@cdarwin@c.imGoogle today announced an early preview of #WebMCP,
a new protocol that defines how AI agents interact with websites.
“WebMCP aims to provide a standard way for exposing structured tools, ensuring AI agents can perform actions on your side with increased speed, reliability, and precision,” wroteAndré Cipriani Bandarra from Google.
WebMCP lets developers tell large language models exactly …
@kornel@mastodon.social@… You seem to apply a strict definition of "safe"/"secure" meaning nothing counts below absolute perfection, which is more of an argument about linguistic/semantics of the wording used, rather than the technical benefits of the language.
Rust has features that broadly improve quality and reliability too, eg. sum types help catch mistakes in sta…
@NFL@darktundra.xyzWhy Alabama WR Germie Bernard might be the safest pick in the 2026 NFL Draft
https://www.cbssports.com/nfl/news/alabama-wr-germie-bernard-2026-nfl-draft/
@danyork@mastodon.socialNext up a talk about "reliability engineering challenges in networking for AI"...
#IETF125 #IETF #AI #Reliability
@mia@hcommons.socialA 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://…
@datascience@genomic.socialDiscover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out:
@mgorny@social.treehouse.systemsAlways grateful for the new levels of stability and reliability that #RustLang rewrites of #Python stuff bring.
> a = Tibs.from_i(-1, 128)
^^^^^^^^^^^^^^^^^^^^
E pyo3_runtime.PanicException: attempt to negate with overflow
https://github.com/scott-griffiths/tibs/issues/1
@joxean@mastodon.socialFrench nuclear 'umbrella' for Europe now increasingly discussed among allies.
French President Emmanuel Macron will deliver a speech on nuclear deterrence on Monday, with Europeans expected to pay close attention amid threats from Russia and growing doubts over the United States' reliability as a NATO ally.
@Techmeme@techhub.socialHeroku says it is transitioning to a "sustaining engineering model", as it focuses on "helping organizations build and deploy enterprise-grade AI" (Nitin T Bhat/Heroku)
https://www.heroku.com/blog/an-update-on-heroku/
@gwire@mastodon.socialRE: https://mastodon.social/@ispreview/116377243959305777
The high position of the UK in the Spamhaus C2 list represents a big failure of the UK's (now decade long) investment in cyber security, and a small win for the reliability of its networks.
@ripienaar@devco.socialBye bye Heroku https://www.heroku.com/blog/an-update-on-heroku/
@david_colquhoun@mstdn.socialThe #UCL World Cancer Day: Public Lecture streamed on Youtube is very (over)optimistic about the role of AI in drug design and in silico clinical trials (ouch). Computer people always overestimate the reliability of data. One day, perhaps (and perhaps not).
https://www…
@catsalad@infosec.exchangeRE: https://mstdn.social/@osnews/116302748664313031
Today, Microsoft is excited to announce a significant step forward in our ongoing commitment to Windows security and system reliability: the removal of trust for all drivers. This update will help protect our…
@HeidiSeibold@fosstodon.orgWith #LoveReplicationsWeek just around the corner, let's talk about the new journal in the field: Replication Research (R2)
Repeating important research is an important building block of improving the reliability of research. R2 is turning this into a rewarding task by creating a venue to get these important studies published.
It's a journal aligned with the value…
@hanno@mastodon.socialWhy does this keep happening? Grammarly recently introduced an AI chat, and also, their spell-checking service, which previously was their main product, suffers from constant reliability issues since them. I'm paying for this service, because it used to be good, I never asked for an AI chat. I can go to ChatGPT, or Google, which used to be a search engine and is now an AI chat with a worse version of the search engine attached. Not that I'd want any of that.
@socallinuxexpo@social.linux.pizzaNathan Handler will speak on 'Building a Unified Cloud Inventory for Reliability: Lessons from Using CloudQuery' as part of our General track at SCaLE 23x. Full details: https://www.socallinuxexpo.org/scale/23x
@primonatura@mstdn.social"Electric cars can make power grids more reliable (and earn owners money)—so why aren't we doing that?"
#EV #ElectricVehicles #cars
@Techmeme@techhub.socialSources: multiple federal agencies raised concerns about Grok's safety and reliability in recent months, before DOD approved Grok for use in classified settings (Wall Street Journal)
https://www.wsj.com/politics/national-security/elon…
@philip@mastodon.mallegolhansen.com“You need to let go of being a perfectionist”…
Yeah, because the software industry is sure *known* for our bang on reliability and stability right? We’ve been haunted by being too focused on perfection for too long!
Who cares if ~10% of insurance claims are incorrectly denied, or pedestrians run over by our autonomous vehicles.
That’s the cost of doing business baby!
@arXiv_csLG_bot@mastoxiv.pagePIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis
Kunyu Zhang, Yanwu Yang, Jing Zhang, Xiangjie Shi, Shujian Yu
https://arxiv.org/abs/2602.21046 https://arxiv.org/pdf/2602.21046 https://arxiv.org/html/2602.21046
arXiv:2602.21046v1 Announce Type: new
Abstract: Recent deep learning methods for fMRI-based diagnosis have achieved promising accuracy by modeling functional connectivity networks. However, standard approaches often struggle with noisy interactions, and conventional post-hoc attribution methods may lack reliability, potentially highlighting dataset-specific artifacts. To address these challenges, we introduce PIME, an interpretable framework that bridges intrinsic interpretability with minimal-sufficient subgraph optimization by integrating prototype-based classification and consistency training with structural perturbations during learning. This encourages a structured latent space and enables Monte Carlo Tree Search (MCTS) under a prototype-consistent objective to extract compact minimal-sufficient explanatory subgraphs post-training. Experiments on three benchmark fMRI datasets demonstrate that PIME achieves state-of-the-art performance. Furthermore, by constraining the search space via learned prototypes, PIME identifies critical brain regions that are consistent with established neuroimaging findings. Stability analysis shows 90% reproducibility and consistent explanations across atlases.
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@arXiv_physicsaccph_bot@mastoxiv.pageToward a Fully Autonomous, AI-Native Particle Accelerator
Chris Tennant
https://arxiv.org/abs/2602.17536 https://arxiv.org/pdf/2602.17536 https://arxiv.org/html/2602.17536
arXiv:2602.17536v1 Announce Type: new
Abstract: This position paper presents a vision for self-driving particle accelerators that operate autonomously with minimal human intervention. We propose that future facilities be designed through artificial intelligence (AI) co-design, where AI jointly optimizes the accelerator lattice, diagnostics, and science application from inception to maximize performance while enabling autonomous operation. Rather than retrofitting AI onto human-centric systems, we envision facilities designed from the ground up as AI-native platforms. We outline nine critical research thrusts spanning agentic control architectures, knowledge integration, adaptive learning, digital twins, health monitoring, safety frameworks, modular hardware design, multimodal data fusion, and cross-domain collaboration. This roadmap aims to guide the accelerator community toward a future where AI-driven design and operation deliver unprecedented science output and reliability.
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@primonatura@mstdn.social"Study finds much lower-than-expected degradation in 1980s and 1990s solar modules"
#Solar #SolarPower #Energy #Renewables
@arXiv_csLG_bot@mastoxiv.pageOn Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning
Andreas Tritsarolis, Gil Sampaio, Nikos Pelekis, Yannis Theodoridis
https://arxiv.org/abs/2602.20782 https://arxiv.org/pdf/2602.20782 https://arxiv.org/html/2602.20782
arXiv:2602.20782v1 Announce Type: new
Abstract: The wide spread of new energy resources, smart devices, and demand side management strategies has motivated several analytics operations, from infrastructure load modeling to user behavior profiling. Energy Demand Forecasting (EDF) of Electric Vehicle Supply Equipments (EVSEs) is one of the most critical operations for ensuring efficient energy management and sustainability, since it enables utility providers to anticipate energy/power demand, optimize resource allocation, and implement proactive measures to improve grid reliability. However, accurate EDF is a challenging problem due to external factors, such as the varying user routines, weather conditions, driving behaviors, unknown state of charge, etc. Furthermore, as concerns and restrictions about privacy and sustainability have grown, training data has become increasingly fragmented, resulting in distributed datasets scattered across different data silos and/or edge devices, calling for federated learning solutions. In this paper, we investigate different well-established time series forecasting methodologies to address the EDF problem, from statistical methods (the ARIMA family) to traditional machine learning models (such as XGBoost) and deep neural networks (GRU and LSTM). We provide an overview of these methods through a performance comparison over four real-world EVSE datasets, evaluated under both centralized and federated learning paradigms, focusing on the trade-offs between forecasting fidelity, privacy preservation, and energy overheads. Our experimental results demonstrate, on the one hand, the superiority of gradient boosted trees (XGBoost) over statistical and NN-based models in both prediction accuracy and energy efficiency and, on the other hand, an insight that Federated Learning-enabled models balance these factors, offering a promising direction for decentralized energy demand forecasting.
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@arXiv_csLG_bot@mastoxiv.pageReplaced 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
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@arXiv_csLG_bot@mastoxiv.pageSparse 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.
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