Tootfinder

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[3/5]:
- Can Small Language Models Handle Context-Summarized Multi-Turn Customer-Service QA? A Synthetic D...
Lakshan Cooray, Deshan Sumanathilaka, Pattigadapa Venkatesh Raju
https://arxiv.org/abs/2602.00665 https://mastoxiv.page/@arXiv_csCL_bot/116006686092324902
- SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue
Dai, Gao, Zhang, Wang, Luo, Wang, Wang, Wu, Wang
https://arxiv.org/abs/2602.03548
- OmniRAG-Agent: Agentic Omnimodal Reasoning for Low-Resource Long Audio-Video Question Answering
Yifan Zhu, Xinyu Mu, Tao Feng, Zhonghong Ou, Yuning Gong, Haoran Luo
https://arxiv.org/abs/2602.03707
- GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greek
Zhang, Konomi, Xypolopoulos, Divriotis, Skianis, Nikolentzos, Stamou, Shang, Vazirgiannis
https://arxiv.org/abs/2602.05150
- Using LLMs for Knowledge Component-level Correctness Labeling in Open-ended Coding Problems
Zhangqi Duan, Arnav Kankaria, Dhruv Kartik, Andrew Lan
https://arxiv.org/abs/2602.17542 https://mastoxiv.page/@arXiv_csCL_bot/116102514058414603
- MetaState: Persistent Working Memory Enhances Reasoning in Discrete Diffusion Language Models
Kejing Xia, Mingzhe Li, Lixuan Wei, Zhenbang Du, Xiangchi Yuan, Dachuan Shi, Qirui Jin, Wenke Lee
https://arxiv.org/abs/2603.01331 https://mastoxiv.page/@arXiv_csCL_bot/116165314672421581
- A Browser-based Open Source Assistant for Multimodal Content Verification
Milner, Foster, Karmakharm, Razuvayevskaya, Roberts, Porcellini, Teyssou, Bontcheva
https://arxiv.org/abs/2603.02842 https://mastoxiv.page/@arXiv_csCL_bot/116170368271004704
- Nw\=ach\=a Mun\=a: A Devanagari Speech Corpus and Proximal Transfer Benchmark for Nepal Bhasha ASR
Sharma, Shrestha, Poudel, Tiwari, Shrestha, Ghimire, Bal
https://arxiv.org/abs/2603.07554 https://mastoxiv.page/@arXiv_csCL_bot/116204797995674104
- Model Merging in the Era of Large Language Models: Methods, Applications, and Future Directions
Mingyang Song, Mao Zheng
https://arxiv.org/abs/2603.09938 https://mastoxiv.page/@arXiv_csCL_bot/116210189810004206
- AgentDrift: Unsafe Recommendation Drift Under Tool Corruption Hidden by Ranking Metrics in LLM Ag...
Zekun Wu, Adriano Koshiyama, Sahan Bulathwela, Maria Perez-Ortiz
https://arxiv.org/abs/2603.12564 https://mastoxiv.page/@arXiv_csCL_bot/116237800898328349
- GhanaNLP Parallel Corpora: Comprehensive Multilingual Resources for Low-Resource Ghanaian Languages
Gyamfi, Azunre, Moore, Budu, Asare, Owusu, Asiamah
https://arxiv.org/abs/2603.13793 https://mastoxiv.page/@arXiv_csCL_bot/116243544688031749
- sebis at ArchEHR-QA 2026: How Much Can You Do Locally? Evaluating Grounded EHR QA on a Single Not...
Ibrahim Ebrar Yurt, Fabian Karl, Tejaswi Choppa, Florian Matthes
https://arxiv.org/abs/2603.13962 https://mastoxiv.page/@arXiv_csCL_bot/116243646346563497
- ExPosST: Explicit Positioning with Adaptive Masking for LLM-Based Simultaneous Machine Translation
Yuzhe Shang, Pengzhi Gao, Yazheng Yang, Jiayao Ma, Wei Liu, Jian Luan, Jinsong Su
https://arxiv.org/abs/2603.14903 https://mastoxiv.page/@arXiv_csCL_bot/116243711232778054
- BanglaSocialBench: A Benchmark for Evaluating Sociopragmatic and Cultural Alignment of LLMs in Ba...
Tanvir Ahmed Sijan, S. M Golam Rifat, Pankaj Chowdhury Partha, Md. Tanjeed Islam, Md. Musfique Anwar
https://arxiv.org/abs/2603.15949 https://mastoxiv.page/@arXiv_csCL_bot/116249122231759766
- EngGPT2: Sovereign, Efficient and Open Intelligence
G. Ciarfaglia, et al.
https://arxiv.org/abs/2603.16430 https://mastoxiv.page/@arXiv_csCL_bot/116249228411487178
- HypeLoRA: Hyper-Network-Generated LoRA Adapters for Calibrated Language Model Fine-Tuning
Bartosz Trojan, Filip G\k{e}bala
https://arxiv.org/abs/2603.19278 https://mastoxiv.page/@arXiv_csCL_bot/116277612915482857
- Automatic Analysis of Collaboration Through Human Conversational Data Resources: A Review
Yi Yu, Maria Boritchev, Chlo\'e Clavel
https://arxiv.org/abs/2603.19292 https://mastoxiv.page/@arXiv_csCL_bot/116277620779254916
- Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Langu...
Xinyue Liu, Niloofar Mireshghallah, Jane C. Ginsburg, Tuhin Chakrabarty
https://arxiv.org/abs/2603.20957 https://mastoxiv.page/@arXiv_csCL_bot/116283538317671552
- KG-Hopper: Empowering Compact Open LLMs with Knowledge Graph Reasoning via Reinforcement Learning
Shuai Wang, Yinan Yu
https://arxiv.org/abs/2603.21440 https://mastoxiv.page/@arXiv_csCL_bot/116283595007808076
toXiv_bot_toot

Replaced article(s) found for math.AC. https://arxiv.org/list/math.AC/new
[1/1]:
- A topological approach to key polynomials
Enric Nart, Josnei Novacoski, Giulio Peruginelli
https://arxiv.org/abs/2404.08357 https://mastoxiv.page/@arXiv_mathAC_bot/112273919597079364
- Local cohomology with support in Schubert varieties
Michael Perlman
https://arxiv.org/abs/2405.02142 https://mastoxiv.page/@arXiv_mathAG_bot/112392829352971463
- Retrieving biparameter persistence modules from monoparameter ones: a characterization of hook-de...
Isabella Mastroianni, Marco Guerra, Ulderico Fugacci, Emanuela De Negri
https://arxiv.org/abs/2506.14678 https://mastoxiv.page/@test_3/114703138072612318
toXiv_bot_toot

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Diffusion Modulation via Environment Mechanism Modeling for Planning
Hanping Zhang, Yuhong Guo
https://arxiv.org/abs/2602.20422 https://mastoxiv.page/@arXiv_csAI_bot/116130110576555049
- Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
Nihal Balivada, Shrey Gupta, Shashank Shreedhar Bhatt, Suyash Gupta
https://arxiv.org/abs/2602.20450 https://mastoxiv.page/@arXiv_csDC_bot/116130191233002036
- Prior-Agnostic Incentive-Compatible Exploration
Ramya Ramalingam, Osbert Bastani, Aaron Roth
https://arxiv.org/abs/2602.20465 https://mastoxiv.page/@arXiv_csGT_bot/116130245628406144
- PhyGHT: Physics-Guided HyperGraph Transformer for Signal Purification at the HL-LHC
Mohammed Rakib, Luke Vaughan, Shivang Patel, Flera Rizatdinova, Alexander Khanov, Atriya Sen
https://arxiv.org/abs/2602.20475 https://mastoxiv.page/@arXiv_hepex_bot/116130242350426528
- ActionEngine: From Reactive to Programmatic GUI Agents via State Machine Memory
Zhong, Faisal, Fran\c{c}a, Leesatapornwongsa, Szekeres, Rong, Nath
https://arxiv.org/abs/2602.20502 https://mastoxiv.page/@arXiv_csAI_bot/116130180718734838
- Inner Speech as Behavior Guides: Steerable Imitation of Diverse Behaviors for Human-AI coordination
Rakshit Trivedi, Kartik Sharma, David C Parkes
https://arxiv.org/abs/2602.20517 https://mastoxiv.page/@arXiv_csAI_bot/116130223344095649
- Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning
Lovelace, Belardi, Zalouk, Polavaram, Kundurthy, Weinberger
https://arxiv.org/abs/2602.20528 https://mastoxiv.page/@arXiv_csCL_bot/116130628998822849
- Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ T...
Yanming Lai, Defeng Sun
https://arxiv.org/abs/2602.20555 https://mastoxiv.page/@arXiv_statML_bot/116130512372759166
- Personal Information Parroting in Language Models
Nishant Subramani, Kshitish Ghate, Mona Diab
https://arxiv.org/abs/2602.20580 https://mastoxiv.page/@arXiv_csCL_bot/116130630309564204
- Characterizing Online and Private Learnability under Distributional Constraints via Generalized S...
Mo\"ise Blanchard, Abhishek Shetty, Alexander Rakhlin
https://arxiv.org/abs/2602.20585 https://mastoxiv.page/@arXiv_statML_bot/116130525452248337
- Amortized Bayesian inference for actigraph time sheet data from mobile devices
Daniel Zhou, Sudipto Banerjee
https://arxiv.org/abs/2602.20611 https://mastoxiv.page/@arXiv_statML_bot/116130543144314661
- Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model
Xueqiang Lv, Shizhou Zhang, Yinghui Xing, Di Xu, Peng Wang, Yanning Zhang
https://arxiv.org/abs/2602.20616 https://mastoxiv.page/@arXiv_csCV_bot/116130795466851481
- On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes
Boao Kong, Hengrui Zhang, Kun Yuan
https://arxiv.org/abs/2602.20646 https://mastoxiv.page/@arXiv_mathOC_bot/116130476952419594
- DANCE: Doubly Adaptive Neighborhood Conformal Estimation
Feng, Reich, Beaglehole, Luo, Park, Yoo, Huang, Mao, Boz, Kim
https://arxiv.org/abs/2602.20652 https://mastoxiv.page/@arXiv_statML_bot/116130551664144143
- Vision-Language Models for Ergonomic Assessment of Manual Lifting Tasks: Estimating Horizontal an...
Mohammad Sadra Rajabi, Aanuoluwapo Ojelade, Sunwook Kim, Maury A. Nussbaum
https://arxiv.org/abs/2602.20658 https://mastoxiv.page/@arXiv_csCV_bot/116130809228818544
- F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performanc...
Xuran Ma, et al.
https://arxiv.org/abs/2602.20712 https://mastoxiv.page/@arXiv_astrophIM_bot/116130530693731576
- Communication-Inspired Tokenization for Structured Image Representations
Davtyan, Sahin, Haghighi, Stapf, Acuaviva, Alahi, Favaro
https://arxiv.org/abs/2602.20731 https://mastoxiv.page/@arXiv_csCV_bot/116130824303022936
- SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing
Yifei Xu, et al.
https://arxiv.org/abs/2602.20751 https://mastoxiv.page/@arXiv_csCL_bot/116130739757479992
- Assessing the Impact of Speaker Identity in Speech Spoofing Detection
Anh-Tuan Dao, Driss Matrouf, Nicholas Evans
https://arxiv.org/abs/2602.20805 https://mastoxiv.page/@arXiv_csSD_bot/116130218074059060
- Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation
Sayantan Dasgupta, Trevor Cohn, Timothy Baldwin
https://arxiv.org/abs/2602.20816 https://mastoxiv.page/@arXiv_csCL_bot/116130753521420972
- DRESS: A Continuous Framework for Structural Graph Refinement
Eduar Castrillo Velilla
https://arxiv.org/abs/2602.20833 https://mastoxiv.page/@arXiv_csDS_bot/116130545112457981
toXiv_bot_toot

Scholarly (history) tables of contents, EN vs FR:
English: no more than 3 words per chapter title. These are merely signifiers, empty, regardless of length, until one has digested the argument each lays out. Why waste ink and paper on such pointless abstraction?
French: describing a chapter in fewer words than are contained in the chapter betrays the essence of the work. Still, we know that nothing is perfect and brevity has its merits so here is our 27-page table of contents.

[2026-03-31 Tue (UTC), 3 new articles found for math.CT Category Theory]
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[2026-03-30 Mon (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

Replaced article(s) found for physics.class-ph. https://arxiv.org/list/physics.class-ph/new
[1/1]:
- A note on Gurzadyan theorem
Christian Carimalo

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/3]:
- SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary
Farabi, Pinto, Lu, Ramos-Maqueda, Das, Deeb, Sautmann
https://arxiv.org/abs/2602.18431 https://mastoxiv.page/@arXiv_csCY_bot/116119352329590193
- Benchmarking Distilled Language Models: Performance and Efficiency in Resource-Constrained Settings
Sachin Gopal Wani, Eric Page, Ajay Dholakia, David Ellison
https://arxiv.org/abs/2602.20164 https://mastoxiv.page/@arXiv_csCL_bot/116130101399805837
- VISION-ICE: Video-based Interpretation and Spatial Identification of Arrhythmia Origins via Neura...
Dorsa EPMoghaddam, Feng Gao, Drew Bernard, Kavya Sinha, Mehdi Razavi, Behnaam Aazhang
https://arxiv.org/abs/2602.20165 https://mastoxiv.page/@arXiv_csCV_bot/116130222034322594
- Benchmarking Early Deterioration Prediction Across Hospital-Rich and MCI-Like Emergency Triage Un...
KMA Solaiman, Joshua Sebastian, Karma Tobden
https://arxiv.org/abs/2602.20168 https://mastoxiv.page/@arXiv_csCY_bot/116130239074411770
- Cross-Chirality Generalization by Axial Vectors for Hetero-Chiral Protein-Peptide Interaction Design
Yang, Tian, Jia, Zhang, Zheng, Wang, Su, He, Liu, Lan
https://arxiv.org/abs/2602.20176 https://mastoxiv.page/@arXiv_qbioBM_bot/116130281674122586
- Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic St...
Aniruddha Bora, Isabel K. Alvarez, Julie Chalfant, Chryssostomos Chryssostomidis
https://arxiv.org/abs/2602.20177 https://mastoxiv.page/@arXiv_csNE_bot/116130397676559696
- Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis
Yongwei Yi, Xinping Yi, Wenjin Wang, Xiao Li, Shi Jin
https://arxiv.org/abs/2602.20178 https://mastoxiv.page/@arXiv_eessSP_bot/116130257424413457
- OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs
Mohammadmahdi Vahediahmar, Matthew A. McDonald, Feng Liu
https://arxiv.org/abs/2602.20195 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/116130271189617558
- KEMP-PIP: A Feature-Fusion Based Approach for Pro-inflammatory Peptide Prediction
Soumik Deb Niloy, Md. Fahmid-Ul-Alam Juboraj, Swakkhar Shatabda
https://arxiv.org/abs/2602.20198 https://mastoxiv.page/@arXiv_qbioQM_bot/116130341315320687
- Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control
Shaorong Chen, Jingbo Zhou, Jun Xia
https://arxiv.org/abs/2602.20209 https://mastoxiv.page/@arXiv_qbioQM_bot/116130374083646541
- The Sim-to-Real Gap in MRS Quantification: A Systematic Deep Learning Validation for GABA
Zien Ma, S. M. Shermer, Oktay Karaku\c{s}, Frank C. Langbein
https://arxiv.org/abs/2602.20289 https://mastoxiv.page/@arXiv_eessSP_bot/116130267228834775
- Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Appro...
Haochen Zhang, Zhong Zheng, Lingzhou Xue
https://arxiv.org/abs/2602.20297 https://mastoxiv.page/@arXiv_statML_bot/116130255458256497
- Multilevel Determinants of Overweight and Obesity Among U.S. Children Aged 10-17: Comparative Eva...
Joyanta Jyoti Mondal
https://arxiv.org/abs/2602.20303 https://mastoxiv.page/@arXiv_csAI_bot/116130097466859145
- An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes ...
Shyr, Hu, Tinker, Cassini, Byram, Hamid, Fabbri, Wright, Peterson, Bastarache, Xu
https://arxiv.org/abs/2602.20324 https://mastoxiv.page/@arXiv_csAI_bot/116130100089848459
- Circuit Tracing in Vision-Language Models: Understanding the Internal Mechanisms of Multimodal Th...
Jingcheng Yang, Tianhu Xiong, Shengyi Qian, Klara Nahrstedt, Mingyuan Wu
https://arxiv.org/abs/2602.20330 https://mastoxiv.page/@arXiv_csCV_bot/116130463214879334
- No One Size Fits All: QueryBandits for Hallucination Mitigation
Nicole Cho, William Watson, Alec Koppel, Sumitra Ganesh, Manuela Veloso
https://arxiv.org/abs/2602.20332 https://mastoxiv.page/@arXiv_csCL_bot/116130370809116915
- Learning During Detection: Continual Learning for Neural OFDM Receivers via DMRS
Mohanad Obeed, Ming Jian
https://arxiv.org/abs/2602.20361 https://mastoxiv.page/@arXiv_csIT_bot/116130289537785136
- Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
Joel Persson, Jurri\"en Bakker, Dennis Bohle, Stefan Feuerriegel, Florian von Wangenheim
https://arxiv.org/abs/2602.20383 https://mastoxiv.page/@arXiv_statME_bot/116130509065601748
- Selecting Optimal Variable Order in Autoregressive Ising Models
Shiba Biswal, Marc Vuffray, Andrey Y. Lokhov
https://arxiv.org/abs/2602.20394 https://mastoxiv.page/@arXiv_statML_bot/116130299369541741
toXiv_bot_toot

LombardoGraphia: Automatic Classification of Lombard Orthography Variants
Edoardo Signoroni, Pavel Rychl\'y
https://arxiv.org/abs/2603.28418 https://arxiv.org/pdf/2603.28418 https://arxiv.org/html/2603.28418
arXiv:2603.28418v1 Announce Type: new
Abstract: Lombard, an underresourced language variety spoken by approximately 3.8 million people in Northern Italy and Southern Switzerland, lacks a unified orthographic standard. Multiple orthographic systems exist, creating challenges for NLP resource development and model training. This paper presents the first study of automatic Lombard orthography classification and LombardoGraphia, a curated corpus of 11,186 Lombard Wikipedia samples tagged across 9 orthographic variants, and models for automatic orthography classification. We curate the dataset, processing and filtering raw Wikipedia content to ensure text suitable for orthographic analysis. We train 24 traditional and neural classification models with various features and encoding levels. Our best models achieve 96.06% and 85.78% overall and average class accuracy, though performance on minority classes remains challenging due to data imbalance. Our work provides crucial infrastructure for building variety-aware NLP resources for Lombard.
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[2026-03-30 Mon (UTC), no new articles found for physics.class-ph Classical Physics]
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Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/3]:
- Functional Continuous Decomposition
Teymur Aghayev
https://arxiv.org/abs/2602.20857 https://mastoxiv.page/@arXiv_eessSP_bot/116130499236089653
- SpatiaLQA: A Benchmark for Evaluating Spatial Logical Reasoning in Vision-Language Models
Xie, Zhang, Shan, Zhu, Tang, Wei, Song, Wan, Song
https://arxiv.org/abs/2602.20901 https://mastoxiv.page/@arXiv_csCV_bot/116130845273808954
- Some Simple Economics of AGI
Christian Catalini, Xiang Hui, Jane Wu
https://arxiv.org/abs/2602.20946 https://mastoxiv.page/@arXiv_econGN_bot/116130470423837005
- Multimodal MRI Report Findings Supervised Brain Lesion Segmentation with Substructures
Yubin Ge, Yongsong Huang, Xiaofeng Liu
https://arxiv.org/abs/2602.20994 https://mastoxiv.page/@arXiv_eessIV_bot/116130212832138624
- MIP Candy: A Modular PyTorch Framework for Medical Image Processing
Tianhao Fu, Yucheng Chen
https://arxiv.org/abs/2602.21033 https://mastoxiv.page/@arXiv_csCV_bot/116130864279556063
- Empirically Calibrated Conditional Independence Tests
Milleno Pan, Antoine de Mathelin, Wesley Tansey
https://arxiv.org/abs/2602.21036 https://mastoxiv.page/@arXiv_statME_bot/116130690605113562
- Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise
Rafael Hanashiro, Abhishek Shetty, Patrick Jaillet
https://arxiv.org/abs/2602.21039 https://mastoxiv.page/@arXiv_statML_bot/116130572661848449
- Position-Aware Sequential Attention for Accurate Next Item Recommendations
Timur Nabiev, Evgeny Frolov
https://arxiv.org/abs/2602.21052 https://mastoxiv.page/@arXiv_csIR_bot/116130263323086316
- Motivation is Something You Need
Mehdi Acheli, Walid Gaaloul
https://arxiv.org/abs/2602.21064 https://mastoxiv.page/@arXiv_csAI_bot/116130680774678580
- An Enhanced Projection Pursuit Tree Classifier with Visual Methods for Assessing Algorithmic Impr...
Natalia da Silva, Dianne Cook, Eun-Kyung Lee
https://arxiv.org/abs/2602.21130 https://mastoxiv.page/@arXiv_statML_bot/116130610674573081
- Complexity of Classical Acceleration for $\ell_1$-Regularized PageRank
Kimon Fountoulakis, David Mart\'inez-Rubio
https://arxiv.org/abs/2602.21138 https://mastoxiv.page/@arXiv_mathOC_bot/116130547076073836
- LUMEN: Longitudinal Multi-Modal Radiology Model for Prognosis and Diagnosis
Jiang, Yang, Nath, Parida, Kulkarni, Xu, Xu, Anwar, Roth, Linguraru
https://arxiv.org/abs/2602.21142 https://mastoxiv.page/@arXiv_csCV_bot/116130871488694585
- A Benchmark for Deep Information Synthesis
Debjit Paul, et al.
https://arxiv.org/abs/2602.21143 https://mastoxiv.page/@arXiv_csAI_bot/116130692571594706
- Scaling State-Space Models on Multiple GPUs with Tensor Parallelism
Anurag Dutt, Nimit Shah, Hazem Masarani, Anshul Gandhi
https://arxiv.org/abs/2602.21144 https://mastoxiv.page/@arXiv_csDC_bot/116130520888343997
- Not Just How Much, But Where: Decomposing Epistemic Uncertainty into Per-Class Contributions
Mame Diarra Toure, David A. Stephens
https://arxiv.org/abs/2602.21160 https://mastoxiv.page/@arXiv_statML_bot/116130618512594211
- Aletheia tackles FirstProof autonomously
Tony Feng, et al.
https://arxiv.org/abs/2602.21201 https://mastoxiv.page/@arXiv_csAI_bot/116130705679345625
- Squint: Fast Visual Reinforcement Learning for Sim-to-Real Robotics
Abdulaziz Almuzairee, Henrik I. Christensen
https://arxiv.org/abs/2602.21203 https://mastoxiv.page/@arXiv_csRO_bot/116130765974498223
toXiv_bot_toot

Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design
Bin Zhu, Qianghuai Jia, Tian Lan, Junyang Ren, Feng Gu, Feihu Jiang, Longyue Wang, Zhao Xu, Weihua Luo
https://arxiv.org/abs/2603.28376 https://arxiv.org/pdf/2603.28376 https://arxiv.org/html/2603.28376
arXiv:2603.28376v1 Announce Type: new
Abstract: Deep research agents autonomously conduct open-ended investigations, integrating complex information retrieval with multi-step reasoning across diverse sources to solve real-world problems. To sustain this capability on long-horizon tasks, reliable verification is critical during both training and inference. A major bottleneck in existing paradigms stems from the lack of explicit verification mechanisms in QA data synthesis, trajectory construction, and test-time scaling. Errors introduced at each stage propagate downstream and degrade the overall agent performance. To address this, we present Marco DeepResearch, a deep research agent optimized with a verification-centric framework design at three levels: \textbf{(1)~QA Data Synthesis:} We introduce verification mechanisms to graph-based and agent-based QA synthesis to control question difficulty while ensuring answers are unique and correct; \textbf{(2)~Trajectory Construction:} We design a verification-driven trajectory synthesis method that injects explicit verification patterns into training trajectories; and \textbf{(3)~Test-time scaling:} We use Marco DeepResearch itself as a verifier at inference time and effectively improve performance on challenging questions. Extensive experimental results demonstrate that our proposed Marco DeepResearch agent significantly outperforms 8B-scale deep research agents on most challenging benchmarks, such as BrowseComp and BrowseComp-ZH. Crucially, under a maximum budget of 600 tool calls, Marco DeepResearch even surpasses or approaches several 30B-scale agents, like Tongyi DeepResearch-30B.
toXiv_bot_toot

L1-2-type surfaces in 3-dimensional De Sitter and anti De Sitter spaces
S. Carolina Garc\'ia-Mart\'inez, Pascual Lucas, H. Fabi\'an Ram\'irez-Ospina
https://arxiv.org/abs/2601.18019

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/6]:
- Towards Scalable Oversight via Partitioned Human Supervision
Ren Yin, Takashi Ishida, Masashi Sugiyama
https://arxiv.org/abs/2510.22500 https://mastoxiv.page/@arXiv_csLG_bot/115451787490434401
- ContextPilot: Fast Long-Context Inference via Context Reuse
Yinsicheng Jiang, Yeqi Huang, Liang Cheng, Cheng Deng, Xuan Sun, Luo Mai
https://arxiv.org/abs/2511.03475 https://mastoxiv.page/@arXiv_csLG_bot/115502245581974540
- Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning
Nabil Belacel, Mohamed Rachid Boulassel
https://arxiv.org/abs/2601.11283 https://mastoxiv.page/@arXiv_csLG_bot/115921183182326799
- PhysE-Inv: A Physics-Encoded Inverse Modeling approach for Arctic Snow Depth Prediction
Akila Sampath, Vandana Janeja, Jianwu Wang
https://arxiv.org/abs/2601.17074
- SAGE-5GC: Security-Aware Guidelines for Evaluating Anomaly Detection in the 5G Core Network
Cristian Manca, Christian Scano, Giorgio Piras, Fabio Brau, Maura Pintor, Battista Biggio
https://arxiv.org/abs/2602.03596
- LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordina...
Anand, Helbling, Davenport, Berman, Alagapan, Rozell
https://arxiv.org/abs/2602.04192
- Towards Robust Scaling Laws for Optimizers
Alexandra Volkova, Mher Safaryan, Christoph H. Lampert, Dan Alistarh
https://arxiv.org/abs/2602.07712 https://mastoxiv.page/@arXiv_csLG_bot/116046369672796465
- Do We Need Adam? Surprisingly Strong and Sparse Reinforcement Learning with SGD in LLMs
Sagnik Mukherjee, Lifan Yuan, Pavan Jayasinha, Dilek Hakkani-T\"ur, Hao Peng
https://arxiv.org/abs/2602.07729 https://mastoxiv.page/@arXiv_csLG_bot/116046377539155485
- AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine L...
Yuzhu Cai, Zexi Liu, Xinyu Zhu, Cheng Wang, Siheng Chen
https://arxiv.org/abs/2602.07906 https://mastoxiv.page/@arXiv_csLG_bot/116046423413650658
- VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training
Guobin Shen, Chenxiao Zhao, Xiang Cheng, Lei Huang, Xing Yu
https://arxiv.org/abs/2602.10693 https://mastoxiv.page/@arXiv_csLG_bot/116057229834947730
- KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
Zukang Xu, Zhixiong Zhao, Xing Hu, Zhixuan Chen, Dawei Yang
https://arxiv.org/abs/2602.11184 https://mastoxiv.page/@arXiv_csLG_bot/116062537528208461
- MUSE: Multi-Tenant Model Serving With Seamless Model Updates
Correia, Ferreira, Martins, Bento, Guerreiro, Pereira, Gomes, Bono, Ferreira, Bizarro
https://arxiv.org/abs/2602.11776 https://mastoxiv.page/@arXiv_csLG_bot/116062952355379801
- Pawsterior: Variational Flow Matching for Structured Simulation-Based Inference
Jorge Carrasco-Pollo, Floor Eijkelboom, Jan-Willem van de Meent
https://arxiv.org/abs/2602.13813 https://mastoxiv.page/@arXiv_csLG_bot/116085828112928218
- Silent Inconsistency in Data-Parallel Full Fine-Tuning: Diagnosing Worker-Level Optimization Misa...
Hong Li, Zhen Zhou, Honggang Zhang, Yuping Luo, Xinyue Wang, Han Gong, Zhiyuan Liu
https://arxiv.org/abs/2602.14462 https://mastoxiv.page/@arXiv_csLG_bot/116085997857526328
- Divine Benevolence is an $x^2$: GLUs scale asymptotically faster than MLPs
Alejandro Francisco Queiruga
https://arxiv.org/abs/2602.14495 https://mastoxiv.page/@arXiv_csLG_bot/116086011618741857
- \"UberWeb: Insights from Multilingual Curation for a 20-Trillion-Token Dataset
DatologyAI, et al.
https://arxiv.org/abs/2602.15210 https://mastoxiv.page/@arXiv_csLG_bot/116090912256712568
- GLM-5: from Vibe Coding to Agentic Engineering
GLM-5-Team, et al.
https://arxiv.org/abs/2602.15763 https://mastoxiv.page/@arXiv_csLG_bot/116091080686771018
- Anatomy of Capability Emergence: Scale-Invariant Representation Collapse and Top-Down Reorganizat...
Jayadev Billa
https://arxiv.org/abs/2602.15997 https://mastoxiv.page/@arXiv_csLG_bot/116096541546306333
- AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models
KC Santosh, Srikanth Baride, Rodrigue Rizk
https://arxiv.org/abs/2602.16042 https://mastoxiv.page/@arXiv_csLG_bot/116096581524696028
- Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning
Chuqin Geng, Li Zhang, Haolin Ye, Ziyu Zhao, Yuhe Jiang, Tara Saba, Xinyu Wang, Xujie Si
https://arxiv.org/abs/2602.16947 https://mastoxiv.page/@arXiv_csLG_bot/116102426238903124
toXiv_bot_toot

Texans hit offseason with familiar feeling after another divisional round loss https://www.espn.com/nfl/story/_/page/Houston-Texans/houston-texans-offseason-familiar-place-divisional-round-loss-cj-stroud

LoD-Structured 3D Gaussian Splatting for Streaming Video Reconstruction
Xinhui Liu, Can Wang, Lei Liu, Zhenghao Chen, Wei Jiang, Wei Wang, Dong Xu
https://arxiv.org/abs/2601.18475 https://arxiv.org/pdf/2601.18475 https://arxiv.org/html/2601.18475
arXiv:2601.18475v1 Announce Type: new
Abstract: Free-Viewpoint Video (FVV) reconstruction enables photorealistic and interactive 3D scene visualization; however, real-time streaming is often bottlenecked by sparse-view inputs, prohibitive training costs, and bandwidth constraints. While recent 3D Gaussian Splatting (3DGS) has advanced FVV due to its superior rendering speed, Streaming Free-Viewpoint Video (SFVV) introduces additional demands for rapid optimization, high-fidelity reconstruction under sparse constraints, and minimal storage footprints. To bridge this gap, we propose StreamLoD-GS, an LoD-based Gaussian Splatting framework designed specifically for SFVV. Our approach integrates three core innovations: 1) an Anchor- and Octree-based LoD-structured 3DGS with a hierarchical Gaussian dropout technique to ensure efficient and stable optimization while maintaining high-quality rendering; 2) a GMM-based motion partitioning mechanism that separates dynamic and static content, refining dynamic regions while preserving background stability; and 3) a quantized residual refinement framework that significantly reduces storage requirements without compromising visual fidelity. Extensive experiments demonstrate that StreamLoD-GS achieves competitive or state-of-the-art performance in terms of quality, efficiency, and storage.
toXiv_bot_toot

A sustainable photocatalytic pathway for concurrent hydrogen and value-added chemical production utilizing microalgae as bio-scavenger in water
Ho Truong Nam Hai, Augusto Ducati Luchessi, Kaveh Edalati
https://arxiv.org/abs/2603.24924 https://arxiv.org/pdf/2603.24924 https://arxiv.org/html/2603.24924
arXiv:2603.24924v1 Announce Type: new
Abstract: Microalgae are an abundant bioorganic material source and play a significant role in life on Earth by conducting photosynthesis for carbon dioxide (CO2) capture and its conversion to oxygen (O2). In this study, a combination of microalgae as a negative-CO2-emitting sacrificial agent with the traditional photocatalytic water-splitting process using brookite TiO2, as a model photocatalyst, is introduced as a new strategy to maximize green hydrogen (H2) production while converting microalgae to valuable products, like methane (CH4) and carbon monoxide (CO). The process, under optimal conditions, produces up to 0.990 mmol/g.h of H2 without cocatalyst addition and 3.200 mmol/g.h with platinum (Pt) cocatalyst, which is 13 times higher than the production rate without microalgae. The strategy of using microalgae in photocatalysis has high potential in green H2 production, as it not only eliminates valuable hole sacrificial agents, like alcohol, but also produces other useful compounds, like CH4 and CO. Moreover, this sustainable process contributes to CO2 capture and conversion during microalgae cultivation.
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The Self-Replication Phase Diagram: Mapping Where Life Becomes Possible in Cellular Automata Rule Space
Don Yin
https://arxiv.org/abs/2603.25239 https://arxiv.org/pdf/2603.25239 https://arxiv.org/html/2603.25239
arXiv:2603.25239v1 Announce Type: new
Abstract: What substrate features allow life? We exhaustively classify all 262,144 outer-totalistic binary cellular automata rules with Moore neighbourhood for self-replication and produce phase diagrams in the $(\lambda, F)$ plane, where $\lambda$ is Langton's rule density and $F$ is a background-stability parameter. Of these rules, 20,152 (7.69%) support pattern proliferation, concentrated at low rule density ($\lambda \approx 0.15$--$0.25$) and low-to-moderate background stability ($F \approx 0.2$--$0.3$), in the weakly supercritical regime (Derrida coefficient $\mu = 1.81$ for replicators vs. $1.39$ for non-replicators). Self-replicating rules are more approximately mass-conserving (mass-balance 0.21 vs. 0.34), and this generalises to $k{=}3$ Moore rules. A three-tier detection hierarchy (pattern proliferation, extended-length confirmation, and causal perturbation) yields an estimated 1.56% causal self-replication rate. Self-replication rate increases monotonically with neighbourhood size under equalised detection: von Neumann 4.79%, Moore 7.69%, extended Moore 16.69%. These results identify background stability and approximate mass conservation as the primary axes of the self-replication phase boundary.
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[2026-01-28 Wed (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

learn 👇
https://archive.org/details/0-kathara-pics-all-3814-pages/0KATHARA PICS ALL 3814 PAGES/page/n989/mode/2up

This weekend I worked on my custom /blogroll page
it has 3 input types :
1. you construct the collection by adding blogs manually
2. you import an OPML with your collection
3. you connect your /microsub existing collection to feed the blogroll on the frontend.
https://rmendes.net/notes/2026/02/08/3

Ricardo Mendes
8 February 2026 indiekit plugins blogroll opml This weekend I worked on my custom /blogroll page it has 3 input types : you construct the collection by adding blogs manually you import an OPML with yo...

Curvature and Lagrangian submanifolds of nearly K\"ahler $\mathbb{C}P^3$
Micha\"el Liefsoens, Joeri Van der Veken
https://arxiv.org/abs/2601.18504 https://

[2026-02-24 Tue (UTC), 3 new articles found for physics.acc-ph Accelerator Physics]
toXiv_bot_toot

How did the Urban Network Flow Adapt to the Collapse of the Carola Bridge?
Jyotirmaya Ijaradar, Ning Xie, Lei Wei, Sebastian Pape, Matthias K\"orner, Meng Wang
https://arxiv.org/abs/2603.19947 https://arxiv.org/pdf/2603.19947 https://arxiv.org/html/2603.19947
arXiv:2603.19947v1 Announce Type: new
Abstract: The unexpected collapse of the Carola Bridge in Dresden, Germany, provides a rare opportunity to characterise how urban network traffic adapts to an unexpected infrastructure disruption. This study develops a data-driven analytical framework using traffic data from the Dresden traffic management system to assess the short-term impacts of the disruption. By combining statistical comparisons of pre- and post-collapse motorised traffic distributions, peak-hour shifts, and Park-and-Ride data analyses, the framework reveals how traffic dynamics and traveller choices adjust under infrastructure disruption. Results reveal that the two closest bridges, the Albert and Marien Bridges, absorb the majority of the diverted motorised traffic. In particular, the daily traffic volume on the Albert bridge increases by up to 81%, which is equivalent to 3.5 hours of traffic operating with maximum flow. Peak hours on critical links are significantly prolonged, reaching up to 250 minutes. Besides redistribution, the overall daily motorised traffic crossing the Elbe river declines by approximately 8,000 vehicles, while Park-and-Ride usage increases by up to 188%, suggesting a potential travel mode shift after the disruption. The study reveals the patterns of traffic redistribution following an unexpected disruption and provides insights for resilience planning and emergency traffic management.
toXiv_bot_toot

On 3-Connected Planar Graphs with Unique Orientable Circuit Double Covers
Meike Wei{\ss}, Reymond Akpanya, Alice C. Niemeyer
https://arxiv.org/abs/2601.10171 https://

Quadric surfaces of revolution in the 3-sphere as Weingarten surfaces
Ildefonso Castro, Daniel L\'opez-L\'opez
https://arxiv.org/abs/2602.21785 https://arxiv.org/pdf/2602.21785 https://arxiv.org/html/2602.21785
arXiv:2602.21785v1 Announce Type: new
Abstract: The study of quadric surfaces of revolution is a cornerstone of classical Euclidean geometry, but its extension to the three-dimensional sphere $\mathbb{S}^3$ has not been sufficiently explored. This article addresses this important gap by providing a rigorous classification and characterization of non-degenerate quadric surfaces of revolution in $\mathbb{S}^3$, namely spherical ellipsoids, hyperboloids and paraboloids, generated by the rotation of spherical conics around a geodesic axis containing their foci or is orthogonal to them.
Using the concept of spherical angular momentum as a prominent geometric invariant, we discover that these surfaces constitute a remarkable class of Weingarten surfaces and prove that they are uniquely characterised by a specific cubic functional relation between their principal curvatures. This result not only provides a unified description of spherical quadric surfaces of revolution, but also highlights a profound geometric universality, reflecting exactly the same cubic Weingarten relations observed in their Euclidean and Lorentzian counterparts.
toXiv_bot_toot

[2026-01-23 Fri (UTC), 3 new articles found for cond-mat.dis-nn Disordered Systems and Neural Networks]
toXiv_bot_toot

Space Complexity Dichotomies for Subgraph Finding Problems in the Streaming Model
Yu-Sheng Shih, Meng-Tsung Tsai, Yen-Chu Tsai, Ying-Sian Wu
https://arxiv.org/abs/2602.08002 https://arxiv.org/pdf/2602.08002 https://arxiv.org/html/2602.08002
arXiv:2602.08002v1 Announce Type: new
Abstract: We study the space complexity of four variants of the standard subgraph finding problem in the streaming model. Specifically, given an $n$-vertex input graph and a fixed-size pattern graph, we consider two settings: undirected simple graphs, denoted by $G$ and $H$, and oriented graphs, denoted by $\vec{G}$ and $\vec{H}$. Depending on the setting, the task is to decide whether $G$ contains $H$ as a subgraph or as an induced subgraph, or whether $\vec{G}$ contains $\vec{H}$ as a subgraph or as an induced subgraph. Let Sub$(H)$, IndSub$(H)$, Sub$(\vec{H})$, and IndSub$(\vec{H})$ denote these four variants, respectively.
An oriented graph is well-oriented if it admits a bipartition in which every arc is oriented from one part to the other, and a vertex is non-well-oriented if both its in-degree and out-degree are non-zero. For each variant, we obtain a complete dichotomy theorem, briefly summarized as follows.
(1) Sub$(H)$ can be solved by an $\tilde{O}(1)$-pass $n^{2-\Omega(1)}$-space algorithm if and only if $H$ is bipartite.
(2) IndSub$(H)$ can be solved by an $\tilde{O}(1)$-pass $n^{2-\Omega(1)}$-space algorithm if and only if $H \in \{P_3, P_4, co\mbox{-}P_3\}$.
(3) Sub$(\vec{H})$ can be solved by a single-pass $n^{2-\Omega(1)}$-space algorithm if and only if every connected component of $\vec H$ is either a well-oriented bipartite graph or a tree containing at most one non-well-oriented vertex.
(4) IndSub$(\vec{H})$ can be solved by an $\tilde{O}(1)$-pass $n^{2-\Omega(1)}$-space algorithm if and only if the underlying undirected simple graph $H$ is a $co\mbox{-}P_3$.
toXiv_bot_toot

The CfP for the 3rd International Workshop of Semantic Digital Humanities is out!
submission deadline for papers & panels: March 3, 2026
webpage: #semanticweb

Replaced article(s) found for physics.class-ph. https://arxiv.org/list/physics.class-ph/new
[1/1]:
- General method for solving nonlinear optical scattering problems using fix point iterations
Per Kristen Jakobsen

Superconducting Accelerator Magnets
Stephane Sanfilippo
https://arxiv.org/abs/2602.19830 https://arxiv.org/pdf/2602.19830 https://arxiv.org/html/2602.19830
arXiv:2602.19830v1 Announce Type: new
Abstract: This course introduces key aspects of superconducting magnet technology in accelerators: basic principles, superconducting materials (NbTi, Nb$_3$Sn, ReBCO), wire and cable architectures, and fabrication methods. Compared to copper or permanent magnets, superconducting systems require cryogenics and complex protection schemes but enable superior performance. Core challenges - like flux pinning, magnetization effects, quench behavior, mechanical forces interception, power tests and magnetic measurements - are addressed through examples of magnets from PSI and CERN.
toXiv_bot_toot

I realize that in 2026, saying the WaPo op-ed page has gotten dumber than a bag of bricks is not saying something novel, but "Your salted caramel mocha latte is destroying society", invoking Edmund Burke, might set a benchmark for the year even though we're only 3 months in.

[2026-03-25 Wed (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

Assessing the Impact of Fitting Methodology at aN$^3$LO with FPPDF: an Open Source Tool for Extracting Parton Distribution Functions in the Hessian Approach
J. M. Cruz-Martinez, T. Giani, L. A. Harland-Lang
https://arxiv.org/abs/2602.07118

Crosslisted article(s) found for physics.class-ph. https://arxiv.org/list/physics.class-ph/new
[1/1]:
- Mapping the limits of equilibrium in sheared granular liquid crystals
Jacopo Bilotto, Martin Trulsson, Jean-Fran\c{c}ois Molinari

[2026-03-11 Wed (UTC), 3 new articles found for math.CA Classical Analysis and ODEs]
toXiv_bot_toot

[2026-02-10 Tue (UTC), 3 new articles found for cs.OS Operating Systems]
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Magnetic equations on the Heisenberg group: symmetries, solutions and the inverse problem of the calculus of variations
Gabriela Ovando, Mauro Subils
https://arxiv.org/abs/2602.21187 https://arxiv.org/pdf/2602.21187 https://arxiv.org/html/2602.21187
arXiv:2602.21187v1 Announce Type: new
Abstract: The Heisenberg Lie group $H_3$ is modeled on the differentiable structure of $\mathbb{R}^3$ but equipped with another non-commutative product operation. By fixing the usual metric on the Heisenberg Lie group, this work provides a comprehensive overview of the behavior of magnetic geodesics for any invariant Lorentz force. After writing the magnetic equations, we found symmetries that enable the explicit computation of the magnetic trajectories for any homogeneous exact and non-exact magnetic form. Finally we show that these magnetic trajectories are solutions of a variational problem: we present explicit examples of Lagrangians.
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Proc3D: Procedural 3D Generation and Parametric Editing of 3D Shapes with Large Language Models
Fadlullah Raji, Stefano Petrangeli, Matheus Gadelha, Yu Shen, Uttaran Bhattacharya, Gang Wu
https://arxiv.org/abs/2601.12234 https://arxiv.org/pdf/2601.12234 https://arxiv.org/html/2601.12234
arXiv:2601.12234v1 Announce Type: new
Abstract: Generating 3D models has traditionally been a complex task requiring specialized expertise. While recent advances in generative AI have sought to automate this process, existing methods produce non-editable representation, such as meshes or point clouds, limiting their adaptability for iterative design. In this paper, we introduce Proc3D, a system designed to generate editable 3D models while enabling real-time modifications. At its core, Proc3D introduces procedural compact graph (PCG), a graph representation of 3D models, that encodes the algorithmic rules and structures necessary for generating the model. This representation exposes key parameters, allowing intuitive manual adjustments via sliders and checkboxes, as well as real-time, automated modifications through natural language prompts using Large Language Models (LLMs). We demonstrate Proc3D's capabilities using two generative approaches: GPT-4o with in-context learning (ICL) and a fine-tuned LLAMA-3 model. Experimental results show that Proc3D outperforms existing methods in editing efficiency, achieving more than 400x speedup over conventional approaches that require full regeneration for each modification. Additionally, Proc3D improves ULIP scores by 28%, a metric that evaluates the alignment between generated 3D models and text prompts. By enabling text-aligned 3D model generation along with precise, real-time parametric edits, Proc3D facilitates highly accurate text-based image editing applications.
toXiv_bot_toot

Cylinder type and $p$-divisible sets in $\mathbb{F}_p^3$
Gergely Kiss, \'Ad\'am Mark\'o, Zolt\'an L\'or\'ant Nagy, G\'abor Somlai
https://arxiv.org/abs/2601.09910

Chiral states induced by symmetry-breaking in $\alpha-T_3$ lattices: Magnetic field effect
J. P. G. Nascimento, J. M. Pereira Jr., R. N. Costa Filho, F. M. Peeters, M. M. Freire, W. P. Lima, D. R. da Costa
https://arxiv.org/abs/2602.10288

Happy to contribute to #Cilium (#documentation).
Good tools deserve good docs. ✨
https://github.com/cilium/cilium/pull/

docs: add policy language chapter headline by orangecms · Pull Request #44204 · cilium/cilium
Up until now, the page title had been 'Layer 3 Examples', which is a section headline and confusing, since it is among other examples. Please ensure your pull request adheres to the followi...

A note on Gurzadyan theorem
Christian Carimalo
https://arxiv.org/abs/2603.25323 https://arxiv.org/pdf/2603.25323 …

3/ In the last week or so he completely "rewrote" their accounts page, and then property marketplace, and lending site, and eliminated a few of the friction points that loftyassist solved.
I guess I should be sad that this happened but I am quite OK with it. This feels like the right overall direction. Their users now get a much better experience, pace of changes has sky rocketed and the product just overall is much more usable. Sure I lose a tiny revenue stream but it is wha…

$T^{-3}$-shift in a short-baseline atomic interferometer-gravimeter
D. N. Kapusta, A. E. Bonert, A. N. Goncharov, V. I. Yudin, K. N. Adamov, A. V. Taichenachev, M. Yu. Basalaev, M. D. Radchenko, O. N. Prudnikov
https://arxiv.org/abs/2603.21202

[2026-03-27 Fri (UTC), 1 new article found for physics.class-ph Classical Physics]
toXiv_bot_toot

Rethink Efficiency Side of Neural Combinatorial Solver: An Offline and Self-Play Paradigm
Zhenxing Xu, Zeyuan Ma, Weidong Bao, Hui Yan, Yan Zheng, Ji Wang
https://arxiv.org/abs/2602.20730 https://arxiv.org/pdf/2602.20730 https://arxiv.org/html/2602.20730
arXiv:2602.20730v1 Announce Type: new
Abstract: We propose ECO, a versatile learning paradigm that enables efficient offline self-play for Neural Combinatorial Optimization (NCO). ECO addresses key limitations in the field through: 1) Paradigm Shift: Moving beyond inefficient online paradigms, we introduce a two-phase offline paradigm consisting of supervised warm-up and iterative Direct Preference Optimization (DPO); 2) Architecture Shift: We deliberately design a Mamba-based architecture to further enhance the efficiency in the offline paradigm; and 3) Progressive Bootstrapping: To stabilize training, we employ a heuristic-based bootstrapping mechanism that ensures continuous policy improvement during training. Comparison results on TSP and CVRP highlight that ECO performs competitively with up-to-date baselines, with significant advantage on the efficiency side in terms of memory utilization and training throughput. We provide further in-depth analysis on the efficiency, throughput and memory usage of ECO. Ablation studies show rationale behind our designs.
toXiv_bot_toot

The 3-symmetric Pseudolinear Crossing Number of $K_{33}$
V\'ictor H. G\'omez Mart\'inez, C\'esar Hern\'andez-V\'elez, Jes\'us Lea\~nos
https://arxiv.org/abs/2601.09689

[2026-02-24 Tue (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

WeirNet: A Large-Scale 3D CFD Benchmark for Geometric Surrogate Modeling of Piano Key Weirs
Lisa L\"uddecke, Michael Hohmann, Sebastian Eilermann, Jan Tillmann-Mumm, Pezhman Pourabdollah, Mario Oertel, Oliver Niggemann
https://arxiv.org/abs/2602.20714 https://arxiv.org/pdf/2602.20714 https://arxiv.org/html/2602.20714
arXiv:2602.20714v1 Announce Type: new
Abstract: Reliable prediction of hydraulic performance is challenging for Piano Key Weir (PKW) design because discharge capacity depends on three-dimensional geometry and operating conditions. Surrogate models can accelerate hydraulic-structure design, but progress is limited by scarce large, well-documented datasets that jointly capture geometric variation, operating conditions, and functional performance. This study presents WeirNet, a large 3D CFD benchmark dataset for geometric surrogate modeling of PKWs. WeirNet contains 3,794 parametric, feasibility-constrained rectangular and trapezoidal PKW geometries, each scheduled at 19 discharge conditions using a consistent free-surface OpenFOAM workflow, resulting in 71,387 completed simulations that form the benchmark and with complete discharge coefficient labels. The dataset is released as multiple modalities compact parametric descriptors, watertight surface meshes and high-resolution point clouds together with standardized tasks and in-distribution and out-of-distribution splits. Representative surrogate families are benchmarked for discharge coefficient prediction. Tree-based regressors on parametric descriptors achieve the best overall accuracy, while point- and mesh-based models remain competitive and offer parameterization-agnostic inference. All surrogates evaluate in milliseconds per sample, providing orders-of-magnitude speedups over CFD runtimes. Out-of-distribution results identify geometry shift as the dominant failure mode compared to unseen discharge values, and data-efficiency experiments show diminishing returns beyond roughly 60% of the training data. By publicly releasing the dataset together with simulation setups and evaluation pipelines, WeirNet establishes a reproducible framework for data-driven hydraulic modeling and enables faster exploration of PKW designs during the early stages of hydraulic planning.
toXiv_bot_toot

Replaced article(s) found for physics.class-ph. https://arxiv.org/list/physics.class-ph/new
[1/1]:
- Representation-induced superposition breakdown in linear physics
Michael Mazilu, Andriejus Dem\v{c}enko

AgentCgroup: Understanding and Controlling OS Resources of AI Agents
Yusheng Zheng, Jiakun Fan, Quanzhi Fu, Yiwei Yang, Wei Zhang, Andi Quinn
https://arxiv.org/abs/2602.09345 https://arxiv.org/pdf/2602.09345 https://arxiv.org/html/2602.09345
arXiv:2602.09345v1 Announce Type: new
Abstract: AI agents are increasingly deployed in multi-tenant cloud environments, where they execute diverse tool calls within sandboxed containers, each call with distinct resource demands and rapid fluctuations. We present a systematic characterization of OS-level resource dynamics in sandboxed AI coding agents, analyzing 144 software engineering tasks from the SWE-rebench benchmark across two LLM models. Our measurements reveal that (1) OS-level execution (tool calls, container and agent initialization) accounts for 56-74% of end-to-end task latency; (2) memory, not CPU, is the concurrency bottleneck; (3) memory spikes are tool-call-driven with a up to 15.4x peak-to-average ratio; and (4) resource demands are highly unpredictable across tasks, runs, and models. Comparing these characteristics against serverless, microservice, and batch workloads, we identify three mismatches in existing resource controls: a granularity mismatch (container-level policies vs. tool-call-level dynamics), a responsiveness mismatch (user-space reaction vs. sub-second unpredictable bursts), and an adaptability mismatch (history-based prediction vs. non-deterministic stateful execution). We propose AgentCgroup , an eBPF-based resource controller that addresses these mismatches through hierarchical cgroup structures aligned with tool-call boundaries, in-kernel enforcement via sched_ext and memcg_bpf_ops, and runtime-adaptive policies driven by in-kernel monitoring. Preliminary evaluation demonstrates improved multi-tenant isolation and reduced resource waste.
toXiv_bot_toot

[2026-02-09 Mon (UTC), 3 new articles found for math.AC Commutative Algebra]
toXiv_bot_toot

A Faster Directed Single-Source Shortest Path Algorithm
Ran Duan, Xiao Mao, Xinkai Shu, Longhui Yin
https://arxiv.org/abs/2602.07868 https://arxiv.org/pdf/2602.07868 https://arxiv.org/html/2602.07868
arXiv:2602.07868v1 Announce Type: new
Abstract: This paper presents a new deterministic algorithm for single-source shortest paths (SSSP) on real non-negative edge-weighted directed graphs, with running time $O(m\sqrt{\log n} \sqrt{mn\log n\log \log n})$, which is $O(m\sqrt{\log n\log \log n})$ for sparse graphs. This improves the recent breakthrough result of $O(m\log^{2/3} n)$ time for directed SSSP algorithm [Duan, Mao, Mao, Shu, Yin 2025].
toXiv_bot_toot

Regret-Guided Search Control for Efficient Learning in AlphaZero
Yun-Jui Tsai, Wei-Yu Chen, Yan-Ru Ju, Yu-Hung Chang, Ti-Rong Wu
https://arxiv.org/abs/2602.20809 https://arxiv.org/pdf/2602.20809 https://arxiv.org/html/2602.20809
arXiv:2602.20809v1 Announce Type: new
Abstract: Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans. While RL agents require extensive self-play games to extract useful signals, humans often need only a few games, improving rapidly by repeatedly revisiting states where mistakes occurred. This idea, known as search control, aims to restart from valuable states rather than always from the initial state. In AlphaZero, prior work Go-Exploit applies this idea by sampling past states from self-play or search trees, but it treats all states equally, regardless of their learning potential. We propose Regret-Guided Search Control (RGSC), which extends AlphaZero with a regret network that learns to identify high-regret states, where the agent's evaluation diverges most from the actual outcome. These states are collected from both self-play trajectories and MCTS nodes, stored in a prioritized regret buffer, and reused as new starting positions. Across 9x9 Go, 10x10 Othello, and 11x11 Hex, RGSC outperforms AlphaZero and Go-Exploit by an average of 77 and 89 Elo, respectively. When training on a well-trained 9x9 Go model, RGSC further improves the win rate against KataGo from 69.3% to 78.2%, while both baselines show no improvement. These results demonstrate that RGSC provides an effective mechanism for search control, improving both efficiency and robustness of AlphaZero training. Our code is available at https://rlg.iis.sinica.edu.tw/papers/rgsc.
toXiv_bot_toot

[2026-02-23 Mon (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

Criterion for the Thermal Radiation Spectrum in Classical Physics
Timothy H. Boyer
https://arxiv.org/abs/2603.24406 https://arxiv.org/pdf/2603.24406…

[2026-03-26 Thu (UTC), 1 new article found for physics.class-ph Classical Physics]
toXiv_bot_toot

Fork, Explore, Commit: OS Primitives for Agentic Exploration
Cong Wang, Yusheng Zheng
https://arxiv.org/abs/2602.08199 https://arxiv.org/pdf/2602.08199 https://arxiv.org/html/2602.08199
arXiv:2602.08199v1 Announce Type: new
Abstract: AI agents increasingly perform agentic exploration: pursuing multiple solution paths in parallel and committing only the successful one. Because each exploration path may modify files and spawn processes, agents require isolated environments with atomic commit and rollback semantics for both filesystem state and process state. We introduce the branch context, a new OS abstraction that provides: (1) copy-on-write state isolation with independent filesystem views and process groups, (2) a structured lifecycle of fork, explore, and commit/abort, (3) first-commit-wins resolution that automatically invalidates sibling branches, and (4) nestable contexts for hierarchical exploration. We realize branch contexts in Linux through two complementary components. First, BranchFS is a FUSE-based filesystem that gives each branch context an isolated copy-on-write workspace, with O(1) creation, atomic commit to the parent, and automatic sibling invalidation, all without root privileges. BranchFS is open sourced in https://github.com/multikernel/branchfs. Second, branch() is a proposed Linux syscall that spawns processes into branch contexts with reliable termination, kernel-enforced sibling isolation, and first-commit-wins coordination. Preliminary evaluation of BranchFS shows sub-350 us branch creation independent of base filesystem size, and modification-proportional commit overhead (under 1 ms for small changes).
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Submodular Maximization over a Matroid $k$-Intersection: Multiplicative Improvement over Greedy
Moran Feldman, Justin Ward
https://arxiv.org/abs/2602.08473 https://arxiv.org/pdf/2602.08473 https://arxiv.org/html/2602.08473
arXiv:2602.08473v1 Announce Type: new
Abstract: We study the problem of maximizing a non-negative monotone submodular objective $f$ subject to the intersection of $k$ arbitrary matroid constraints. The natural greedy algorithm guarantees $(k 1)$-approximation for this problem, and the state-of-the-art algorithm only improves this approximation ratio to $k$. We give a $\frac{2k\ln2}{1 \ln2} O(\sqrt{k})<0.819k O(\sqrt{k})$ approximation for this problem. Our result is the first multiplicative improvement over the approximation ratio of the greedy algorithm for general $k$. We further show that our algorithm can be used to obtain roughly the same approximation ratio also for the more general problem in which the objective is not guaranteed to be monotone (the sublinear term in the approximation ratio becomes $O(k^{2/3})$ rather than $O(\sqrt{k})$ in this case).
All of our results hold also when the $k$-matroid intersection constraint is replaced with a more general matroid $k$-parity constraint. Furthermore, unlike the case in many of the previous works, our algorithms run in time that is independent of $k$ and polynomial in the size of the ground set. Our algorithms are based on a hybrid greedy local search approach recently introduced by Singer and Thiery (STOC 2025) for the weighted matroid $k$-intersection problem, which is a special case of the problem we consider. Leveraging their approach in the submodular setting requires several non-trivial insights and algorithmic modifications since the marginals of a submodular function $f$, which correspond to the weights in the weighted case, are not independent of the algorithm's internal randomness. In the special weighted case studied by Singer and Thiery, our algorithms reduce to a variant of their algorithm with an improved approximation ratio of $k\ln2 1-\ln2<0.694k 0.307$, compared to an approximation ratio of $\frac{k 1}{2\ln2}\approx0.722k 0.722$ guaranteed by Singer and Thiery.
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Replaced article(s) found for physics.class-ph. https://arxiv.org/list/physics.class-ph/new
[1/1]:
- Parametric Design of a Cable-Driven Coaxial Spherical Parallel Mechanism for Ultrasound Scans
Maryam Seraj, Mohammad Hossein Kamrava, Carlo Tiseo

[2026-03-25 Wed (UTC), no new articles found for physics.class-ph Classical Physics]
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Online Algorithm for Fractional Matchings with Edge Arrivals in Graphs of Maximum Degree Three
Kanstantsin Pashkovich, Thomas Snow
https://arxiv.org/abs/2602.07355 https://arxiv.org/pdf/2602.07355 https://arxiv.org/html/2602.07355
arXiv:2602.07355v1 Announce Type: new
Abstract: We study online algorithms for maximum cardinality matchings with edge arrivals in graphs of low degree. Buchbinder, Segev, and Tkach showed that no online algorithm for maximum cardinality fractional matchings can achieve a competitive ratio larger than $4/(9-\sqrt 5)\approx 0.5914$ even for graphs of maximum degree three. The negative result of Buchbinder et al. holds even when the graph is bipartite and edges are revealed according to vertex arrivals, i.e. once a vertex arrives, all edges are revealed that include the newly arrived vertex and one of the previously arrived vertices. In this work, we complement the negative result of Buchbinder et al. by providing an online algorithm for maximum cardinality fractional matchings with a competitive ratio at least $4/(9-\sqrt 5)\approx 0.5914$ for graphs of maximum degree three. We also demonstrate that no online algorithm for maximum cardinality integral matchings can have the competitive guarantee $0.5807$, establishing a gap between integral and fractional matchings for graphs of maximum degree three. Note that the work of Buchbinder et al. shows that for graphs of maximum degree two, there is no such gap between fractional and integral matchings, because for both of them the best achievable competitive ratio is $2/3$. Also, our results demonstrate that for graphs of maximum degree three best possible competitive ratios for fractional matchings are the same in the vertex arrival and in the edge arrival models.
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Replaced article(s) found for physics.class-ph. https://arxiv.org/list/physics.class-ph/new
[1/1]:
- Comment on the "Electric Power Generation from Earth's Rotation through its Own Magnetic Field"
Iver H. Brevik, Moshe M. Chaichian, Mikhail I. Katsnelson

Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Spin-orbital entanglement in Cr$^{3 }$-doped glasses
J. S. Robles-P\'aez, A. T. Sarre\~no-Santos, V. Garc\'ia-Rojas, J. F. P\'erez-Torres

Crosslisted article(s) found for physics.class-ph. https://arxiv.org/list/physics.class-ph/new
[1/1]:
- Diffraction of deep-water solitons
Novkoski, Fache, Bonnefoy, Ducrozet, Barckicke, Copie, Suret, Falcon, Randoux

Towards Efficient Data Structures for Approximate Search with Range Queries
Ladan Kian, Dariusz R. Kowalski
https://arxiv.org/abs/2602.06860 https://arxiv.org/pdf/2602.06860 https://arxiv.org/html/2602.06860
arXiv:2602.06860v1 Announce Type: new
Abstract: Range queries are simple and popular types of queries used in data retrieval. However, extracting exact and complete information using range queries is costly. As a remedy, some previous work proposed a faster principle, {\em approximate} search with range queries, also called single range cover (SRC) search. It can, however, produce some false positives. In this work we introduce a new SRC search structure, a $c$-DAG (Directed Acyclic Graph), which provably decreases the average number of false positives by logarithmic factor while keeping asymptotically same time and memory complexities as a classic tree structure. A $c$-DAG is a tunable augmentation of the 1D-Tree with denser overlapping branches ($c \geq 3$ children per node). We perform a competitive analysis of a $c$-DAG with respect to 1D-Tree and derive an additive constant time overhead and a multiplicative logarithmic improvement of the false positives ratio, on average. We also provide a generic framework to extend our results to empirical distributions of queries, and demonstrate its effectiveness for Gowalla dataset. Finally, we quantify and discuss security and privacy aspects of SRC search on $c$-DAG vs 1D-Tree, mainly mitigation of structural leakage, which makes $c$-DAG a good data structure candidate for deployment in privacy-preserving systems (e.g., searchable encryption) and multimedia retrieval.
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Contractions of the relativistic quantum LCT group and the emergence of spacetime symmetries
Anjary Feno Hasina Rasamimanana, Ravo Tokiniaina Ranaivoson, Roland Raboanary, Raoelina Andriambololona, Wilfrid Chrysante Solofoarisina, Philippe Manjakasoa Randriantsoa
https://arxiv.org/abs/2603.21333

[2026-03-24 Tue (UTC), 1 new article found for physics.class-ph Classical Physics]
toXiv_bot_toot

[2026-02-13 Fri (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

Crosslisted article(s) found for physics.class-ph. https://arxiv.org/list/physics.class-ph/new
[1/1]:
- Macroscopic Mpemba Effect from Cumulative-Heat-Enhanced Relaxation
Yun-Qian Lin, Z. C. Tu, Yu-Han Ma

[2026-03-13 Fri (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
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[2026-03-23 Mon (UTC), no new articles found for physics.class-ph Classical Physics]
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[2026-01-12 Mon (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

[2026-02-12 Thu (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

Replaced article(s) found for physics.class-ph. https://arxiv.org/list/physics.class-ph/new
[1/1]:
- Mechanically concealed holes
Kanka Ghosh, Andreas M. Menzel

Crosslisted article(s) found for physics.class-ph. https://arxiv.org/list/physics.class-ph/new
[1/1]:
- Geometric Dynamics of Turbulence
Alejandro Sevilla

[2026-01-09 Fri (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

[2026-03-20 Fri (UTC), no new articles found for physics.class-ph Classical Physics]
toXiv_bot_toot

Features of the van der Waals Interaction on the Cesium $6S_{1/2} \rightarrow 7P_{3/2}$ Transition in an Optical Nanocell
Armen Sargsyan, Anahit Gogyan, David Sarkisyan
https://arxiv.org/abs/2601.04661

Electron capture induced fragmentation of CO$_2^{3 }$: Influence of projectile charge on sequential and concerted break-up pathways
Akash Srivastav, Sumit Srivastav, Bhas Bapat
https://arxiv.org/abs/2601.03711

[2026-01-06 Tue (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
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