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Trump officials may be encouraging banks to test Anthropic’s Mythos model | TechCrunch
Machine Learning

Trump officials may be encouraging banks to test Anthropic’s Mythos model | TechCrunch

The report is particularly surprising since the Department of Defense recently declared Anthropic a supply-chain risk.

TechCrunch - AI · 3 min ·
Machine Learning

Palantir CEO says AI 'will destroy' humanities jobs, but there will be 'more than enough jobs' for people with vocational training

submitted by /u/esporx [link] [comments]

Reddit - Artificial Intelligence · 1 min ·
Llms

Frameworks For Supporting LLM/Agentic Benchmarking [P]

I think the way we are approaching benchmarking is a bit problematic. From reading about how frontier labs benchmark their models, they e...

Reddit - Machine Learning · 1 min ·

All Content

[2603.23668] Energy Efficient Software Hardware CoDesign for Machine Learning: From TinyML to Large Language Models
Llms

[2603.23668] Energy Efficient Software Hardware CoDesign for Machine Learning: From TinyML to Large Language Models

Abstract page for arXiv paper 2603.23668: Energy Efficient Software Hardware CoDesign for Machine Learning: From TinyML to Large Language...

arXiv - Machine Learning · 3 min ·
[2603.23640] LLM Inference at the Edge: Mobile, NPU, and GPU Performance Efficiency Trade-offs Under Sustained Load
Llms

[2603.23640] LLM Inference at the Edge: Mobile, NPU, and GPU Performance Efficiency Trade-offs Under Sustained Load

Abstract page for arXiv paper 2603.23640: LLM Inference at the Edge: Mobile, NPU, and GPU Performance Efficiency Trade-offs Under Sustain...

arXiv - Machine Learning · 4 min ·
[2603.23611] LLMORPH: Automated Metamorphic Testing of Large Language Models
Llms

[2603.23611] LLMORPH: Automated Metamorphic Testing of Large Language Models

Abstract page for arXiv paper 2603.23611: LLMORPH: Automated Metamorphic Testing of Large Language Models

arXiv - Machine Learning · 4 min ·
[2603.23576] Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM
Llms

[2603.23576] Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM

Abstract page for arXiv paper 2603.23576: Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM

arXiv - Machine Learning · 3 min ·
[2603.23547] PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors for Nonlinear ICA
Machine Learning

[2603.23547] PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors for Nonlinear ICA

Abstract page for arXiv paper 2603.23547: PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors ...

arXiv - Machine Learning · 4 min ·
[2603.23544] DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reduction
Machine Learning

[2603.23544] DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio Reduction

Abstract page for arXiv paper 2603.23544: DeepOFW: Deep Learning-Driven OFDM-Flexible Waveform Modulation for Peak-to-Average Power Ratio...

arXiv - Machine Learning · 4 min ·
[2603.23539] PLDR-LLMs Reason At Self-Organized Criticality
Llms

[2603.23539] PLDR-LLMs Reason At Self-Organized Criticality

Abstract page for arXiv paper 2603.23539: PLDR-LLMs Reason At Self-Organized Criticality

arXiv - Machine Learning · 3 min ·
[2603.23534] Not All Pretraining are Created Equal: Threshold Tuning and Class Weighting for Imbalanced Polarization Tasks in Low-Resource Settings
Machine Learning

[2603.23534] Not All Pretraining are Created Equal: Threshold Tuning and Class Weighting for Imbalanced Polarization Tasks in Low-Resource Settings

Abstract page for arXiv paper 2603.23534: Not All Pretraining are Created Equal: Threshold Tuning and Class Weighting for Imbalanced Pola...

arXiv - Machine Learning · 3 min ·
[2603.23530] Did You Forget What I Asked? Prospective Memory Failures in Large Language Models
Llms

[2603.23530] Did You Forget What I Asked? Prospective Memory Failures in Large Language Models

Abstract page for arXiv paper 2603.23530: Did You Forget What I Asked? Prospective Memory Failures in Large Language Models

arXiv - Machine Learning · 3 min ·
[2603.23514] DepthCharge: A Domain-Agnostic Framework for Measuring Depth-Dependent Knowledge in Large Language Models
Llms

[2603.23514] DepthCharge: A Domain-Agnostic Framework for Measuring Depth-Dependent Knowledge in Large Language Models

Abstract page for arXiv paper 2603.23514: DepthCharge: A Domain-Agnostic Framework for Measuring Depth-Dependent Knowledge in Large Langu...

arXiv - Machine Learning · 4 min ·
[2603.23507] Beyond Masks: Efficient, Flexible Diffusion Language Models via Deletion-Insertion Processes
Llms

[2603.23507] Beyond Masks: Efficient, Flexible Diffusion Language Models via Deletion-Insertion Processes

Abstract page for arXiv paper 2603.23507: Beyond Masks: Efficient, Flexible Diffusion Language Models via Deletion-Insertion Processes

arXiv - Machine Learning · 4 min ·
[2603.24594] Polynomial Speedup in Diffusion Models with the Multilevel Euler-Maruyama Method
Machine Learning

[2603.24594] Polynomial Speedup in Diffusion Models with the Multilevel Euler-Maruyama Method

Abstract page for arXiv paper 2603.24594: Polynomial Speedup in Diffusion Models with the Multilevel Euler-Maruyama Method

arXiv - Machine Learning · 4 min ·
[2603.24587] DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving
Machine Learning

[2603.24587] DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving

Abstract page for arXiv paper 2603.24587: DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving

arXiv - Machine Learning · 3 min ·
[2603.24562] Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction
Llms

[2603.24562] Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction

Abstract page for arXiv paper 2603.24562: Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction

arXiv - Machine Learning · 4 min ·
[2603.24533] UI-Voyager: A Self-Evolving GUI Agent Learning via Failed Experience
Llms

[2603.24533] UI-Voyager: A Self-Evolving GUI Agent Learning via Failed Experience

Abstract page for arXiv paper 2603.24533: UI-Voyager: A Self-Evolving GUI Agent Learning via Failed Experience

arXiv - Machine Learning · 4 min ·
[2603.24524] No Single Metric Tells the Whole Story: A Multi-Dimensional Evaluation Framework for Uncertainty Attributions
Machine Learning

[2603.24524] No Single Metric Tells the Whole Story: A Multi-Dimensional Evaluation Framework for Uncertainty Attributions

Abstract page for arXiv paper 2603.24524: No Single Metric Tells the Whole Story: A Multi-Dimensional Evaluation Framework for Uncertaint...

arXiv - Machine Learning · 4 min ·
[2603.24518] TuneShift-KD: Knowledge Distillation and Transfer for Fine-tuned Models
Llms

[2603.24518] TuneShift-KD: Knowledge Distillation and Transfer for Fine-tuned Models

Abstract page for arXiv paper 2603.24518: TuneShift-KD: Knowledge Distillation and Transfer for Fine-tuned Models

arXiv - Machine Learning · 4 min ·
[2603.24517] AVO: Agentic Variation Operators for Autonomous Evolutionary Search
Llms

[2603.24517] AVO: Agentic Variation Operators for Autonomous Evolutionary Search

Abstract page for arXiv paper 2603.24517: AVO: Agentic Variation Operators for Autonomous Evolutionary Search

arXiv - Machine Learning · 4 min ·
[2603.24503] Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling
Machine Learning

[2603.24503] Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling

Abstract page for arXiv paper 2603.24503: Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Networ...

arXiv - Machine Learning · 3 min ·
[2603.24500] Project and Generate: Divergence-Free Neural Operators for Incompressible Flows
Machine Learning

[2603.24500] Project and Generate: Divergence-Free Neural Operators for Incompressible Flows

Abstract page for arXiv paper 2603.24500: Project and Generate: Divergence-Free Neural Operators for Incompressible Flows

arXiv - Machine Learning · 3 min ·
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