Tootfinder

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/6]:
- Performance Asymmetry in Model-Based Reinforcement Learning
Jing Yu Lim, Rushi Shah, Zarif Ikram, Samson Yu, Haozhe Ma, Tze-Yun Leong, Dianbo Liu
https://arxiv.org/abs/2505.19698 https://mastoxiv.page/@arXiv_csLG_bot/114578810521008766
- Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependenc...
Jinkwan Jang, Hyungjin Park, Jinmyeong Choi, Taesup Kim
https://arxiv.org/abs/2506.08660 https://mastoxiv.page/@arXiv_csLG_bot/114664238967892509
- Wasserstein Barycenter Soft Actor-Critic
Zahra Shahrooei, Ali Baheri
https://arxiv.org/abs/2506.10167 https://mastoxiv.page/@arXiv_csLG_bot/114675175949432731
- Foundation Models for Causal Inference via Prior-Data Fitted Networks
Yuchen Ma, Dennis Frauen, Emil Javurek, Stefan Feuerriegel
https://arxiv.org/abs/2506.10914 https://mastoxiv.page/@arXiv_csLG_bot/114675529854402158
- FREQuency ATTribution: benchmarking frequency-based occlusion for time series data
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
https://arxiv.org/abs/2506.18481 https://mastoxiv.page/@arXiv_csLG_bot/114738421450807709
- Complexity-aware fine-tuning
Andrey Goncharov, Daniil Vyazhev, Petr Sychev, Edvard Khalafyan, Alexey Zaytsev
https://arxiv.org/abs/2506.21220 https://mastoxiv.page/@arXiv_csLG_bot/114754764750730849
- Transfer Learning in Infinite Width Feature Learning Networks
Clarissa Lauditi, Blake Bordelon, Cengiz Pehlevan
https://arxiv.org/abs/2507.04448 https://mastoxiv.page/@arXiv_csLG_bot/114818005803079705
- A hierarchy tree data structure for behavior-based user segment representation
Liu, Kang, Iyer, Malik, Li, Wang, Lu, Zhao, Wang, Liu, Liu, Liang, Yu
https://arxiv.org/abs/2508.01115 https://mastoxiv.page/@arXiv_csLG_bot/114975999992144374
- One-Step Flow Q-Learning: Addressing the Diffusion Policy Bottleneck in Offline Reinforcement Lea...
Thanh Nguyen, Chang D. Yoo
https://arxiv.org/abs/2508.13904 https://mastoxiv.page/@arXiv_csLG_bot/115060568241390847
- Uncertainty Propagation Networks for Neural Ordinary Differential Equations
Hadi Jahanshahi, Zheng H. Zhu
https://arxiv.org/abs/2508.16815 https://mastoxiv.page/@arXiv_csLG_bot/115094785677272005
- Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling
Zhengdong Huang, Zicheng Xie, Wentao Tian, Jingyu Liu, Lunhong Dong, Peng Yang
https://arxiv.org/abs/2508.21785 https://mastoxiv.page/@arXiv_csLG_bot/115128450608548173
- Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
Liu, Cao, Jiang, Luo, Duan, Wang, Sosnick, Xu, Stevens
https://arxiv.org/abs/2509.15796 https://mastoxiv.page/@arXiv_csLG_bot/115247429156900905
- From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
Xilin Dai, Zhijian Xu, Wanxu Cai, Qiang Xu
https://arxiv.org/abs/2509.19975 https://mastoxiv.page/@arXiv_csLG_bot/115264498084813952
- Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs
Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Masahiro Ikeda
https://arxiv.org/abs/2509.21895 https://mastoxiv.page/@arXiv_csLG_bot/115287261047939306
- From Parameters to Behaviors: Unsupervised Compression of the Policy Space
Davide Tenedini, Riccardo Zamboni, Mirco Mutti, Marcello Restelli
https://arxiv.org/abs/2509.22566 https://mastoxiv.page/@arXiv_csLG_bot/115287379672141023
- RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility
Haoyu He, Haozheng Luo, Yan Chen, Qi R. Wang
https://arxiv.org/abs/2509.23115 https://mastoxiv.page/@arXiv_csLG_bot/115293273559547106
- Polychromic Objectives for Reinforcement Learning
Jubayer Ibn Hamid, Ifdita Hasan Orney, Ellen Xu, Chelsea Finn, Dorsa Sadigh
https://arxiv.org/abs/2509.25424 https://mastoxiv.page/@arXiv_csLG_bot/115298579764580635
- Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman, et al.
https://arxiv.org/abs/2509.26626 https://mastoxiv.page/@arXiv_csLG_bot/115298789487177431
- Cautious Weight Decay
Chen, Li, Liang, Su, Xie, Pierse, Liang, Lao, Liu
https://arxiv.org/abs/2510.12402 https://mastoxiv.page/@arXiv_csLG_bot/115377759317818093
- TeamFormer: Shallow Parallel Transformers with Progressive Approximation
Wei Wang, Xiao-Yong Wei, Qing Li
https://arxiv.org/abs/2510.15425 https://mastoxiv.page/@arXiv_csLG_bot/115405933861293858
- Latent-Augmented Discrete Diffusion Models
Dario Shariatian, Alain Durmus, Umut Simsekli, Stefano Peluchetti
https://arxiv.org/abs/2510.18114 https://mastoxiv.page/@arXiv_csLG_bot/115417332500265972
- Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Method...
Mary E. An, Paul Griffin, Jonathan G. Stine, Ramakrishna Balakrishnan, Soundar Kumara
https://arxiv.org/abs/2510.22293 https://mastoxiv.page/@arXiv_csLG_bot/115451746201804373
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[2026-03-31 Tue (UTC), 4 new articles found for cs.CC Computational Complexity]
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Ordering Power is Sanctioning Power: Sanction Evasion-MEV and the Limits of On-Chain Enforcement
Di Wu, Yuman Bai, Shoupeng Ren, Xinyu Zhang, Yiyue Cao, Xuechao Wang, Wu Wen, Jian Liu
https://arxiv.org/abs/2603.27739

https://github.com/fedidb/fedidb-nuxt/issues/36
JavaScript was a mistake.

Time is Not Compute: Scaling Laws for Wall-Clock Constrained Training on Consumer GPUs
Yi Liu
https://arxiv.org/abs/2603.28823 https://arxiv.org/pdf/2603.28823 https://arxiv.org/html/2603.28823
arXiv:2603.28823v1 Announce Type: new
Abstract: Scaling laws relate model quality to compute budget (FLOPs), but practitioners face wall-clock time constraints, not compute budgets. We study optimal model sizing under fixed time budgets from 5 minutes to 24 hours on consumer GPUs (RTX 4090). Across 70 runs spanning 50M--1031M parameters, we find: (1)~at each time budget a U-shaped curve emerges where too-small models overfit and too-large models undertrain; (2)~optimal model size follows $N^* \propto t^{0.60}$, growing \emph{faster} than Chinchilla's $N^* \propto C^{0.50}$, with $\alpha = 0.60 \pm 0.07$ robustly exceeding compute-optimal across all sensitivity analyses; (3)~a \emph{dual U-shape mechanism}: short-budget U-curves arise from compute bottlenecks, while long-budget U-curves emerge from data bottlenecks (overfitting), with an intermediate regime where the U-curve temporarily disappears. These findings have immediate implications for researchers training on consumer hardware, where wall-clock time -- not FLOPs -- is the binding constraint. We release all code, logs, and 70 experimental configurations.
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Near-Optimal Bounds for Parameterized Euclidean k-means
Vincent Cohen-Addad, Karthik C. S., David Saulpic, Chris Schwiegelshohn
https://arxiv.org/abs/2603.28268 https://

[2026-03-31 Tue (UTC), 4 new articles found for cond-mat.quant-gas Quantum Gases]
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edit_wikinews: Wikipedia news edits (2010)
Two bipartite user-page networks extracted from Wikipedia, about news events. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 2511 nodes and 7745 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

[2026-03-31 Tue (UTC), 3 new articles found for math.CT Category Theory]
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Gradualist descriptionalist set theory
David Simmons
https://arxiv.org/abs/2603.27077 https://arxiv.org/pdf/2603.27077 https://arxiv.org/html/2603.27077
arXiv:2603.27077v1 Announce Type: new
Abstract: We introduce a formal language GDST (gradualist descriptionalist set theory) with a family of interpretations indexed by ordinals, as well as a sublanguage NMID (the language of not necessarily monotonic inductive definitions), and show that the assertion that all propositions in NMID have well-defined truth values is equivalent to the existence for each $k \in \mathbb N$ of a sequence of ordinals $\eta_0 < . . . < \eta_k$ such that for each $i < k$, $\eta_i$ is $\eta_{i 1}$-reflecting, a notion we introduce which implies being $\Pi_n$-reflecting for all $n \in \mathbb N$ (and in particular being admissible and recursively Mahlo).
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Legendrian and Lagrangian higher torsion
Daniel Alvarez Gavela, Kiyoshi Igusa, Michael Sullivan
https://arxiv.org/abs/2603.28007 https://arxiv.org/pdf/2603.28007 https://arxiv.org/html/2603.28007
arXiv:2603.28007v1 Announce Type: new
Abstract: Let $M$ be a closed manifold. We introduce a family of Legendrian isotopy invariants for Legendrians in $J^1M$, which we collectively call Legendrian higher torsion. Given a choice of a class $\mathcal{F}$ of fibre bundles over $M$, equipped with suitable unitary local systems, the Legendrian higher torsion of a Legendrian $\Lambda \subset J^1M$ is the subset of $H^*(M;\mathbf{R})$ consisting of higher Reidemeister torsion cohomology classes of fibre bundles $W$ over $M$ in the class $\mathcal{F}$ such that $\Lambda$ admits a generating function on a stabilization of $W$. For the class of tube bundles in the sense of Waldhausen we call the invariant tube torsion. In particular, we show that the tube torsion of a nearby Lagrangian $L \subset T^*M$ is well-defined when the stable Gauss map $L \to U/O$ is trivial and consists of a union of cosets of a normalized version of the Pontryagin character. We also identify a distinguished coset, invariant under Hamiltonian isotopy of $L$, which we call nearby Lagrangian torsion. We do not know whether nearby Lagrangians must have trivial tube torsion, as would follow from the nearby Lagrangian conjecture. However, we show that there exist Legendrians $\Lambda \subset J^1M$ with nontrivial tube torsion whose projection $\Lambda \to M$ is homotopic to a diffeomorphism.
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Compressing Transformer Language Models via Matrix Product Operator Decomposition: A Case Study on PicoGPT
Younes Javanmard, Tanmoy Pandit, Masoud Mardani
https://arxiv.org/abs/2603.28534 https://arxiv.org/pdf/2603.28534 https://arxiv.org/html/2603.28534
arXiv:2603.28534v1 Announce Type: new
Abstract: Transformer-based language models achieve strong performance across NLP tasks, but their quadratic parameter scaling with hidden dimension makes deployment on resource-constrained hardware expensive. We study Matrix Product Operator (MPO) decomposition as a principled compression method for transformers. MPO factorises weight matrices into chains of low-rank cores, with approximation quality controlled by the bond dimension chi. We replace every nn.Linear layer in PicoGPT, a GPT-2-style character-level language model with about 1M parameters, with an MPOLinear module parameterised as an MPO chain. Cores are initialised either by TT-SVD from pretrained dense weights or from random initialisation, and trained using standard PyTorch autograd without a custom backward pass. We derive balanced factorisation schemes for the five distinct weight shapes in PicoGPT and evaluate bond dimensions chi in {4, 8, 16, 32} on Tiny Shakespeare. MPO compression achieves up to 13x compression per transformer block at chi = 4. At chi = 16, the model uses 191,872 parameters instead of 1,020,224 while retaining 97.7% of baseline token accuracy (51.6% vs 52.8%). Reconstruction error follows the expected trend and is lower for three-site than two-site factorisations at the same bond dimension. The chi = 8 model gives the best accuracy per parameter, exceeding the dense baseline by 2.7x on this metric. These results show that MPO parameterisation is a practical and theoretically grounded alternative to low-rank methods and unstructured pruning for transformer compression.
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Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/6]:
- Towards Attributions of Input Variables in a Coalition
Xinhao Zheng, Huiqi Deng, Quanshi Zhang
https://arxiv.org/abs/2309.13411
- Knee or ROC
Veronica Wendt, Jacob Steiner, Byunggu Yu, Caleb Kelly, Justin Kim
https://arxiv.org/abs/2401.07390
- Rethinking Disentanglement under Dependent Factors of Variation
Antonio Almud\'evar, Alfonso Ortega
https://arxiv.org/abs/2408.07016 https://mastoxiv.page/@arXiv_csLG_bot/112959235461894530
- Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching
Etrit Haxholli, Yeti Z. Gurbuz, Ogul Can, Eli Waxman
https://arxiv.org/abs/2411.00759 https://mastoxiv.page/@arXiv_csLG_bot/113423933393275133
- Predicting Subway Passenger Flows under Incident Situation with Causality
Xiannan Huang, Shuhan Qiu, Quan Yuan, Chao Yang
https://arxiv.org/abs/2412.06871 https://mastoxiv.page/@arXiv_csLG_bot/113632934357523592
- Characterizing LLM Inference Energy-Performance Tradeoffs across Workloads and GPU Scaling
Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic
https://arxiv.org/abs/2501.08219 https://mastoxiv.page/@arXiv_csLG_bot/113831081884570770
- Universality of Benign Overfitting in Binary Linear Classification
Ichiro Hashimoto, Stanislav Volgushev, Piotr Zwiernik
https://arxiv.org/abs/2501.10538 https://mastoxiv.page/@arXiv_csLG_bot/113872351652969955
- Safe Reinforcement Learning for Real-World Engine Control
Julian Bedei, Lucas Koch, Kevin Badalian, Alexander Winkler, Patrick Schaber, Jakob Andert
https://arxiv.org/abs/2501.16613 https://mastoxiv.page/@arXiv_csLG_bot/113910356206562660
- A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers
Roman Tarasov, Petr Mokrov, Milena Gazdieva, Evgeny Burnaev, Alexander Korotin
https://arxiv.org/abs/2502.01310
- Improving the Convergence of Private Shuffled Gradient Methods with Public Data
Shuli Jiang, Pranay Sharma, Zhiwei Steven Wu, Gauri Joshi
https://arxiv.org/abs/2502.03652 https://mastoxiv.page/@arXiv_csLG_bot/113961314098841096
- Using the Path of Least Resistance to Explain Deep Networks
Sina Salek, Joseph Enguehard
https://arxiv.org/abs/2502.12108 https://mastoxiv.page/@arXiv_csLG_bot/114023706252106865
- Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence
Wenzhe Yin, Zehao Xiao, Pan Zhou, Shujian Yu, Jiayi Shen, Jan-Jakob Sonke, Efstratios Gavves
https://arxiv.org/abs/2502.17028 https://mastoxiv.page/@arXiv_csLG_bot/114063477202397951
- Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster
Sharan Vaswani, Reza Babanezhad
https://arxiv.org/abs/2503.00229 https://mastoxiv.page/@arXiv_csLG_bot/114103018985567633
- Semantic Parallelism: Redefining Efficient MoE Inference via Model-Data Co-Scheduling
Yan Li, Zhenyu Zhang, Zhengang Wang, Pengfei Chen, Pengfei Zheng
https://arxiv.org/abs/2503.04398 https://mastoxiv.page/@arXiv_csLG_bot/114120014622063602
- A Survey on Federated Fine-tuning of Large Language Models
Wu, Tian, Li, Sun, Tam, Zhou, Liao, Xiong, Guo, Li, Xu
https://arxiv.org/abs/2503.12016 https://mastoxiv.page/@arXiv_csLG_bot/114182234054681647
- Towards Trustworthy GUI Agents: A Survey
Yucheng Shi, Wenhao Yu, Jingyuan Huang, Wenlin Yao, Wenhu Chen, Ninghao Liu
https://arxiv.org/abs/2503.23434 https://mastoxiv.page/@arXiv_csLG_bot/114263024618476521
- CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee
Chao Yang, Xiannan Huang, Shuhan Qiu, Yan Cheng
https://arxiv.org/abs/2504.13961 https://mastoxiv.page/@arXiv_csLG_bot/114380404041503229
- Regularity and Stability Properties of Selective SSMs with Discontinuous Gating
Nikola Zubi\'c, Davide Scaramuzza
https://arxiv.org/abs/2505.11602 https://mastoxiv.page/@arXiv_csLG_bot/114538965060456498
- RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization
Alonso Urbano, David W. Romero, Max Zimmer, Sebastian Pokutta
https://arxiv.org/abs/2505.13289 https://mastoxiv.page/@arXiv_csLG_bot/114539124884913788
- RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
Yilang Zhang, Bingcong Li, Georgios B. Giannakis
https://arxiv.org/abs/2505.18877 https://mastoxiv.page/@arXiv_csLG_bot/114578778213033886
- SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data
Bechler-Speicher, Zerio, Huri, Vestergaard, Gilad-Bachrach, Jess, Bhatt, Sazonovs
https://arxiv.org/abs/2505.19193 https://mastoxiv.page/@arXiv_csLG_bot/114578790124778172
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[2026-01-01 Thu (UTC), 5 new articles found for physics.atom-ph Atomic Physics]
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There's laws, there's permits, and there's Amsterdam Schiphol Airport. Outgoing double-failed Dutch government ignores judge, gives Schiphol another two years to operate without nature permit, and allows it even higher nitrogen emissions.
Conveniently arranged before the new coalition starts. In @… today.

[2026-03-30 Mon (UTC), no new articles found for nlin.AO Adaptation and Self-Organizing Systems]
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[2026-03-30 Mon (UTC), 7 new articles found for physics.chem-ph Chemical Physics]
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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
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On the Complexity of Determinations
Joseph M. Hellerstein
https://arxiv.org/abs/2603.28031 https://arxiv.org/pdf/2603.28031

Lots of Shade on Satellite Constellations
Michael B. Lund
https://arxiv.org/abs/2603.29212 https://arxiv.org/pdf/2603.29212

So for many folks - cancelling their Hilton Honors membership was a way to let the chain know that their handling of the situation in Minneapolis was unacceptable.
Nice techdirt article and template for those perhaps looking to do the same https://www.techdirt.com/2026/01/07/dear-h

Fine-Grained Complexity of Continuous Euclidean k-Center
Lotte Blank, Karl Bringmann, Parinya Chalermsook, Karthik C. S., Benedikt Kolbe, Hung Le, Geert van Wordragen
https://arxiv.org/abs/2603.28265

[2026-04-01 Wed (UTC), 3 new articles found for cs.PF Performance]
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[2026-03-31 Tue (UTC), 11 new articles found for math.LO Logic]
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[2026-03-31 Tue (UTC), 1 new article found for math.SG Symplectic Geometry]
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[2026-01-30 Fri (UTC), no new articles found for math.AC Commutative Algebra]
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On the first eigenvalue of the area Jacobi operator for complex curves in K\"ahler surfaces
Zhenxiao Xie
https://arxiv.org/abs/2602.22744 https://arxiv.org/pdf/2602.22744 https://arxiv.org/html/2602.22744
arXiv:2602.22744v1 Announce Type: new
Abstract: In this paper, we investigate the first eigenvalue $\Lambda_1$ of the area Jacobi operator for complex curves in K\"ahler surfaces, establishing an extrinsic counterpart to the classical Lichnerowicz theorem for the Laplace-Beltrami operator. By analyzing the second variation of a conformally invariant Willmore-type functional, we derive the lower bound $\Lambda_1 \geq 2\,\mathfrak{Ric}$, where $\mathfrak{Ric}$ denotes the infimum of the ambient Ricci curvature. For K\"ahler-Einstein surfaces with positive Einstein constant $\mathfrak{c}>0$, this bound reduces to $\Lambda_1 \geq 2\mathfrak{c}$. We then explore the equality case, computing the exact dimension of the corresponding first eigenspace in terms of the area, genus, and the dimension of a space of holomorphic sections. This analysis shows that the equality is achieved for all curves of genus $g \leq 1$.
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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.
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[2026-02-27 Fri (UTC), no new articles found for math.GN General Topology]
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Possibly one of my least favourite tasks as an academic: getting a paper back to the page limit after adding the ACM guff and removing the space hacks 😐

Crosslisted article(s) found for q-bio.PE. https://arxiv.org/list/q-bio.PE/new
[1/1]:
- Braess's paradox in tandem-running ants: When shortest path is not the quickest
Joy Das Bairagya, Udipta Chakraborti, Sumana Annagiri, Sagar Chakraborty
https://arxiv.org/abs/2603.26226 https://mastoxiv.page/@arXiv_physicsbioph_bot/116317046208603894
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Alan Page after false claim he attended ICE protest: 'Why would somebody make that up?' https://www.nytimes.com/athletic/6997720/2026/01/25/alan-page-ice-protests-minneapolis-social-media/

Crosslisted article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- The Image of Functor Morphing
Ehud Meir
https://arxiv.org/abs/2603.26368 https://mastoxiv.page/@arXiv_mathRT_bot/116317084548940951
- The motivic tt-geometry of real quadrics
Jean Paul Schemeil
https://arxiv.org/abs/2603.26492 https://mastoxiv.page/@arXiv_mathAG_bot/116317122890328019
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Automated Reencoding Meets Graph Theory
Benjamin Przybocki, Bernardo Subercaseaux, Marijn J. H. Heule
https://arxiv.org/abs/2603.27774 https://arxiv.org/pd…

Quality of Descriptive Information on Cultural Heritage Objects: Definition and Empirical Evaluation
Markus Matoni, Arno Kesper, Gabriele Taentzer
https://arxiv.org/abs/2602.21249

Resonant two-cluster scattering in a quasi-one-dimensional Bose gas
Tomohiro Tanaka, Yusuke Nishida
https://arxiv.org/abs/2603.28250 https://arxiv.org/pdf/…

A Polynomial Coreset for Furthest Neighbor in Planar Metrics
Kacper Kluk, Hung Le, Wojciech Nadara, Marcin Pilipczuk, Hector Tierno, Vinayak
https://arxiv.org/abs/2603.28100 ht…

[2026-02-26 Thu (UTC), no new articles found for nlin.PS Pattern Formation and Solitons]
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SysOM-AI: Continuous Cross-Layer Performance Diagnosis for Production AI Training
Yusheng Zheng, Wenan Mao, Shuyi Cheng, Fuqiu Feng, Guangshui Li, Zhaoyan Liao, Yongzhuo Huang, Zhenwei Xiao, Yuqing Li, Andi Quinn, Tao Ma
https://arxiv.org/abs/2603.29235 https://arxiv.org/pdf/2603.29235 https://arxiv.org/html/2603.29235
arXiv:2603.29235v1 Announce Type: new
Abstract: Performance diagnosis in production-scale AI training is challenging because subtle OS-level issues can trigger cascading GPU delays and network slowdowns, degrading training efficiency across thousands of GPUs. Existing profiling tools are limited to single system layers, incur prohibitive overhead (10--30%), or lack continuous deployment capabilities, resulting in manual analyses spanning days. We argue that continuous, cross-layer observability enabled by OS-level instrumentation and layered differential diagnosis is necessary to address this gap. We introduce SysOM-AI, a production observability system that continuously integrates CPU stack profiling, GPU kernel tracing, and NCCL event instrumentation via adaptive hybrid stack unwinding and eBPF-based tracing, incurring less than 0.4% overhead. Deployed at Alibaba across over 80,000 GPUs for more than one year, SysOM-AI helped diagnose 94 confirmed production issues, reducing median diagnosis time from days to approximately 10 minutes.
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Hindman and Owings-like theorems without the Axiom of Choice
Jos\'e A. Guzm\'an-Vega, David J. Fern\'andez Bret\'on, Eliseo Sarmiento Rosales
https://arxiv.org/abs/2603.27163 https://arxiv.org/pdf/2603.27163 https://arxiv.org/html/2603.27163
arXiv:2603.27163v1 Announce Type: new
Abstract: We investigate Hindman- and Owings-type Ramsey-theoretic statements in Zermelo-Fraenkel set theory without the Axiom of Choice, with some occasional extra assumptions (such as the Axiom of Dependent Choice and/or the Axiom of Determinacy). We study several variations of Hindman's theorem on $\mathbb Q$-vector spaces; notably, we show that the uncountable analog of Hindman's theorem fails for the additive group of $\mathbb R$ (under ZF), and for $\mathbb Q$-vector spaces of uncountable dimension (under DC if such dimension is not well-orderable), among other results. In contrast, for Owings-type configurations, we obtain several positive results, especially when assuming AD. These results highlight the interaction between determinacy, algebraic structure, and dimension in the study of infinite Ramsey theory without the Axiom of Choice.
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I just had to … ¯\_(ツ)_/¯
#shimmyshimmyya

Random tensor isomorphism under orthogonal and unitary actions
Jeremy Chizewer, Samuel Everett, Deven Mithal, Youming Qiao
https://arxiv.org/abs/2603.27128 https://

[2026-04-01 Wed (UTC), 1 new article found for physics.pop-ph Popular Physics]
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edit_wikiquote: Wikiquote edits (2010)
A bipartite user-page network extracted from Wikiquotes. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 4414 nodes and 11668 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

Proximity Alert: Ipelets for Neighborhood Graphs and Clustering
Gitan Balogh, June Cagan, Bea Fatima, Auguste H. Gezalyan, Danesh Sivakumar, Arushi Srinivasan, Yixuan Sun, Vahe Zaprosyan, David M. Mount
https://arxiv.org/abs/2603.27023

Moir\'e and frustration physics of dipolar supersolids under periodic confinement
Ze-Hong Guo, Kai Gan, and Qizhong Zhu
https://arxiv.org/abs/2603.27983 https://

A Boothby-Wang construction in generalized contact geometry
Debjit Pal
https://arxiv.org/abs/2602.22385 https://arxiv.org/pdf/2602.22385 https://arxiv.org/html/2602.22385
arXiv:2602.22385v1 Announce Type: new
Abstract: We establish a generalized analogue of the Boothby-Wang theorem in generalized contact geometry, along with related results. We present a general method for constructing examples of generalized contact structures that are not of Poon-Wade type, and even examples that fail to be generalized contact structures. Using Courant reduction methods, we construct a generalized complex structure on a smooth leaf space and equip the generalized contact manifold with a principal bundle structure whose connection is defined by the generalized contact data. Under mild assumptions, we show that the curvature induces a symplectic foliation on the leaf space. Several examples are provided.
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The Ice Sheet State and Parameter Estimator (ICESEE) Library (v1.0.0): Ensemble Kalman Filtering for Ice Sheet Models
Brian Kyanjo, Talea L. Mayo, Alexander A. Robel
https://arxiv.org/abs/2603.26947

Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Painted loading: a toolkit for loading spatially large optical tweezer arrays
Walker, Moriya, Segal, Gallagher, Hill, Leroux, Xu, Jones

Visualizing Higher Order Structures, Overlap Regions, and Clustering in the Hilbert Geometry
Hridhaan Banerjee, Soren Brown, June Cagan, Auguste H. Gezalyan, Megan Hunleth, Veena Kailad, Chaewoon Kyoung, Rowan Shigeno, Yasmine Tajeddin, Andrew Wagger, Kelin Zhu, David M. Moun
https://arxiv.org/abs/2603.27009

BuffCut: Prioritized Buffered Streaming Graph Partitioning
Linus Baumg\"artner, Adil Chhabra, Marcelo Fonseca Faraj, Christian Schulz
https://arxiv.org/abs/2602.21248 https…

Neural Approximation of Generalized Voronoi Diagrams
Panagiotis Rigas, George Ioannakis, Ioannis Emiris
https://arxiv.org/abs/2603.26964 https://arxiv.org/…

[2026-02-27 Fri (UTC), 4 new articles found for math.DG Differential Geometry]
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[2026-03-30 Mon (UTC), no new articles found for math.CT Category Theory]
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[2026-03-30 Mon (UTC), no new articles found for math.SG Symplectic Geometry]
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A One-Step Cascade Symmetric Model: Rank-$1$ Packets, Binary Shielding, and the Even Exact-Cardinality Profile
Frank Gilson
https://arxiv.org/abs/2603.25950 https://arxiv.org/pdf/2603.25950 https://arxiv.org/html/2603.25950
arXiv:2603.25950v1 Announce Type: new
Abstract: We introduce a one-step cascade symmetric system whose local symmetry geometry is organized by finite $\rho$-closed windows and one-step stars rather than by rowwise-independent toggles. The resulting symmetric model isolates a new $ZF DC \neg \mathrm{BPI}$ geometry in which rank-$1$ hereditarily symmetric reals admit a packet normalization theorem over countable $\rho$-closed supports.
The technical center of the paper is the finite star-span lemma and the associated rank-$1$ packet calculus. From this we obtain a normalization theorem and a two-layer coding consequence for rank-$1$ reals (in the metatheory, via a well-orderable base of packets). We then apply the same binary fresh-support shielding pattern to prove $\neg C_2$, hence $\neg AC_{\mathrm{fin}}$, and therefore the failure of every even $C_n$ (where $C_n$ denotes the principle that every family of nonempty $n$-element sets admits a choice function). On the odd side, the present bounded packet calculus remains dyadic: support-fixed local actions factor through finite $2$-groups, bounded support-equivariant quotients of finite local orbits have power-of-two size, and trace-separated bounded rigid ternary families admit canonical selectors within a fixed finite trace window. Accordingly, the odd exact-cardinality profile remains open beyond the current local binary machinery.
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[2026-03-30 Mon (UTC), 1 new article found for q-bio.PE Populations and Evolution]
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[2026-03-30 Mon (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
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[2026-03-31 Tue (UTC), 6 new articles found for cs.CG Computational Geometry]
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[2026-02-26 Thu (UTC), no new articles found for math.GN General Topology]
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wiki_science: Wikipedia Map of Science (2020)
A network of scientific fields, extracted from the English Wikipedia in early 2020. Nodes are wikipedia pages representing natural, formal, social and applied sciences, and two nodes are linked if the cosine similarity of the page content is above a threshold. See <http://www.s…

An $\Omega ( (\log n / \log \log n)^2 )$ Cell-Probe Lower Bound for Dynamic Boolean Data Structures
Young Kun Ko
https://arxiv.org/abs/2603.25914 https://a…

PiPNN: Ultra-Scalable Graph-Based Nearest Neighbor Indexing
Tobias Rubel, Richard Wen, Laxman Dhulipala, Lars Gottesb\"uren, Rajesh Jayaram, Jakub {\L}\k{a}cki
https://arxiv.org/abs/2602.21247

Replaced article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Analysis of collision shift assessments in ion-based clocks
M. D. Barrett, K. J. Arnold

Speedability of computably approximable reals and their approximations
George Barmpalias, Nan Fang, Wolfgang Merkle, Ivan Titov
https://arxiv.org/abs/2603.26484 https://arxiv.org/pdf/2603.26484 https://arxiv.org/html/2603.26484
arXiv:2603.26484v1 Announce Type: new
Abstract: An approximation of a real is a sequence of rational numbers that converges to the real. An approximation is left-c.e. if it is computable and nondecreasing and is d.c.e. if it is computable and has bounded variation. A real is computably approximable if it has some computable approximation, and left-c.e. and d.c.e. reals are defined accordingly.
An approximation $\{a_s\}_{s \in \omega}$ is speedable if there exists a nondecreasing computable function $f$ such that the approximation $\{a_{f(s)}\}_{s \in \omega}$ converges in a certain formal sense faster than $\{a_s\}_{s \in \omega}$. This leads to various notions of speedability for reals, e.g., one may require for a computably approximable real that either all or some of its approximations of a specific type are speedable.
Merkle and Titov established the equivalence of several speedability notions for left-c.e. reals that are defined in terms of left-c.e. approximations. We extend these results to d.c.e. reals and d.c.e. approximations, and we prove that in this setting, being speedable is equivalent to not being Martin-L\"{o}f random. Finally, we demonstrate that every computably approximable real has a computable approximation that is speedable.
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Premature Dimensional Collapse and Tensor-based Execution Paths for High-Dimensional Relational Operations in Cost-Based Database Systems
Il-Sun Chang
https://arxiv.org/abs/2602.21237

Phase Coherence of Strongly Interacting Bosons in One-Dimensional Optical Lattices
R. Vatr\'e, G. Morettini, J. Beugnon, R. Lopes, L. Mazza, F. Gerbier
https://arxiv.org/abs/2603.26118

Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Collisional-radiative data for tokamak disruption mitigation modeling
Sharma, Fontes, Fursa, Bray, Zammit, Colgan, Chung, Garland, Tang

Proofdoors and Efficiency of CDCL Solvers
Sunidhi Singh, Vincent Liew, Marc Vinyals, Vijay Ganesh
https://arxiv.org/abs/2603.26286 https://arxiv.org/pdf/26…

[2026-03-30 Mon (UTC), 3 new articles found for math.LO Logic]
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The Cone of J-Hermitian Matrices and a Geometric Mean
Jose Franco, Allan Merino
https://arxiv.org/abs/2602.21258 https://arxiv.org/pdf/2602.21258 https://arxiv.org/html/2602.21258
arXiv:2602.21258v1 Announce Type: new
Abstract: We study the cone $\mathscr{P}_{\text{J}}$ of positive J-Hermitian matrices associated with an indefinite signature matrix J = $\text{Id}_{p,q}$. We show that the J-exponential map is bijective and use it to analyze the algebraic and geometric structure of $\mathscr{P}_{\text{J}}$. Through a canonical identification with the cone of positive definite matrices, we endow $\mathscr{P}_{\text{J}}$ with a natural Riemannian structure. In this setting, we define a J-geometric mean as the midpoint of geodesics and prove that it is uniquely characterized as the solution of a Riccati-type equation.
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Topological Relational Theory: A Simplicial-Complex View of Functional Dependencies, Lossless Decomposition, and Acyclicity
Bilge Senturk, Faruk Alpay
https://arxiv.org/abs/2602.21213

Extreme (Rogue) Waves: From Theory to Experiments in Ultracold Gases and Beyond
A. Chabchoub, P. Engels, P. G. Kevrekidis, S. I. Mistakidis, G. C. Katsimiga, M. E. Mossman, S. Mossman
https://arxiv.org/abs/2603.25908

Control of emission interval and timing in triggered periodic superradiance
Hideaki Hara, Riku Omoto, Noboru Sasao, Akihiro Yoshimi, Junseok Han, Yasutaka Imai, Koji Yoshimura, Motohiko Yoshimura, Yuki Miyamoto
https://arxiv.org/abs/2603.26103

[2026-03-30 Mon (UTC), 2 new articles found for cs.CC Computational Complexity]
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Diffeological Spaces with a Non-Smooth Derivation
Masaki Taho
https://arxiv.org/abs/2602.21235 https://arxiv.org/pdf/2602.21235 https://arxiv.org/html/2602.21235
arXiv:2602.21235v1 Announce Type: new
Abstract: We show that on certain diffeological spaces there exist linear derivations that satisfy the Leibniz rule but are not smooth with respect to the given diffeology. This reveals that the notion of tangent space defined via all such derivations is strictly larger than the one defined using only smooth derivations, showing that smoothness cannot be recovered from the Leibniz rule alone.
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Dynamic Nearest-Neighbor Searching Under General Metrics in ${\mathbb R}^3$ and Its Applications
Pankaj K. Agarwal, Matthew J. Katz, Micha Sharir
https://arxiv.org/abs/2603.26585

Structured-Light Magnetometry in a Coherently Controlled Atomic Medium
Parkhi Bhardwaj, Shubhrangshu Dasgupta
https://arxiv.org/abs/2603.25781 https://arxi…

[2026-02-26 Thu (UTC), 13 new articles found for cs.DB Databases]
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A Dipolar Chiral Spin Liquid on the Breathed Kagome Lattice
Francisco Machado, Sabrina Chern, Michael P. Zaletel, Norman Y. Yao
https://arxiv.org/abs/2603.25784 https://

[2026-03-30 Mon (UTC), 1 new article found for cs.CG Computational Geometry]
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Detecting Complex-Energy Braiding Topology in a Dissipative Atomic Simulator with Transformer-Based Geometric Tomography
Yang Yue, Nan Li, Xin Zhang, Chenhao Wang, Zeming Fang, Zhonghua Ji, Liantuan Xiao, Suotang Jia, Yanting Zhao, Liang Bai, Ying Hu
https://arxiv.org/abs/2603.25775

[2026-02-26 Thu (UTC), 5 new articles found for math.DG Differential Geometry]
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[2026-03-30 Mon (UTC), 6 new articles found for cond-mat.quant-gas Quantum Gases]
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RISK: Efficiently processing rich spatial-keyword queries on encrypted geo-textual data
Zhen Lv, Cong Cao, Hongwei Huo, Jiangtao Cui, Yanguo Peng, Hui Li, Yingfan Liu
https://arxiv.org/abs/2602.20952

Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Comment on "Determining angle of arrival of radio-frequency fields using subwavelength, amplitude...
M. Chilcott, N. Kj{\ae}rgaard

cuRPQ: A High-Performance GPU-Based Framework for Processing Regular and Conjunctive Regular Path Queries
Sungwoo Park, Seohyeon Kim, Min-Soo Kim
https://arxiv.org/abs/2602.20748

Development of a Quantum Blackbody Thermometer toward Primary On-orbit Thermometry
Peter J. Beierle, Denis Tremblay, Noah Schlossberger, Christopher L. Holloway, Stephen P. Eckel, Eric B. Norrgard
https://arxiv.org/abs/2601.20607

[2026-01-29 Thu (UTC), 1 new article found for physics.atom-ph Atomic Physics]
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Spectral Phase Pulse Shaping Alters Photoionization Time
J. Aygun, D. Yaacoub, A. L. Harris
https://arxiv.org/abs/2602.22137 https://arxiv.org/pdf/2602.221…

Efimov Effect in Ultracold Microwave-Shielded Polar Molecules
Shayamal Singh, Chris H. Greene
https://arxiv.org/abs/2602.21433 https://arxiv.org/pdf/2602.2…

[2026-02-26 Thu (UTC), 2 new articles found for physics.atom-ph Atomic Physics]
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