Tootfinder

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
Jikai Jin, Vasilis Syrgkanis
https://arxiv.org/abs/2512.17341 https://mastoxiv.page/@arXiv_statML_bot/115762312049963700
- Timely Information Updating for Mobile Devices Without and With ML Advice
Yu-Pin Hsu, Yi-Hsuan Tseng
https://arxiv.org/abs/2512.17381 https://mastoxiv.page/@arXiv_csNI_bot/115762180316858485
- SWE-Bench : A Framework for the Scalable Generation of Software Engineering Benchmarks from Open...
Wang, Ramalho, Celestino, Pham, Liu, Sinha, Portillo, Osunwa, Maduekwe
https://arxiv.org/abs/2512.17419 https://mastoxiv.page/@arXiv_csSE_bot/115762487015279852
- Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing
Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
https://arxiv.org/abs/2512.17426 https://mastoxiv.page/@arXiv_statML_bot/115762346108219997
- MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic s...
Jon Muhovi\v{c}, Janez Per\v{s}
https://arxiv.org/abs/2512.17450 https://mastoxiv.page/@arXiv_csCV_bot/115762717053353674
- When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issu...
Emmanuel Charleson Dapaah, Jens Grabowski
https://arxiv.org/abs/2512.17460 https://mastoxiv.page/@arXiv_csSE_bot/115762500123147574
- Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
Olivier Jeunen, Schaun Wheeler
https://arxiv.org/abs/2512.17462 https://mastoxiv.page/@arXiv_csIR_bot/115762430673347625
- Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks
Irched Chafaa, Giacomo Bacci, Luca Sanguinetti
https://arxiv.org/abs/2512.17466 https://mastoxiv.page/@arXiv_eessSY_bot/115762336277179643
- Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, J{\o}rn Eirik Betten, Helge Spieker
https://arxiv.org/abs/2512.17470 https://mastoxiv.page/@arXiv_csAI_bot/115762556506696539
- Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
Atharva Awari, Nicolas Gillis, Arnaud Vandaele
https://arxiv.org/abs/2512.17473 https://mastoxiv.page/@arXiv_eessSP_bot/115762580078964235
- TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
Almustapha A. Wakili, Adamu Hussaini, Abubakar A. Musa, Woosub Jung, Wei Yu
https://arxiv.org/abs/2512.17488 https://mastoxiv.page/@arXiv_csCV_bot/115762726884307901
- Resource-efficient medical image classification for edge devices
Mahsa Lavaei, Zahra Abadi, Salar Beigzad, Alireza Maleki
https://arxiv.org/abs/2512.17515 https://mastoxiv.page/@arXiv_eessIV_bot/115762459510336799
- PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning i...
Brussee, Valkema, Weijer, Doeleman, Schrader, Kers
https://arxiv.org/abs/2512.17517 https://mastoxiv.page/@arXiv_csCV_bot/115762741957639051
- HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, et al.
https://arxiv.org/abs/2512.17534 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115762391350754768
- When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Sys...
Chondhekar, Murukuri, Vasani, Goyal, Badami, Rana, SN, Pandia, Katiyar, Jagadeesh, Gulati
https://arxiv.org/abs/2512.17562 https://mastoxiv.page/@arXiv_csSD_bot/115762423443170715
- Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
Lingxiao Zhao, Haoran Zhou, Yuezhi Che, Dazhao Cheng
https://arxiv.org/abs/2512.17574 https://mastoxiv.page/@arXiv_csDC_bot/115762425409322293
- SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation i...
N. A. Adarsh Pritam, Jeba Shiney O, Sanyam Jain
https://arxiv.org/abs/2512.17585 https://mastoxiv.page/@arXiv_eessIV_bot/115762479150695610
- MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection an...
Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Asif Rahman, Olukunle Kolade, Sasidhar Kunapuli
https://arxiv.org/abs/2512.17594 https://mastoxiv.page/@arXiv_csCR_bot/115762509298207765
- Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion De...
Menna Elgabry, Ali Hamdi
https://arxiv.org/abs/2512.17630 https://mastoxiv.page/@arXiv_csCL_bot/115762575512981257
- Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Effic...
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson
https://arxiv.org/abs/2512.17659 https://mastoxiv.page/@arXiv_statML_bot/115762554519447500
toXiv_bot_toot

Deep Sea Divers Recover Bell, Other Artifacts From Titanic’s Sunken Sister Ship–The Britannic https://www.goodnewsnetwork.org/deep-sea-divers-recover-bell-other-artifacts-from-titanics-sunken-sister-ship-the-britannic/

Deep Sea Divers Recover Bell, Other Artifacts From Titanic’s Sunken Sister Ship–The Britannic
Called the HMHS Britannic, it was one of three Olympic-class ocean liners built by the White Star Lines company in the early 20th century

We introduce the Remote Labor Index (RLI),
a broadly multi-sector benchmark comprising real-world, economically valuable remote-work projects
designed to evaluate end-to-end agent performance in practical settings.
Across evaluated frontier AI agent frameworks, performance sits near the floor,
with a maximum automation rate of 2.5% on RLI projects.

Series B, Episode 01 - Redemption
AVON: [Coming up behind him] Why Earth Sector?
BLAKE: Would you rather stay here?
AVON: You make them sound like the only alternatives.
https://blake.torpidity.net/m/201/559 B7B2

Ghost-free, gauge invariant SVT generalizations of Horndeski theory
S. Mironov, A. Shtennikova, M. Valencia-Villegas
https://arxiv.org/abs/2509.16850 https://

Ghost-free, gauge invariant SVT generalizations of Horndeski theory
We present a new type of Scalar-Vector-Tensor (SVT) theories with higher derivatives of all the fields in the action, but with second order equations of motion. The higher derivative vector field is invariant under a U(1) gauge transformation and the Scalar-Tensor sector corresponds to Horndeski theory. We also present a subclass of these SVT theories with 8 free functions of $π$ and $X$ where the speed of the tensor and vector modes is exactly the same. In particular, the Horndeski functions …

Decaying vector dark matter with low reheating temperature for KM3NeT signal and its impact on gravitational waves
Sarif Khan, Jongkuk Kim, Hyun Min Lee
https://arxiv.org/abs/2509.17129

Decaying vector dark matter with low reheating temperature for KM3NeT signal and its impact on gravitational waves
We propose a new model to explain the KM3NeT neutrino event through a low reheating scenario with a suppression in the GW spectrum originating from cosmic string networks. To achieve this, we extend the SM gauge sector by an abelian gauge symmetry and a singlet scalar. Once the abelian gauge symmetry spontaneously breaks, the extra gauge boson acquires mass and becomes a suitable Dark Matter (DM) candidate. Due to the kinetic mixing with the hypercharge gauge group, DM can decay into SM particl…

This widely-linked story is, I think, much ado about nothing. Anyone who's seen the inside of an Amazon warehouse immediately thinks “Shouldn’t robots be doing this?” and that includes every Amazon biz and engineering leader, starting like 25 years ago. There is no doubt that the whole warehouse sector would love to replace humans with robots. It’s hard. So what’s new? Is it possible LLM fairy dust will solve the problem? Unconvinced.

Amazon hopes to replace 600,000 US workers with robots, according to leaked documents
Amazon is leaning into automation plans that will enable the company to avoid hiring more than half a million US workers, according to leaked documents.

What's Not on the Plate? Rethinking Food Computing through Indigenous Indian Datasets
Pamir Gogoi, Neha Joshi, Ayushi Pandey, Deepthi Sudharsan, Saransh Kumar Gupta, Lipika Dey, Partha Pratim Das, Kalika Bali, Vivek Seshadri
https://arxiv.org/abs/2509.16286

What's Not on the Plate? Rethinking Food Computing through Indigenous Indian Datasets
This paper presents a multimodal dataset of 1,000 indigenous recipes from remote regions of India, collected through a participatory model involving first-time digital workers from rural areas. The project covers ten endangered language communities in six states. Documented using a dedicated mobile app, the data set includes text, images, and audio, capturing traditional food practices along with their ecological and cultural contexts. This initiative addresses gaps in food computing, such as t…

Cosmological constraints on Galileon dark energy with broken shift symmetry
William J. Wolf, Pedro G. Ferreira, Carlos Garc\'ia-Garc\'ia
https://arxiv.org/abs/2509.17586

Cosmological constraints on Galileon dark energy with broken shift symmetry
Current cosmological data seem to show that dark energy is evolving in time and that it possibly crossed the phantom divide in the past. So far the only theories that lead to such a behavior involve a non-trivial coupling between dark energy, in the form of a scalar field, and the gravitational or matter sector. We show that there is another possibility involving both a non-trivial kinetic sector in a cubic Galileon theory and a scalar field potential that breaks the Galileon shift symmetry, wh…

Explainability Needs in Agriculture: Exploring Dairy Farmers' User Personas
Mengisti Berihu Girmay, Jakob Droste, Hannah Deters, Joerg Doerr
https://arxiv.org/abs/2509.16249

Explainability Needs in Agriculture: Exploring Dairy Farmers' User Personas
Artificial Intelligence (AI) promises new opportunities across many domains, including agriculture. However, the adoption of AI systems in this sector faces several challenges. System complexity can impede trust, as farmers' livelihoods depend on their decision-making and they may reject opaque or hard-to-understand recommendations. Data privacy concerns also pose a barrier, especially when farmers lack transparency regarding who can access their data and for what purposes. This paper examine…