Tootfinder

A nice piece of work, I hope it encourages improvements!
Towards a science of AI agent reliability
AI 'reliability lags capability, and that reliability will remain a barrier to deployment unless researchers and developers focus effort on improving reliability as a separate dimension from accuracy'
https://…

New Paper: Towards a science of AI agent reliability
Quantifying the capability-reliability gap

And here is my published dissertation @…, about quantifying the natural CO2 exhaust at the Starzach site in Southwest Germany (my result: ~10t/d):
http://hdl.handle.net/10900/176213…

I've finally gotten Termux to rsync files via crontab. Needed to set up keychain, something I used to understand in the distant past. Now I have my own quantified-self data (well, location and battery) uploading to my private cloud.
#termux termux.api, python, rsync, crontab, keychain.

🇺🇦 Auf radioeins läuft...
Thee Marloes:
🎵 Under The Silver Moon
#NowPlaying #TheeMarloes
https://theemarloes.bandcamp.com/track/under-the-silver-moon
https://open.spotify.com/track/6qa9ImotbPJJ287VzSSN9X

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/6]:
- Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints
Chuqin Geng, Li Zhang, Mark Zhang, Haolin Ye, Ziyu Zhao, Xujie Si
https://arxiv.org/abs/2602.16954 https://mastoxiv.page/@arXiv_csLG_bot/116102434757760085
- Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Fres...
Ziliang Zhao, et al.
https://arxiv.org/abs/2602.17050 https://mastoxiv.page/@arXiv_csLG_bot/116102517335590034
- MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sam...
Fu, Lin, Fang, Zheng, Hu, Shao, Qin, Pan, Zeng, Cai
https://arxiv.org/abs/2602.17550 https://mastoxiv.page/@arXiv_csLG_bot/116102581561441103
- A Theoretical Framework for Modular Learning of Robust Generative Models
Corinna Cortes, Mehryar Mohri, Yutao Zhong
https://arxiv.org/abs/2602.17554 https://mastoxiv.page/@arXiv_csLG_bot/116102582216715527
- Multi-Round Human-AI Collaboration with User-Specified Requirements
Sima Noorani, Shayan Kiyani, Hamed Hassani, George Pappas
https://arxiv.org/abs/2602.17646 https://mastoxiv.page/@arXiv_csLG_bot/116102592047544971
- NEXUS: A compact neural architecture for high-resolution spatiotemporal air quality forecasting i...
Rampunit Kumar, Aditya Maheshwari
https://arxiv.org/abs/2602.19654 https://mastoxiv.page/@arXiv_csLG_bot/116125610403473755
- Augmenting Lateral Thinking in Language Models with Humor and Riddle Data for the BRAINTEASER Task
Mina Ghashami, Soumya Smruti Mishra
https://arxiv.org/abs/2405.10385 https://mastoxiv.page/@arXiv_csCL_bot/112472190479013167
- Watermarking Language Models with Error Correcting Codes
Patrick Chao, Yan Sun, Edgar Dobriban, Hamed Hassani
https://arxiv.org/abs/2406.10281 https://mastoxiv.page/@arXiv_csCR_bot/112636307340218522
- Learning to Control Unknown Strongly Monotone Games
Siddharth Chandak, Ilai Bistritz, Nicholas Bambos
https://arxiv.org/abs/2407.00575 https://mastoxiv.page/@arXiv_csMA_bot/112715733875586837
- Classification and reconstruction for single-pixel imaging with classical and quantum neural netw...
Sofya Manko, Dmitry Frolovtsev
https://arxiv.org/abs/2407.12506 https://mastoxiv.page/@arXiv_quantph_bot/112806295477530195
- Statistical Inference for Temporal Difference Learning with Linear Function Approximation
Weichen Wu, Gen Li, Yuting Wei, Alessandro Rinaldo
https://arxiv.org/abs/2410.16106 https://mastoxiv.page/@arXiv_statML_bot/113350611306532443
- Big data approach to Kazhdan-Lusztig polynomials
Abel Lacabanne, Daniel Tubbenhauer, Pedro Vaz
https://arxiv.org/abs/2412.01283 https://mastoxiv.page/@arXiv_mathRT_bot/113587812663608119
- MoEMba: A Mamba-based Mixture of Experts for High-Density EMG-based Hand Gesture Recognition
Mehran Shabanpour, Kasra Rad, Sadaf Khademi, Arash Mohammadi
https://arxiv.org/abs/2502.17457 https://mastoxiv.page/@arXiv_eessSP_bot/114069047434302054
- Tightening Optimality gap with confidence through conformal prediction
Miao Li, Michael Klamkin, Russell Bent, Pascal Van Hentenryck
https://arxiv.org/abs/2503.04071 https://mastoxiv.page/@arXiv_statML_bot/114120074927291283
- SEED: Towards More Accurate Semantic Evaluation for Visual Brain Decoding
Juhyeon Park, Peter Yongho Kim, Jiook Cha, Shinjae Yoo, Taesup Moon
https://arxiv.org/abs/2503.06437 https://mastoxiv.page/@arXiv_csCV_bot/114142690988862508
- How much does context affect the accuracy of AI health advice?
Prashant Garg, Thiemo Fetzer
https://arxiv.org/abs/2504.18310 https://mastoxiv.page/@arXiv_econGN_bot/114414380916957986
- Reproducing and Improving CheXNet: Deep Learning for Chest X-ray Disease Classification
Daniel J. Strick, Carlos Garcia, Anthony Huang, Thomas Gardos
https://arxiv.org/abs/2505.06646 https://mastoxiv.page/@arXiv_eessIV_bot/114499319986528625
- Sharp Gaussian approximations for Decentralized Federated Learning
Soham Bonnerjee, Sayar Karmakar, Wei Biao Wu
https://arxiv.org/abs/2505.08125 https://mastoxiv.page/@arXiv_statML_bot/114505047719395949
- HoloLLM: Multisensory Foundation Model for Language-Grounded Human Sensing and Reasoning
Chuhao Zhou, Jianfei Yang
https://arxiv.org/abs/2505.17645 https://mastoxiv.page/@arXiv_csCV_bot/114572928659057348
- A Copula Based Supervised Filter for Feature Selection in Diabetes Risk Prediction Using Machine ...
Agnideep Aich, Md Monzur Murshed, Sameera Hewage, Amanda Mayeaux
https://arxiv.org/abs/2505.22554 https://mastoxiv.page/@arXiv_statML_bot/114589983451462525
- Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
Florian F\"urrutter, Zohim Chandani, Ikko Hamamura, Hans J. Briegel, Gorka Mu\~noz-Gil
https://arxiv.org/abs/2506.01666 https://mastoxiv.page/@arXiv_quantph_bot/114618420761346125
toXiv_bot_toot

Machado: Venezuela could quintuple oil production under a democratic government (James Bikales/Politico)
https://www.politico.com/news/2026/03/24/machado-venezuela-oil-production-democracy-00843221
http://www.memeorandum.com/260324/p145#a260324p145

General framework for quantifying entanglement production in ultracold molecular collisions and chemical reactions
Adrien Devolder, Paul Brumer, Timur Tscherbul
https://arxiv.org/abs/2601.17144

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/6]:
- Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning
Chi-Pin Huang, Yunze Man, Zhiding Yu, Min-Hung Chen, Jan Kautz, Yu-Chiang Frank Wang, Fu-En Yang
https://arxiv.org/abs/2601.09708 https://mastoxiv.page/@arXiv_csCV_bot/115898618760721320
- Universality of Many-body Projected Ensemble for Learning Quantum Data Distribution
Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo, Hirotaka Oshima
https://arxiv.org/abs/2601.18637 https://mastoxiv.page/@arXiv_quantph_bot/115967001797773134
- FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning
Haozheng Luo, Zhuolin Jiang, Md Zahid Hasan, Yan Chen, Soumalya Sarkar
https://arxiv.org/abs/2601.19001 https://mastoxiv.page/@arXiv_csCL_bot/115972068838908815
- Analysis of Shuffling Beyond Pure Local Differential Privacy
Shun Takagi, Seng Pei Liew
https://arxiv.org/abs/2601.19154 https://mastoxiv.page/@arXiv_csDS_bot/115971701218309765
- CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models
Weining Fu, Kai Shu, Kui Xu, Qiangfeng Cliff Zhang
https://arxiv.org/abs/2602.02620
- XtraLight-MedMamba for Classification of Neoplastic Tubular Adenomas
Sultana, Afsar, Rahu, Singh, Shula, Combs, Forchetti, Asari
https://arxiv.org/abs/2602.04819
- Flow-Based Conformal Predictive Distributions
Trevor Harris
https://arxiv.org/abs/2602.07633 https://mastoxiv.page/@arXiv_statML_bot/116045671088130364
- GOT-Edit: Geometry-Aware Generic Object Tracking via Online Model Editing
Shih-Fang Chen, Jun-Cheng Chen, I-Hong Jhuo, Yen-Yu Lin
https://arxiv.org/abs/2602.08550 https://mastoxiv.page/@arXiv_csCV_bot/116046486984991360
- UI-Venus-1.5 Technical Report
Venus Team, et al.
https://arxiv.org/abs/2602.09082 https://mastoxiv.page/@arXiv_csCV_bot/116050980295461008
- The Wisdom of Many Queries: Complexity-Diversity Principle for Dense Retriever Training
Xincan Feng, Noriki Nishida, Yusuke Sakai, Yuji Matsumoto
https://arxiv.org/abs/2602.09448 https://mastoxiv.page/@arXiv_csIR_bot/116051022881293649
- Intent Laundering: AI Safety Datasets Are Not What They Seem
Shahriar Golchin, Marc Wetter
https://arxiv.org/abs/2602.16729 https://mastoxiv.page/@arXiv_csCR_bot/116101884238965526
- The Metaphysics We Train: A Heideggerian Reading of Machine Learning
Heman Shakeri
https://arxiv.org/abs/2602.19028 https://mastoxiv.page/@arXiv_csCY_bot/116125225694943789
- Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks
David Schmotz, Luca Beurer-Kellner, Sahar Abdelnabi, Maksym Andriushchenko
https://arxiv.org/abs/2602.20156 https://mastoxiv.page/@arXiv_csCR_bot/116125330557447048
- A Very Big Video Reasoning Suite
Maijunxian Wang, et al.
https://arxiv.org/abs/2602.20159 https://mastoxiv.page/@arXiv_csCV_bot/116125664801070747
toXiv_bot_toot

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/6]:
- Watermarking Degrades Alignment in Language Models: Analysis and Mitigation
Apurv Verma, NhatHai Phan, Shubhendu Trivedi
https://arxiv.org/abs/2506.04462 https://mastoxiv.page/@arXiv_csCL_bot/114635190037336859
- Sensory-Motor Control with Large Language Models via Iterative Policy Refinement
J\^onata Tyska Carvalho, Stefano Nolfi
https://arxiv.org/abs/2506.04867 https://mastoxiv.page/@arXiv_csAI_bot/114635187854195641
- ICE-ID: A Novel Historical Census Dataset for Longitudinal Identity Resolution
de Carvalho, Popov, Kaatee, Correia, Th\'orisson, Li, Bj\"ornsson, Sigur{\dh}arson, Dibangoye
https://arxiv.org/abs/2506.13792 https://mastoxiv.page/@arXiv_csAI_bot/114703312162525342
- Feedback-driven recurrent quantum neural network universality
Lukas Gonon, Rodrigo Mart\'inez-Pe\~na, Juan-Pablo Ortega
https://arxiv.org/abs/2506.16332 https://mastoxiv.page/@arXiv_quantph_bot/114732532383196043
- Programming by Backprop: An Instruction is Worth 100 Examples When Finetuning LLMs
Cook, Sapora, Ahmadian, Khan, Rocktaschel, Foerster, Ruis
https://arxiv.org/abs/2506.18777 https://mastoxiv.page/@arXiv_csAI_bot/114738213040759661
- Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning
Jiechen Chen, Bipin Rajendran, Osvaldo Simeone
https://arxiv.org/abs/2506.21324 https://mastoxiv.page/@arXiv_csNE_bot/114754367612728319
- Enjoying Non-linearity in Multinomial Logistic Bandits: A Minimax-Optimal Algorithm
Pierre Boudart (SIERRA), Pierre Gaillard (Thoth), Alessandro Rudi (PSL, DI-ENS, Inria)
https://arxiv.org/abs/2507.05306 https://mastoxiv.page/@arXiv_statML_bot/114822374525501660
- Characterizing State Space Model and Hybrid Language Model Performance with Long Context
Saptarshi Mitra, Rachid Karami, Haocheng Xu, Sitao Huang, Hyoukjun Kwon
https://arxiv.org/abs/2507.12442 https://mastoxiv.page/@arXiv_csAR_bot/114867589638074984
- Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Da...
Ayush Roy, Samin Enam, Jun Xia, Won Hwa Kim, Vishnu Suresh Lokhande
https://arxiv.org/abs/2507.19575 https://mastoxiv.page/@arXiv_csCV_bot/114935399825741861
- TASER: Table Agents for Schema-guided Extraction and Recommendation
Nicole Cho, Kirsty Fielding, William Watson, Sumitra Ganesh, Manuela Veloso
https://arxiv.org/abs/2508.13404 https://mastoxiv.page/@arXiv_csAI_bot/115060386723032051
- Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling
Nuno Costa, Julija Zavadlav
https://arxiv.org/abs/2509.02060 https://mastoxiv.page/@arXiv_qbioBM_bot/115139546511384706
- PCPO: Proportionate Credit Policy Optimization for Aligning Image Generation Models
Jeongjae Lee, Jong Chul Ye
https://arxiv.org/abs/2509.25774 https://mastoxiv.page/@arXiv_csCV_bot/115298580419859537
- Multi-hop Deep Joint Source-Channel Coding with Deep Hash Distillation for Semantically Aligned I...
Didrik Bergstr\"om, Deniz G\"und\"uz, Onur G\"unl\"u
https://arxiv.org/abs/2510.06868 https://mastoxiv.page/@arXiv_csIT_bot/115343320768797486
- MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile...
Chengshu Li, et al.
https://arxiv.org/abs/2510.18316 https://mastoxiv.page/@arXiv_csRO_bot/115416889485910123
- A Spectral Framework for Graph Neural Operators: Convergence Guarantees and Tradeoffs
Roxanne Holden, Luana Ruiz
https://arxiv.org/abs/2510.20954 https://mastoxiv.page/@arXiv_statML_bot/115445273121677005
- Breaking Agent Backbones: Evaluating the Security of Backbone LLMs in AI Agents
Bazinska, Mathys, Casucci, Rojas-Carulla, Davies, Souly, Pfister
https://arxiv.org/abs/2510.22620 https://mastoxiv.page/@arXiv_csCR_bot/115451397563132982
- Uncertainty Calibration of Multi-Label Bird Sound Classifiers
Raphael Schwinger, Ben McEwen, Vincent S. Kather, Ren\'e Heinrich, Lukas Rauch, Sven Tomforde
https://arxiv.org/abs/2511.08261 https://mastoxiv.page/@arXiv_csSD_bot/115535982708483824
- Two-dimensional RMSD projections for reaction path visualization and validation
Rohit Goswami (Institute IMX and Lab-COSMO, \'Ecole polytechnique f\'ed\'erale de Lausanne)
https://arxiv.org/abs/2512.07329 https://mastoxiv.page/@arXiv_physicschemph_bot/115688910885717951
- Distribution-informed Online Conformal Prediction
Dongjian Hu, Junxi Wu, Shu-Tao Xia, Changliang Zou
https://arxiv.org/abs/2512.07770 https://mastoxiv.page/@arXiv_statML_bot/115689281155541568
- Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss
Ang Lv, Jin Ma, Yiyuan Ma, Siyuan Qiao
https://arxiv.org/abs/2512.23447 https://mastoxiv.page/@arXiv_csCL_bot/115808311310246601
toXiv_bot_toot

Sparse Bayesian Deep Functional Learning with Structured Region Selection
Xiaoxian Zhu, Yingmeng Li, Shuangge Ma, Mengyun Wu
https://arxiv.org/abs/2602.20651 https://arxiv.org/pdf/2602.20651 https://arxiv.org/html/2602.20651
arXiv:2602.20651v1 Announce Type: new
Abstract: In modern applications such as ECG monitoring, neuroimaging, wearable sensing, and industrial equipment diagnostics, complex and continuously structured data are ubiquitous, presenting both challenges and opportunities for functional data analysis. However, existing methods face a critical trade-off: conventional functional models are limited by linearity, whereas deep learning approaches lack interpretable region selection for sparse effects. To bridge these gaps, we propose a sparse Bayesian functional deep neural network (sBayFDNN). It learns adaptive functional embeddings through a deep Bayesian architecture to capture complex nonlinear relationships, while a structured prior enables interpretable, region-wise selection of influential domains with quantified uncertainty. Theoretically, we establish rigorous approximation error bounds, posterior consistency, and region selection consistency. These results provide the first theoretical guarantees for a Bayesian deep functional model, ensuring its reliability and statistical rigor. Empirically, comprehensive simulations and real-world studies confirm the effectiveness and superiority of sBayFDNN. Crucially, sBayFDNN excels in recognizing intricate dependencies for accurate predictions and more precisely identifies functionally meaningful regions, capabilities fundamentally beyond existing approaches.
toXiv_bot_toot