXiv_csLG_bot/115100270137397046
- EEGDM: Learning EEG Representation with Latent Diffusion Model
Shaocong Wang, Tong Liu, Yihan Li, Ming Li, Kairui Wen, Pei Yang, Wenqi Ji, Minjing Yu, Yong-Jin Liu
https://arxiv.org/abs/2508.20705 https://mastoxiv.page/@arXiv_csLG_bot/115111565155687451
- Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
Xiao Zhang, Zengzhe Chen, Yuan Yuan, Yifei Zou, Fuzhen Zhuang, Wenyu Jiao, Yuke Wang, Dongxiao Yu
https://arxiv.org/abs/2509.25977 https://mastoxiv.page/@arXiv_csLG_bot/115298721327100391
- Fine-Tuning Masked Diffusion for Provable Self-Correction
Jaeyeon Kim, Seunggeun Kim, Taekyun Lee, David Z. Pan, Hyeji Kim, Sham Kakade, Sitan Chen
https://arxiv.org/abs/2510.01384 https://mastoxiv.page/@arXiv_csLG_bot/115309690976554356
- A Generic Machine Learning Framework for Radio Frequency Fingerprinting
Alex Hiles, Bashar I. Ahmad
https://arxiv.org/abs/2510.09775 https://mastoxiv.page/@arXiv_csLG_bot/115372387779061015
- ASecond-Order SpikingSSM for Wearables
Kartikay Agrawal, Abhijeet Vikram, Vedant Sharma, Vaishnavi Nagabhushana, Ayon Borthakur
https://arxiv.org/abs/2510.14386 https://mastoxiv.page/@arXiv_csLG_bot/115389079527543821
- Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning
Heming Zou, Yixiu Mao, Yun Qu, Qi Wang, Xiangyang Ji
https://arxiv.org/abs/2510.16882 https://mastoxiv.page/@arXiv_csLG_bot/115412243355962887
- Seeing Structural Failure Before it Happens: An Image-Based Physics-Informed Neural Network (PINN...
Omer Jauhar Khan, Sudais Khan, Hafeez Anwar, Shahzeb Khan, Shams Ul Arifeen
https://arxiv.org/abs/2510.23117 https://mastoxiv.page/@arXiv_csLG_bot/115451891042176876
- Training Deep Physics-Informed Kolmogorov-Arnold Networks
Spyros Rigas, Fotios Anagnostopoulos, Michalis Papachristou, Georgios Alexandridis
https://arxiv.org/abs/2510.23501 https://mastoxiv.page/@arXiv_csLG_bot/115451942159737549
- Semi-Supervised Preference Optimization with Limited Feedback
Seonggyun Lee, Sungjun Lim, Seojin Park, Soeun Cheon, Kyungwoo Song
https://arxiv.org/abs/2511.00040 https://mastoxiv.page/@arXiv_csLG_bot/115490555013124989
- Towards Causal Market Simulators
Dennis Thumm, Luis Ontaneda Mijares
https://arxiv.org/abs/2511.04469 https://mastoxiv.page/@arXiv_csLG_bot/115507943827841017
- Incremental Generation is Necessary and Sufficient for Universality in Flow-Based Modelling
Hossein Rouhvarzi, Anastasis Kratsios
https://arxiv.org/abs/2511.09902 https://mastoxiv.page/@arXiv_csLG_bot/115547587245365920
- Optimizing Mixture of Block Attention
Guangxuan Xiao, Junxian Guo, Kasra Mazaheri, Song Han
https://arxiv.org/abs/2511.11571 https://mastoxiv.page/@arXiv_csLG_bot/115564541392410174
- Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs
Shasha Zhou, Mingyu Huang, Jack Cole, Charles Britton, Ming Yin, Jan Wolber, Ke Li
https://arxiv.org/abs/2511.12817 https://mastoxiv.page/@arXiv_csLG_bot/115570877730326947
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