Tootfinder

5 reasons to submit your idea for #TNC26 Call for Proposals - Part 2
Next June, Helsinki 🇫🇮 will welcome our global R&E community under the theme Digital Sisu—inspired by the Finnish concept of “sisu:” inner strength, perseverance, and determination when things get tough.
In our digital era, sisu is about taking action, meeting challenges with courage, creativity, and collaboration to…

5 reasons to submit your idea for #TNC26 Call for Proposals - Part 1
Next June, Helsinki 🇫🇮 will welcome our global R&E community under the theme Digital Sisu—inspired by the Finnish concept of “sisu:” inner strength, perseverance, and determination when things get tough.
In our digital era, sisu is about taking action, meeting challenges with courage, creativity, and collaboration to…

[2025-12-05 Fri (UTC), 5 new articles found for q-bio.NC Neurons and Cognition]
toXiv_bot_toot

[2026-02-04 Wed (UTC), 5 new articles found for cs.GR Graphics]
toXiv_bot_toot

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

Sources: Peter Thiel and Larry Page may leave CA over a proposed ballot measure, opposed by Newsom, that levies a one-time 5% tax on those with $1B in assets (New York Times)
https://www.nytimes.com/2025/12/26/tec…

A Wealth Tax Floated in California Has Billionaires Thinking of Leaving
It’s uncertain whether the proposal will reach the statewide ballot in November, but some billionaires like Peter Thiel and Larry Page may be unwilling to take the risk.

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Sample, Don't Search: Rethinking Test-Time Alignment for Language Models
Gon\c{c}alo Faria, Noah A. Smith
https://arxiv.org/abs/2504.03790 https://mastoxiv.page/@arXiv_csCL_bot/114301112970577326
- A Survey on Archetypal Analysis
Aleix Alcacer, Irene Epifanio, Sebastian Mair, Morten M{\o}rup
https://arxiv.org/abs/2504.12392 https://mastoxiv.page/@arXiv_statME_bot/114357826909813483
- The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations
Michael L. Wells, Kamel Lahouel, Bruno Jedynak
https://arxiv.org/abs/2505.11622 https://mastoxiv.page/@arXiv_statML_bot/114539065460187982
- BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions
Kingsley Yeon, Promit Ghosal, Mihai Anitescu
https://arxiv.org/abs/2505.12289 https://mastoxiv.page/@arXiv_mathNA_bot/114539035462135281
- Clustering and Pruning in Causal Data Fusion
Otto Tabell, Santtu Tikka, Juha Karvanen
https://arxiv.org/abs/2505.15215 https://mastoxiv.page/@arXiv_statML_bot/114550346291754635
- On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of ph...
Chloe H. Choi, Andrea Zanoni, Daniele E. Schiavazzi, Alison L. Marsden
https://arxiv.org/abs/2506.11683 https://mastoxiv.page/@arXiv_statML_bot/114692410563481289
- Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Maheshwari, Tang, Ock, Kolluru, Farimani, Kitchin
https://arxiv.org/abs/2506.14850 https://mastoxiv.page/@arXiv_physicschemph_bot/114709402590755731
- Quantifying Uncertainty in the Presence of Distribution Shifts
Yuli Slavutsky, David M. Blei
https://arxiv.org/abs/2506.18283 https://mastoxiv.page/@arXiv_statML_bot/114738165218533987
- ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models
Mina Namazi, Alexander Nemecek, Erman Ayday
https://arxiv.org/abs/2506.20915 https://mastoxiv.page/@arXiv_csCR_bot/114754394485208892
- SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars
Zhao, Huang, Xue, Kong, Liu, Tang, Beers, Ting, Luo
https://arxiv.org/abs/2507.01939 https://mastoxiv.page/@arXiv_astrophIM_bot/114788369702591337
- Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based I...
Ko Watanabe, Stanislav Frolov, Aya Hassan, David Dembinsky, Adriano Lucieri, Andreas Dengel
https://arxiv.org/abs/2507.17860 https://mastoxiv.page/@arXiv_csCV_bot/114912976717523345
- PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning
Yushi Feng, Junye Du, Yingying Hong, Qifan Wang, Lequan Yu
https://arxiv.org/abs/2508.10501 https://mastoxiv.page/@arXiv_csAI_bot/115032101532614110
- Unified Acoustic Representations for Screening Neurological and Respiratory Pathologies from Voice
Ran Piao, Yuan Lu, Hareld Kemps, Tong Xia, Aaqib Saeed
https://arxiv.org/abs/2508.20717 https://mastoxiv.page/@arXiv_csSD_bot/115111255835875066
- Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
Tasnuva Chowdhury, et al.
https://arxiv.org/abs/2509.11512 https://mastoxiv.page/@arXiv_csDC_bot/115213444524490263
- MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair
Ali Reza Ibrahimzada, Brandon Paulsen, Reyhaneh Jabbarvand, Joey Dodds, Daniel Kroening
https://arxiv.org/abs/2509.16187 https://mastoxiv.page/@arXiv_csSE_bot/115247172280557686
- Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation ...
Adam Lahouari, Jutta Rogal, Mark E. Tuckerman
https://arxiv.org/abs/2509.21647 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/115286737423175311
- Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference
Han Yuan, Yue Zhao, Li Zhang, Wuqiong Luo, Zheng Ma
https://arxiv.org/abs/2509.21791 https://mastoxiv.page/@arXiv_csCL_bot/115287166674809413
- The Generation Phases of Flow Matching: a Denoising Perspective
Anne Gagneux, S\'egol\`ene Martin, R\'emi Gribonval, Mathurin Massias
https://arxiv.org/abs/2510.24830 https://mastoxiv.page/@arXiv_csCV_bot/115462527449411627
- Data-driven uncertainty-aware seakeeping prediction of the Delft 372 catamaran using ensemble Han...
Giorgio Palma, Andrea Serani, Matteo Diez
https://arxiv.org/abs/2511.04461 https://mastoxiv.page/@arXiv_eessSY_bot/115507785247809767
- Generalized infinite dimensional Alpha-Procrustes based geometries
Salvish Goomanee, Andi Han, Pratik Jawanpuria, Bamdev Mishra
https://arxiv.org/abs/2511.09801 https://mastoxiv.page/@arXiv_statML_bot/115547135711272091
toXiv_bot_toot

High-Performance KV$_3$Sb$_5$/WSe$_2$ van der Waals Photodetectors
Yang Yang, Shaofeng Rao, Yuxuan Hou, Jiabo Liu, Deng Hu, Yunfei Guo, Jianzhou Zhao, Hechen Ren, Zhiwei Wang, Fan Yang
https://arxiv.org/abs/2512.24229

[2026-01-01 Thu (UTC), 5 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

We also got a copy of the book “Murder Muzik,” which should make for good holiday reading.
https://www.editionsdarkside.com/produit/35/9782017309055/murder-muzik
(I shouldn’t have looked at this page, because I now know that it costs twice as much in Sw…

We also got a copy of the book “Murder Muzik,” which should make for good holiday reading.
https://www.editionsdarkside.com/produit/35/9782017309055/murder-muzik
(I shouldn’t have looked at this page, because I now know that it costs twice as much in Sw…

[Türchenöffnungsgeräusch]
Tag 5 im #klexadventskalender - diesmalholte ich Dir bienenfleißig die Sterne vom Himmel (ganz ohne Buntfug ging das natürlich nicht ab - und passt zu Tag 1!)
Die Neuheiten von heute sind jetzt auf #art #artShop #fediArt MastoArt #creativeToots #artForSale

OK, this is genuinely scary. It's a picture of me, aged 5. It was taken in 1960. It was scanned and uploaded to Google Photos in 2002. No one has told Google where it was taken. But the map, bottom right, is correct.
Obviously, in 1960, my father's pre-war Exakta camera did not have GPS. Could you identify, from that picture, exactly where it was taken?
Google can.

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Look-Ahead Reasoning on Learning Platforms
Haiqing Zhu, Tijana Zrnic, Celestine Mendler-D\"unner
https://arxiv.org/abs/2511.14745 https://mastoxiv.page/@arXiv_csLG_bot/115575981129228810
- Deep Gaussian Process Proximal Policy Optimization
Matthijs van der Lende, Juan Cardenas-Cartagena
https://arxiv.org/abs/2511.18214 https://mastoxiv.page/@arXiv_csLG_bot/115610315210502140
- Spectral Concentration at the Edge of Stability: Information Geometry of Kernel Associative Memory
Akira Tamamori
https://arxiv.org/abs/2511.23083 https://mastoxiv.page/@arXiv_csLG_bot/115644325602130493
- xGR: Efficient Generative Recommendation Serving at Scale
Sun, Liu, Zhang, Wu, Yang, Liang, Li, Ma, Liang, Ren, Zhang, Liu, Zhang, Qian, Yang
https://arxiv.org/abs/2512.11529 https://mastoxiv.page/@arXiv_csLG_bot/115723008170311172
- Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset
Atalay Denknalbant, Emre Sezdi, Zeki Furkan Kutlu, Polat Goktas
https://arxiv.org/abs/2512.12783 https://mastoxiv.page/@arXiv_csLG_bot/115729287232895097
- The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems
Debu Sinha
https://arxiv.org/abs/2512.15068 https://mastoxiv.page/@arXiv_csLG_bot/115740048142898391
- Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
Stritzel, H\"uhnerbein, Rauch, Zarate, Fleischmann, Buck, Lischka, Frey
https://arxiv.org/abs/2512.16715 https://mastoxiv.page/@arXiv_csLG_bot/115745910810427061
- Differentially private Bayesian tests
Abhisek Chakraborty, Saptati Datta
https://arxiv.org/abs/2401.15502 https://mastoxiv.page/@arXiv_statML_bot/111843467510507382
- SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Reda Alami, Alexey Naumov, Eric Moulines
https://arxiv.org/abs/2402.04114
- Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
https://arxiv.org/abs/2408.07588 https://mastoxiv.page/@arXiv_statML_bot/112965266196097314
- Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng
https://arxiv.org/abs/2410.13161 https://mastoxiv.page/@arXiv_heplat_bot/113327593338897860
- Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules
Sun, Zhang, Xia, Sun, Chen, Yang, Liu, Zhu, Liu
https://arxiv.org/abs/2410.22674 https://mastoxiv.page/@arXiv_eessIV_bot/113401026110345647
- Targeted Learning for Variable Importance
Xiaohan Wang, Yunzhe Zhou, Giles Hooker
https://arxiv.org/abs/2411.02221 https://mastoxiv.page/@arXiv_statML_bot/113429912435819479
- Refined Analysis of Federated Averaging and Federated Richardson-Romberg
Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
https://arxiv.org/abs/2412.01389 https://mastoxiv.page/@arXiv_statML_bot/113588027268311334
- Embedding-Driven Data Distillation for 360-Degree IQA With Residual-Aware Refinement
Abderrezzaq Sendjasni, Seif-Eddine Benkabou, Mohamed-Chaker Larabi
https://arxiv.org/abs/2412.12667 https://mastoxiv.page/@arXiv_csCV_bot/113672538318570349
- 3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence
Peter Chen, Bryan Chang, Olivia A Creasey, Julie Beth Sneddon, Zev J Gartner, Yining Liu
https://arxiv.org/abs/2502.01890 https://mastoxiv.page/@arXiv_csCV_bot/113949981686723660
- DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents
Shashank Sharma, Janina Hoffmann, Vinay Namboodiri
https://arxiv.org/abs/2502.01956 https://mastoxiv.page/@arXiv_csRO_bot/113949997485625086
- Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
Diana Koldasbayeva, Alexey Zaytsev
https://arxiv.org/abs/2502.03480
- GraphCompNet: A Position-Aware Model for Predicting and Compensating Shape Deviations in 3D Printing
Juheon Lee (Rachel), Lei (Rachel), Chen, Juan Carlos Catana, Hui Wang, Jun Zeng
https://arxiv.org/abs/2502.09652 https://mastoxiv.page/@arXiv_csCV_bot/114017924551186136
- LookAhead Tuning: Safer Language Models via Partial Answer Previews
Liu, Wang, Luo, Yuan, Sun, Liang, Zhang, Zhou, Hooi, Deng
https://arxiv.org/abs/2503.19041 https://mastoxiv.page/@arXiv_csCL_bot/114227502448008352
- Constraint-based causal discovery with tiered background knowledge and latent variables in single...
Christine W. Bang, Vanessa Didelez
https://arxiv.org/abs/2503.21526 https://mastoxiv.page/@arXiv_statML_bot/114238919468512990
toXiv_bot_toot

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization a...
Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert Nowak, Yixuan Li
https://arxiv.org/abs/2306.09158
- Sparse, Efficient and Explainable Data Attribution with DualXDA
Galip \"Umit Yolcu, Moritz Weckbecker, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin
https://arxiv.org/abs/2402.12118 https://mastoxiv.page/@arXiv_csLG_bot/111962593972369958
- HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs
Sun, Que, {\AA}rrestad, Loncar, Ngadiuba, Luk, Spiropulu
https://arxiv.org/abs/2405.00645 https://mastoxiv.page/@arXiv_csLG_bot/112370274737558603
- On the Identification of Temporally Causal Representation with Instantaneous Dependence
Li, Shen, Zheng, Cai, Song, Gong, Chen, Zhang
https://arxiv.org/abs/2405.15325 https://mastoxiv.page/@arXiv_csLG_bot/112511890051553111
- Basis Selection: Low-Rank Decomposition of Pretrained Large Language Models for Target Applications
Yang Li, Daniel Agyei Asante, Changsheng Zhao, Ernie Chang, Yangyang Shi, Vikas Chandra
https://arxiv.org/abs/2405.15877 https://mastoxiv.page/@arXiv_csLG_bot/112517547424098076
- Privacy Bias in Language Models: A Contextual Integrity-based Auditing Metric
Yan Shvartzshnaider, Vasisht Duddu
https://arxiv.org/abs/2409.03735 https://mastoxiv.page/@arXiv_csLG_bot/113089789682783135
- Low-Rank Filtering and Smoothing for Sequential Deep Learning
Joanna Sliwa, Frank Schneider, Nathanael Bosch, Agustinus Kristiadi, Philipp Hennig
https://arxiv.org/abs/2410.06800 https://mastoxiv.page/@arXiv_csLG_bot/113283021321510736
- Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification
Xiaoyu Tao, Tingyue Pan, Mingyue Cheng, Yucong Luo, Qi Liu, Enhong Chen
https://arxiv.org/abs/2410.18686 https://mastoxiv.page/@arXiv_csLG_bot/113367101100828901
- Fairness via Independence: A (Conditional) Distance Covariance Framework
Ruifan Huang, Haixia Liu
https://arxiv.org/abs/2412.00720 https://mastoxiv.page/@arXiv_csLG_bot/113587817648503815
- Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning
Simon Frieder, et al.
https://arxiv.org/abs/2412.15184 https://mastoxiv.page/@arXiv_csLG_bot/113683924322164777
- Pairwise Elimination with Instance-Dependent Guarantees for Bandits with Cost Subsidy
Ishank Juneja, Carlee Joe-Wong, Osman Ya\u{g}an
https://arxiv.org/abs/2501.10290 https://mastoxiv.page/@arXiv_csLG_bot/113859392622871057
- Towards Human-Guided, Data-Centric LLM Co-Pilots
Evgeny Saveliev, Jiashuo Liu, Nabeel Seedat, Anders Boyd, Mihaela van der Schaar
https://arxiv.org/abs/2501.10321 https://mastoxiv.page/@arXiv_csLG_bot/113859392688054204
- Regularized Langevin Dynamics for Combinatorial Optimization
Shengyu Feng, Yiming Yang
https://arxiv.org/abs/2502.00277
- Generating Samples to Probe Trained Models
Eren Mehmet K{\i}ral, Nur\c{s}en Ayd{\i}n, \c{S}. \.Ilker Birbil
https://arxiv.org/abs/2502.06658 https://mastoxiv.page/@arXiv_csLG_bot/113984059089245671
- On Agnostic PAC Learning in the Small Error Regime
Julian Asilis, Mikael M{\o}ller H{\o}gsgaard, Grigoris Velegkas
https://arxiv.org/abs/2502.09496 https://mastoxiv.page/@arXiv_csLG_bot/114000974082372598
- Preconditioned Inexact Stochastic ADMM for Deep Model
Shenglong Zhou, Ouya Wang, Ziyan Luo, Yongxu Zhu, Geoffrey Ye Li
https://arxiv.org/abs/2502.10784 https://mastoxiv.page/@arXiv_csLG_bot/114023667639951005
- On the Effect of Sampling Diversity in Scaling LLM Inference
Wang, Liu, Chen, Light, Liu, Chen, Zhang, Cheng
https://arxiv.org/abs/2502.11027 https://mastoxiv.page/@arXiv_csLG_bot/114023688225233656
- How to use score-based diffusion in earth system science: A satellite nowcasting example
Randy J. Chase, Katherine Haynes, Lander Ver Hoef, Imme Ebert-Uphoff
https://arxiv.org/abs/2505.10432 https://mastoxiv.page/@arXiv_csLG_bot/114516300594057680
- PEAR: Equal Area Weather Forecasting on the Sphere
Hampus Linander, Christoffer Petersson, Daniel Persson, Jan E. Gerken
https://arxiv.org/abs/2505.17720 https://mastoxiv.page/@arXiv_csLG_bot/114572963019603744
- Train Sparse Autoencoders Efficiently by Utilizing Features Correlation
Vadim Kurochkin, Yaroslav Aksenov, Daniil Laptev, Daniil Gavrilov, Nikita Balagansky
https://arxiv.org/abs/2505.22255 https://mastoxiv.page/@arXiv_csLG_bot/114589956040892075
- A Certified Unlearning Approach without Access to Source Data
Umit Yigit Basaran, Sk Miraj Ahmed, Amit Roy-Chowdhury, Basak Guler
https://arxiv.org/abs/2506.06486 https://mastoxiv.page/@arXiv_csLG_bot/114658421178857085
toXiv_bot_toot

Minnesota's brief asserts that federal agents left the scene several hrs after the shooting,
“allowing the perimeter to collapse and potentially spoiling evidence,”
a “sharp departure from normal best practices” that may’ve “directly led to the destruction of evidence.”
https://

Halleluja!
Dann sehe ich Eure Antworten demnächst sogar.
https://blog.joinmastodon.org/2025/11/mastodon-4.5/

Multi-port programmable silicon photonics using low-loss phase change material Sb$_2$Se$_3$
Thomas W. Radford, Idris A Ajia, Latif Rozaqi, Priya Deoli, Xingzhao Yan, Mehdi Banakar, David J Thomson, Ioannis Zeimpekis, Alberto Politi, Otto L. Muskens
https://arxiv.org/abs/2511.18205 https://arxiv.org/pdf/2511.18205 https://arxiv.org/html/2511.18205
arXiv:2511.18205v1 Announce Type: new
Abstract: Reconfigurable photonic devices are rapidly emerging as a cornerstone of next generation optical technologies, with wide ranging applications in quantum simulation, neuromorphic computing, and large-scale photonic processors. A central challenge in this field is identifying an optimal platform to enable compact, efficient, and scalable reconfigurability. Optical phase-change materials (PCMs) offer a compelling solution by enabling non-volatile, reversible tuning of optical properties, compatible with a wide range of device platforms and current CMOS technologies. In particular, antimony tri-selenide ($\text{Sb}_{2}\text{Se}_{3}$) stands out for its ultra low-loss characteristics at telecommunication wavelengths and its reversible switching. In this work, we present an experimental platform capable of encoding multi-port operations onto the transmission matrix of a compact multimode interferometer architecture on standard 220~nm silicon photonics using \textit{in-silico} designed digital patterns. The multi-port devices are clad with a thin film of $\text{Sb}_{2}\text{Se}_{3}$, which can be optically addressed using direct laser writing to provide local perturbations to the refractive index. A range of multi-port geometries from 2$\times$2 up to 5$\times$5 couplers are demonstrated, achieving simultaneous control of up to 25 matrix elements with programming accuracy of 90% relative to simulated patterns. Patterned devices remain stable with consistent optical performance across the C-band wavelengths. Our work establishes a pathway towards the development of large scale PCM-based reconfigurable multi-port devices which will allow implementing matrix operations on three orders of magnitude smaller areas than interferometer meshes.
toXiv_bot_toot

If you are watching CART via Otter•ai and need the scrollbars at all (to scroll, to see where you are in the page, etc), then you can fix the WCAG SC 1.4.11 and 2.5.5 failures by adding this CSS to the page:
```
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scrollbar-width: unset;
scrollbar-color: unset;
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Analysis: music copyright's global value rose 5.2% YoY to a record $47.2B in 2024; the revenue split favored labels and artists at 62%, above songwriters' 38% (Will Page/Pivotal Economics)
https://pivotaleconomics.com/undercurrents/music-copyright-2025

Global Value of Music Copyright Hits New All-Time High, Nearly Doubling in a Decade to $47.2bn in 2024 — Pivotal Economics
The global value of music copyright reached $47.2bn in 2024. That's up just $2.3bn (5.2%) on the prior year; growth is slowing largely because this is the first year where the pandemic effects have vanished.

#AdventOfSystemSeeing Day 5, still trying to catch up with @…

Advent(ure) in System Seeing — Day 5 — Key Systems Concept: Feedback
Let’s gather some of the thoughts that have been percolating around systems. Let’s assume that you are designing the introductory page of…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/5]:
- Object-centric proto-symbolic behavioural reasoning from pixels
Ruben van Bergen, Justus H\"ubotter, Alma Lago, Pablo Lanillos

[2025-12-30 Tue (UTC), 5 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

found the website of a dog-obsessed german couple, then the page where they memorialize their dead dogs
http://www.cmf-dogs.at/index.php/in-memoriam

A very dear friend of mine, Fern, who taught me pretty much all I know about overland bike travel, is in need of a new wheelchair. One that will get her out and about again on new adventures on five wheels rather than the two she is more used to.
Please do give what you can - it all helps.
https://www.justgivin…

Help raise £4500 to get mobile and living on 5 wheels rather than two, with a specialist wheelchair and power assist, and hopefully have some adventures too! – JustGiving Crowdfunding
Iʼm raising money to get mobile and living on 5 wheels rather than two, with a specialist wheelchair and power assist, and hopefully have some adventures too!. Support this JustGiving Crowdfunding Page.

🏡 Silicon Valley ohne Tech-Milliardäre? Noch ist unklar, ob die 5 %-Reichensteuer kommt. Aber die Erwartung reicht schon: Larry Page, Peter Thiel & Co. verlegen Vermögenssitze in andere US-Bundesstaaten. 🇺🇸 #Capital | #Reichensteuer

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- CLAReSNet: When Convolution Meets Latent Attention for Hyperspectral Image Classification
Asmit Bandyopadhyay, Anindita Das Bhattacharjee, Rakesh Das
https://arxiv.org/abs/2511.12346 https://mastoxiv.page/@arXiv_csCV_bot/115570753208147835
- Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without ...
Dimitris Oikonomou, Nicolas Loizou
https://arxiv.org/abs/2512.02342 https://mastoxiv.page/@arXiv_mathOC_bot/115654870924418771
- Predictive Modeling of I/O Performance for Machine Learning Training Pipelines: A Data-Driven App...
Karthik Prabhakar, Durgamadhab Mishra
https://arxiv.org/abs/2512.06699 https://mastoxiv.page/@arXiv_csPF_bot/115688618582182232
- Minimum Bayes Risk Decoding for Error Span Detection in Reference-Free Automatic Machine Translat...
Lyu, Song, Kamigaito, Ding, Tanaka, Utiyama, Funakoshi, Okumura
https://arxiv.org/abs/2512.07540 https://mastoxiv.page/@arXiv_csCL_bot/115689532163491162
- In-Context Learning for Seismic Data Processing
Fabian Fuchs, Mario Ruben Fernandez, Norman Ettrich, Janis Keuper
https://arxiv.org/abs/2512.11575 https://mastoxiv.page/@arXiv_csCV_bot/115723040285820239
- Journey Before Destination: On the importance of Visual Faithfulness in Slow Thinking
Rheeya Uppaal, Phu Mon Htut, Min Bai, Nikolaos Pappas, Zheng Qi, Sandesh Swamy
https://arxiv.org/abs/2512.12218 https://mastoxiv.page/@arXiv_csCV_bot/115729165330908574
- Non-Resolution Reasoning (NRR): A Computational Framework for Contextual Identity and Ambiguity P...
Kei Saito
https://arxiv.org/abs/2512.13478 https://mastoxiv.page/@arXiv_csCL_bot/115729234145554554
- Stylized Synthetic Augmentation further improves Corruption Robustness
Georg Siedel, Rojan Regmi, Abhirami Anand, Weijia Shao, Silvia Vock, Andrey Morozov
https://arxiv.org/abs/2512.15675 https://mastoxiv.page/@arXiv_csCV_bot/115740141862163631
- mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs
Jonas Pai, Liam Achenbach, Victoriano Montesinos, Benedek Forrai, Oier Mees, Elvis Nava
https://arxiv.org/abs/2512.15692 https://mastoxiv.page/@arXiv_csRO_bot/115739947869830764
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[2026-01-30 Fri (UTC), 5 new articles found for physics.atom-ph Atomic Physics]
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Raiders Week 15 snap counts vs Eagles: 5 notable observations https://raiderswire.usatoday.com/story/sports/nfl/raiders/2025/12/16/raiders-week-15-snap-counts-vs-eagles/87797009007/

Hey Mastodon, can you please help me out here?
Which is the best *free tool* to organise a community of school parents in Italy?
For now they consider a GoogleDoc for documentation (just 4-5 pages for now) a Facebook page (to organise the discussions in threads). I'd like to propose a free alternative
People involved could be 10-30. There should be some sort of collaborative document management (like a wiki), and ideally a space for discussion (like a forum).
Id…

High-precision luminescence cryothermometry strategy by using hyperfine structure
Marina N. Popova, Mosab Diab, Boris Z. Malkin
https://arxiv.org/abs/2511.19088 https://arxiv.org/pdf/2511.19088 https://arxiv.org/html/2511.19088
arXiv:2511.19088v1 Announce Type: new
Abstract: A novel, to the best of our knowledge, ultralow-temperature luminescence thermometry strategy is proposed, based on a measurement of relative intensities of hyperfine components in the spectra of Ho$^{3 }$ ions doped into a crystal. A $^{7}$LiYF$_4$:Ho$^{3 }$ crystal is chosen as an example. First, we show that temperatures in the range 10-35 K can be measured using the Boltzmann behavior of the populations of crystal-field levels separated by an energy interval of 23 cm$^{-1}$. Then we select the 6089 cm$^{-1}$ line of the holmium $^5I_5 \rightarrow ^5I_7$ transition, which has a well-resolved hyperfine structure and falls within the transparency window of optical fibers (telecommunication S band), to demonstrate the possibility of measuring temperatures below 3 K. The temperature $T$ is determined by a least-squares fit to the measured intensities of all eight hyperfine components using the dependence $I(\nu) = I_1 \exp(-b\nu)$, where $I_1$ and $b = a\nu \frac{\nu}{kT}$ are fitting parameters and a accounts for intensity variations due to mixing of wave functions of different crystal-field levels by the hyperfine interaction. In this method, the absolute and relative thermal sensitivities grow at $T$ approaching zero as $\frac{1}{T^2}$.and $\frac{1}{T}$, respectively. We theoretically considered the intensity distributions within hyperfine manifolds and compared the results with experimental data. Application of the method to experimentally measured relative intensities of hyperfine components of the 6089 cm$^{-1}$ PL line yielded $T = 3.7 \pm 0.2$ K. For a temperature of 1 K, an order of magnitude better accuracy is expected.
toXiv_bot_toot

Uh, that's new?! Apps installed from #AuroraStore can't be updated in #GooglePlayStore anymore?
I just updated to Aurora 4.7.5.
#Android15

Non-Gravitational Acceleration in 3I ATLAS: Constraints on Exotic Volatile Outgassing in Interstellar Comets
Florian Neukart
https://arxiv.org/abs/2511.07450 https://arxiv.org/pdf/2511.07450 https://arxiv.org/html/2511.07450
arXiv:2511.07450v1 Announce Type: new
Abstract: The interstellar comet 3I/ATLAS exhibited a measurable nongravitational acceleration similar in form to that of 1I/'Oumuamua but of smaller magnitude. Using thermophysical and Monte Carlo models, we show that this acceleration can be fully explained by anisotropic outgassing of conventional volatiles, primarily CO and CO2, under realistic surface and rotational conditions. The model includes diurnal and obliquity-averaged energy balance, empirical vapor-pressure relations, and collimated jet emission from localized active regions. Mixed CO-CO2 compositions reproduce both the magnitude and direction of the observed acceleration with physically plausible active fractions below one percent for nucleus radii between 0.5 and 3 km. Less volatile species such as NH3 and CH4 underproduce thrust at equilibrium temperatures near 1 AU. These results eliminate the need for nonphysical or exotic explanations and define thermophysical limits for natural acceleration mechanisms in interstellar comets.
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[2026-01-21 Wed (UTC), 5 new articles found for cond-mat.dis-nn Disordered Systems and Neural Networks]
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[2025-12-10 Wed (UTC), 5 new articles found for cs.GT Computer Science and Game Theory]
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"Faking Receipts with AI"
smh ... every part of our digital experience is about to be cooked.
https://www.schneier.com/blog/archives/2025/11/faking-receipts-with-ai.html

Did California lose Larry Page?
The Google and Alphabet cofounder, who left day-to-day operations in 2019,
has seen his net worth soar in the years since
—from around $50 billion at the time of his departure to somewhere approximating $260 billion today.
(Leaving his job clearly didn’t hurt his wallet.)
Last year, a proposed ballot initiative in California threatened billionaires like Page with a one-time 5 percent wealth tax
—prompting some of them to con…

Silicon Valley Billionaires Panic Over California’s Proposed Wealth Tax
Larry Page’s apparent Florida move highlights how seriously the ultra-rich are taking a one-time tax aimed at extreme wealth inequality.

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/5]:
- Fairness-Aware Fine-Tuning of Vision-Language Models for Medical Glaucoma Diagnosis
Zijian Gu, Yuxi Liu, Zhenhao Zhang, Song Wang

Scientific Advice Mechanism to the European Commission
December 2025
SAPEA (2025). Artificial intelligence in emergency and crisis management: Rapid evidence review report.
Downloadable from https://doi.org/10.5281/zenodo.17737962

[2026-01-27 Tue (UTC), 5 new articles found for physics.atom-ph Atomic Physics]
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Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/5]:
- Unsupervised Learning for Industrial Defect Detection: A Case Study on Shearographic Data
Jessica Plassmann, Nicolas Schuler, Georg von Freymann, Michael Schuth

#WritersCoffeeClub November
3. How much of your writing time is actively spent putting words on the page?
4. What's the funniest thing you've ever written?
5. Is the idea of a work requiring a takeaway 'moral' old-fashioned?
---
3. Much less than mulling it over. 5%? Probably still too generous.
I can cycle ideas for years.
4. Wish I could r…

NeuroSketch: An Effective Framework for Neural Decoding via Systematic Architectural Optimization
Gaorui Zhang, Zhizhang Yuan, Jialan Yang, Junru Chen, Li Meng, Yang Yang
https://arxiv.org/abs/2512.09524 https://arxiv.org/pdf/2512.09524 https://arxiv.org/html/2512.09524
arXiv:2512.09524v1 Announce Type: new
Abstract: Neural decoding, a critical component of Brain-Computer Interface (BCI), has recently attracted increasing research interest. Previous research has focused on leveraging signal processing and deep learning methods to enhance neural decoding performance. However, the in-depth exploration of model architectures remains underexplored, despite its proven effectiveness in other tasks such as energy forecasting and image classification. In this study, we propose NeuroSketch, an effective framework for neural decoding via systematic architecture optimization. Starting with the basic architecture study, we find that CNN-2D outperforms other architectures in neural decoding tasks and explore its effectiveness from temporal and spatial perspectives. Building on this, we optimize the architecture from macro- to micro-level, achieving improvements in performance at each step. The exploration process and model validations take over 5,000 experiments spanning three distinct modalities (visual, auditory, and speech), three types of brain signals (EEG, SEEG, and ECoG), and eight diverse decoding tasks. Experimental results indicate that NeuroSketch achieves state-of-the-art (SOTA) performance across all evaluated datasets, positioning it as a powerful tool for neural decoding. Our code and scripts are available at https://github.com/Galaxy-Dawn/NeuroSketch.
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Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/5]:
- ExAct: A Video-Language Benchmark for Expert Action Analysis
Han Yi, Yulu Pan, Feihong He, Xinyu Liu, Benjamin Zhang, Oluwatumininu Oguntola, Gedas Bertasius

[2025-12-24 Wed (UTC), 5 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

Strategyproof Tournament Rules for Teams with a Constant Degree of Selfishness
David Pennock, Daniel Schoepflin, Kangning Wang
https://arxiv.org/abs/2512.05235 https://arxiv.org/pdf/2512.05235 https://arxiv.org/html/2512.05235
arXiv:2512.05235v1 Announce Type: new
Abstract: We revisit the well-studied problem of designing fair and manipulation-resistant tournament rules. In this problem, we seek a mechanism that (probabilistically) identifies the winner of a tournament after observing round-robin play among $n$ teams in a league. Such a mechanism should satisfy the natural properties of monotonicity and Condorcet consistency. Moreover, from the league's perspective, the winner-determination tournament rule should be strategyproof, meaning that no team can do better by losing a game on purpose.
Past work considered settings in which each team is fully selfish, caring only about its own probability of winning, and settings in which each team is fully selfless, caring only about the total winning probability of itself and the team to which it deliberately loses. More recently, researchers considered a mixture of these two settings with a parameter $\lambda$. Intermediate selfishness $\lambda$ means that a team will not lose on purpose unless its pair gains at least $\lambda s$ winning probability, where $s$ is the individual team's sacrifice from its own winning probability. All of the dozens of previously known tournament rules require $\lambda = \Omega(n)$ to be strategyproof, and it has been an open problem to find such a rule with the smallest $\lambda$.
In this work, we make significant progress by designing a tournament rule that is strategyproof with $\lambda = 11$. Along the way, we propose a new notion of multiplicative pairwise non-manipulability that ensures that two teams cannot manipulate the outcome of a game to increase the sum of their winning probabilities by more than a multiplicative factor $\delta$ and provide a rule which is multiplicatively pairwise non-manipulable for $\delta = 3.5$.
toXiv_bot_toot

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/5]:
- Dual Cluster Contrastive learning for Object Re-Identification
Hantao Yao, Changsheng Xu

Optical investigation of ultra-slow spin relaxation in $^{171}$Yb$^{3 }$:Y$_2$SiO$_5$ single crystals
Federico Chiossi, Alexey Tiranov, Luois Nicolas, Diana Serrano, Felix Montjovet-Basset, Elo\"ise Lafitte-Houssat, Alban Ferrier, Sacha Welinski, Lo\"ic Morvan, Perrine Berger, Mikael Afzelius, Philippe Goldner
https://arxiv.org/a…

Atomic and molecular systems for radiation thermometry
Stephen P. Eckel, Eric B. Norrgard, Christopher Holloway, Nikunjkumar Prajapati, Noah Schlossberger, Matthew Simons
https://arxiv.org/abs/2512.08668 https://arxiv.org/pdf/2512.08668 https://arxiv.org/html/2512.08668
arXiv:2512.08668v1 Announce Type: new
Abstract: Atoms and simple molecules are excellent candidates for new standards and sensors because they are both all identical and their properties are determined by the immutable laws of quantum physics. Here, we introduce the concept of building a standard and sensor of radiative temperature using atoms and molecules. Such standards are based on precise measurement of the rate at which blackbody radiation (BBR) either excites or stimulates emission for a given atomic transition. We summarize the recent results of two experiments while detailing the rate equation models required for their interpretation. The cold atom thermometer (CAT) uses a gas of laser cooled $^{85}$Rb Rydberg atoms to probe the BBR spectrum near 130~GHz. This primary, {\it i.e.}, not traceable to a measurement of like kind, temperature measurement currently has a total uncertainty of approximately 1~\%, with clear paths toward improvement. The compact blackbody radiation atomic sensor (CoBRAS) uses a vapour of $^{85}$Rb and monitors fluorescence from states that are either populated by BBR or populated by spontaneous emission to measure the blackbody spectrum near 24.5~THz. The CoBRAS has an excellent relative precision of $u(T)\approx 0.13$~K, with a clear path toward implementing a primary
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The 0.5 Ratio Limit and Geometry-Induced Missing Energy: Universal 3D Quantum Constraints on Fragment Distributions from Attosecond to Subatomic Scales
Jinzhen Zhu
https://arxiv.org/abs/2601.08255

Optical clocks with accuracy validated at the 19th digit
K. J. Arnold, M. D. K. Lee, Zhao Qi, Qichen Qin, Zhang Zhao, N. Jayjong, M. D. Barrett
https://arxiv.org/abs/2512.07346 https://arxiv.org/pdf/2512.07346 https://arxiv.org/html/2512.07346
arXiv:2512.07346v1 Announce Type: new
Abstract: We report a comprehensive evaluation of all known sources of systematic uncertainty for two independent $^{176}$Lu$^ $ single-ion optical references, finding total systematic uncertainty of $1.1\times10^{-19}$ and $1.4\times10^{-19}$ for the two individual systems and $9.6\times10^{-20}$ for the difference. Through direct comparison via correlation spectroscopy, we demonstrate a relative frequency agreement of $-2.4\pm(5.7)_\mathrm{stat}\pm(1.0)_\mathrm{sys}\times10^{-19}$, where `stat' and `sys' indicate the statistical and systematic uncertainty, respectively. The comparison uncertainty is statistically limited after approximately 200 hours of averaging with a measurement instability of $4.8\times10^{-16}(\tau/\mathrm{s})^{-1/2}$.
toXiv_bot_toot

Galaktisch beliebt und jetzt wieder ein paar im Lager: von den Edelstahl-Schätzchen mit handgemalten #wandklexschmuck Galaxien sind wieder pro Ringmodell 5 Stück verfügbar (als Set oder Einzeln zu haben, Farbe wählbar, denn wird erst on demand montiert - auf #fediArt #mastoArt #creativeToots #artjewellery

[2026-01-14 Wed (UTC), 5 new articles found for physics.atom-ph Atomic Physics]
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Nuclear spin quenching of the $^2S_{1/2}\rightarrow {^2}F_{7/2} $ electric octupole transition in $^{173}$Yb$^ $
Jialiang Yu, Anand Prakash, Clara Zyskind, Ikbal A. Biswas, Rattakorn Kaewuam, Piyaphat Phoonthong, Tanja E. Mehlst\"aubler
https://arxiv.org/abs/2512.05872 https://arxiv.org/pdf/2512.05872 https://arxiv.org/html/2512.05872
arXiv:2512.05872v1 Announce Type: new
Abstract: We report the coherent excitation of the highly forbidden $^2S_{1/2} \rightarrow {^2}F_{7/2}$ clock transition in the odd isotope $^{173}\mathrm{Yb}^ $ with nuclear spin $I = 5/2$, and reveal the hyperfine-state-dependent, nuclear spin induced quenching of this transition. The inferred lifetime of the $F_e = 4$ hyperfine state is one order of magnitude shorter than the unperturbed ${^2}F_{7/2}$ clock state of $^{171}\mathrm{Yb}^ $. This reduced lifetime lowers the required optical power for coherent excitation of the clock transition, thereby reducing the AC Stark shift caused by the clock laser. Using a 3-ion Coulomb crystal, we experimentally demonstrate an approximately 20-fold suppression of the AC Stark shift, a critical improvement for the scalability of future multi-ion $\mathrm{Yb}^ $ clocks. Furthermore, we report the $|^2S_{1/2},F_g=3\rangle~\rightarrow~|^2F_{7/2},F_e=6\rangle$ unquenched reference transition frequency as $642.11917656354(43)$ THz, along with the measured hyperfine splitting and calculated quadratic Zeeman sensitivities of the ${^2}F_{7/2}$ clock state. Our results pave the way toward multi-ion optical clocks and quantum computers based on $^{173}\mathrm{Yb}^ $.
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