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White-collar workers are quietly rebelling against AI as 80% outright refuse adoption mandates

submitted by /u/Effective-Trick-5795 [link] [comments]

Reddit - Artificial Intelligence · 1 min ·
Llms

[R] Forced Depth Consideration Reduces Type II Errors in LLM Self-Classification: Evidence from an Exploration Prompting Ablation Study - (200 trap prompts, 4 models, 8 Step-0 variants) [R]

LLM-Based task classifier tend to misroute prompts that look simple at first glance, but require deeper understanding - I call it "Type I...

Reddit - Machine Learning · 1 min ·
Machine Learning

Anyone have an S3-compatible store that actually saturates H100s without the AWS egress tax? [R]

We’re training on a cluster in Lambda Labs, but our main dataset ( over 40TB) is sitting in AWS S3. The egress fees are high, so we tried...

Reddit - Machine Learning · 1 min ·

All Content

[2602.15084] TokaMind: A Multi-Modal Transformer Foundation Model for Tokamak Plasma Dynamics
Llms

[2602.15084] TokaMind: A Multi-Modal Transformer Foundation Model for Tokamak Plasma Dynamics

TokaMind is a new open-source multi-modal transformer model designed for tokamak plasma dynamics, demonstrating superior performance on f...

arXiv - Machine Learning · 4 min ·
[2602.15158] da Costa and Tarski meet Goguen and Carnap: a novel approach for ontological heterogeneity based on consequence systems
Ai Agents

[2602.15158] da Costa and Tarski meet Goguen and Carnap: a novel approach for ontological heterogeneity based on consequence systems

This paper introduces a novel approach to ontological heterogeneity, integrating concepts from Carnapian-Goguenism and consequence system...

arXiv - AI · 3 min ·
[2602.15056] Reconstructing Carbon Monoxide Reanalysis with Machine Learning
Machine Learning

[2602.15056] Reconstructing Carbon Monoxide Reanalysis with Machine Learning

This article discusses a study on using machine learning to reconstruct carbon monoxide reanalysis data, addressing challenges posed by v...

arXiv - Machine Learning · 3 min ·
[2602.15040] SOON: Symmetric Orthogonal Operator Network for Global Subseasonal-to-Seasonal Climate Forecasting
Machine Learning

[2602.15040] SOON: Symmetric Orthogonal Operator Network for Global Subseasonal-to-Seasonal Climate Forecasting

The paper introduces the Symmetric Orthogonal Operator Network (SOON) for improved global Subseasonal-to-Seasonal climate forecasting, ad...

arXiv - Machine Learning · 3 min ·
[2602.15112] ResearchGym: Evaluating Language Model Agents on Real-World AI Research
Llms

[2602.15112] ResearchGym: Evaluating Language Model Agents on Real-World AI Research

ResearchGym introduces a benchmark for evaluating AI agents in real-world research scenarios, revealing significant performance gaps and ...

arXiv - AI · 4 min ·
[2306.17652] Accurate 2D Reconstruction for PET Scanners based on the Analytical White Image Model
Machine Learning

[2306.17652] Accurate 2D Reconstruction for PET Scanners based on the Analytical White Image Model

This paper presents a mathematical model for accurate 2D reconstruction in PET scanners, utilizing an Analytical White Image Model to enh...

arXiv - Machine Learning · 4 min ·
[2602.15067] Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis
Machine Learning

[2602.15067] Attention-gated U-Net model for semantic segmentation of brain tumors and feature extraction for survival prognosis

The article presents an Attention-Gated U-Net model for semantic segmentation of brain tumors, enhancing treatment planning through impro...

arXiv - AI · 3 min ·
[2602.15823] CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing
Llms

[2602.15823] CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing

CrispEdit introduces a novel algorithm for editing large language models (LLMs) that preserves their capabilities while allowing for targ...

arXiv - AI · 3 min ·
[2602.15820] Stabilizing Test-Time Adaptation of High-Dimensional Simulation Surrogates via D-Optimal Statistics
Machine Learning

[2602.15820] Stabilizing Test-Time Adaptation of High-Dimensional Simulation Surrogates via D-Optimal Statistics

This article presents a framework for Test-Time Adaptation (TTA) of high-dimensional simulation surrogates using D-optimal statistics, ad...

arXiv - Machine Learning · 3 min ·
[2602.15752] Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching
Machine Learning

[2602.15752] Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching

This paper introduces a new approach to two-sided matching platforms, focusing on maximizing user retention rather than just match quanti...

arXiv - Machine Learning · 4 min ·
[2602.15750] UrbanVerse: Learning Urban Region Representation Across Cities and Tasks
Machine Learning

[2602.15750] UrbanVerse: Learning Urban Region Representation Across Cities and Tasks

UrbanVerse proposes a novel model for urban region representation learning that generalizes across cities and tasks, enhancing urban anal...

arXiv - AI · 4 min ·
[2602.15740] MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis
Machine Learning

[2602.15740] MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis

The paper presents the MRC-GAT, a novel Meta-Relational Copula-Based Graph Attention Network designed for accurate and interpretable Alzh...

arXiv - AI · 4 min ·
[2602.15711] Random Wavelet Features for Graph Kernel Machines
Nlp

[2602.15711] Random Wavelet Features for Graph Kernel Machines

This paper introduces randomized spectral node embeddings for graph kernel machines, enhancing node similarity estimation while improving...

arXiv - AI · 3 min ·
[2602.15677] CAMEL: An ECG Language Model for Forecasting Cardiac Events
Llms

[2602.15677] CAMEL: An ECG Language Model for Forecasting Cardiac Events

CAMEL is a novel ECG language model designed to forecast cardiac events by leveraging advanced machine learning techniques, achieving sta...

arXiv - Machine Learning · 4 min ·
[2602.15649] Continuous-Time Piecewise-Linear Recurrent Neural Networks
Machine Learning

[2602.15649] Continuous-Time Piecewise-Linear Recurrent Neural Networks

This article presents Continuous-Time Piecewise-Linear Recurrent Neural Networks (cPLRNNs), a novel approach to modeling dynamical system...

arXiv - Machine Learning · 4 min ·
[2602.15648] Guided Diffusion by Optimized Loss Functions on Relaxed Parameters for Inverse Material Design
Generative Ai

[2602.15648] Guided Diffusion by Optimized Loss Functions on Relaxed Parameters for Inverse Material Design

This paper presents a novel method for inverse material design using guided diffusion and optimized loss functions, addressing challenges...

arXiv - Machine Learning · 4 min ·
[2602.15637] The Stationarity Bias: Stratified Stress-Testing for Time-Series Imputation in Regulated Dynamical Systems
Ai Safety

[2602.15637] The Stationarity Bias: Stratified Stress-Testing for Time-Series Imputation in Regulated Dynamical Systems

The paper discusses the 'Stationarity Bias' in time-series imputation, proposing a 'Stratified Stress-Test' to evaluate methods under dif...

arXiv - Machine Learning · 4 min ·
[2602.15603] Symbolic recovery of PDEs from measurement data
Machine Learning

[2602.15603] Symbolic recovery of PDEs from measurement data

This paper explores the symbolic recovery of partial differential equations (PDEs) from measurement data, highlighting a neural network a...

arXiv - Machine Learning · 4 min ·
[2602.15617] DNN-Enabled Multi-User Beamforming for Throughput Maximization under Adjustable Fairness
Machine Learning

[2602.15617] DNN-Enabled Multi-User Beamforming for Throughput Maximization under Adjustable Fairness

This paper presents a DNN-based approach to optimize multi-user beamforming in wireless communications, balancing throughput and fairness...

arXiv - Machine Learning · 3 min ·
[2602.15602] Certified Per-Instance Unlearning Using Individual Sensitivity Bounds
Machine Learning

[2602.15602] Certified Per-Instance Unlearning Using Individual Sensitivity Bounds

This article presents a novel approach to certified machine unlearning through adaptive per-instance noise calibration, significantly red...

arXiv - Machine Learning · 3 min ·
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