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Anthropic Teams Up With Its Rivals to Keep AI From Hacking Everything | WIRED
Llms

Anthropic Teams Up With Its Rivals to Keep AI From Hacking Everything | WIRED

The AI lab's Project Glasswing will bring together Apple, Google, and more than 45 other organizations. They'll use the new Claude Mythos...

Wired - AI · 7 min ·
Machine Learning

[for hire] Open for contracts – Veteran Data Scientist (AI / ML / OR) focused on delivering real‑world solutions.

Hi Reddit, I've spent 20 years working with data, and I've learned how to crack problems that AI systems struggle with. I've got a knack ...

Reddit - ML Jobs · 1 min ·
Llms

The public needs to control AI-run infrastructure, labor, education, and governance— NOT private actors

A lot of discussion around AI is becoming siloed, and I think that is dangerous. People in AI-focused spaces often talk as if the only qu...

Reddit - Artificial Intelligence · 1 min ·

All Content

[2603.24111] Toward a Multi-Layer ML-Based Security Framework for Industrial IoT
Machine Learning

[2603.24111] Toward a Multi-Layer ML-Based Security Framework for Industrial IoT

Abstract page for arXiv paper 2603.24111: Toward a Multi-Layer ML-Based Security Framework for Industrial IoT

arXiv - Machine Learning · 4 min ·
[2603.24083] Knowledge-Guided Manipulation Using Multi-Task Reinforcement Learning
Machine Learning

[2603.24083] Knowledge-Guided Manipulation Using Multi-Task Reinforcement Learning

Abstract page for arXiv paper 2603.24083: Knowledge-Guided Manipulation Using Multi-Task Reinforcement Learning

arXiv - Machine Learning · 4 min ·
[2603.24016] COVTrack++: Learning Open-Vocabulary Multi-Object Tracking from Continuous Videos via a Synergistic Paradigm
Machine Learning

[2603.24016] COVTrack++: Learning Open-Vocabulary Multi-Object Tracking from Continuous Videos via a Synergistic Paradigm

Abstract page for arXiv paper 2603.24016: COVTrack++: Learning Open-Vocabulary Multi-Object Tracking from Continuous Videos via a Synergi...

arXiv - Machine Learning · 4 min ·
[2603.24054] Hierarchical Spatial-Temporal Graph-Enhanced Model for Map-Matching
Machine Learning

[2603.24054] Hierarchical Spatial-Temporal Graph-Enhanced Model for Map-Matching

Abstract page for arXiv paper 2603.24054: Hierarchical Spatial-Temporal Graph-Enhanced Model for Map-Matching

arXiv - Machine Learning · 4 min ·
[2603.24041] Minimal Sufficient Representations for Self-interpretable Deep Neural Networks
Machine Learning

[2603.24041] Minimal Sufficient Representations for Self-interpretable Deep Neural Networks

Abstract page for arXiv paper 2603.24041: Minimal Sufficient Representations for Self-interpretable Deep Neural Networks

arXiv - Machine Learning · 3 min ·
[2603.23974] Machine vision with small numbers of detected photons per inference
Machine Learning

[2603.23974] Machine vision with small numbers of detected photons per inference

Abstract page for arXiv paper 2603.23974: Machine vision with small numbers of detected photons per inference

arXiv - Machine Learning · 4 min ·
[2603.23971] The Price Reversal Phenomenon: When Cheaper Reasoning Models End Up Costing More
Llms

[2603.23971] The Price Reversal Phenomenon: When Cheaper Reasoning Models End Up Costing More

Abstract page for arXiv paper 2603.23971: The Price Reversal Phenomenon: When Cheaper Reasoning Models End Up Costing More

arXiv - Machine Learning · 4 min ·
[2603.23943] ChargeFlow: Flow-Matching Refinement of Charge-Conditioned Electron Densities
Machine Learning

[2603.23943] ChargeFlow: Flow-Matching Refinement of Charge-Conditioned Electron Densities

Abstract page for arXiv paper 2603.23943: ChargeFlow: Flow-Matching Refinement of Charge-Conditioned Electron Densities

arXiv - Machine Learning · 3 min ·
[2603.23937] Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development
Llms

[2603.23937] Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development

Abstract page for arXiv paper 2603.23937: Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development

arXiv - Machine Learning · 4 min ·
[2603.23911] Self-Distillation for Multi-Token Prediction
Llms

[2603.23911] Self-Distillation for Multi-Token Prediction

Abstract page for arXiv paper 2603.23911: Self-Distillation for Multi-Token Prediction

arXiv - Machine Learning · 3 min ·
[2603.23933] ORACLE: Orchestrate NPC Daily Activities using Contrastive Learning with Transformer-CVAE
Machine Learning

[2603.23933] ORACLE: Orchestrate NPC Daily Activities using Contrastive Learning with Transformer-CVAE

Abstract page for arXiv paper 2603.23933: ORACLE: Orchestrate NPC Daily Activities using Contrastive Learning with Transformer-CVAE

arXiv - Machine Learning · 4 min ·
[2603.23873] The DeepXube Software Package for Solving Pathfinding Problems with Learned Heuristic Functions and Search
Machine Learning

[2603.23873] The DeepXube Software Package for Solving Pathfinding Problems with Learned Heuristic Functions and Search

Abstract page for arXiv paper 2603.23873: The DeepXube Software Package for Solving Pathfinding Problems with Learned Heuristic Functions...

arXiv - Machine Learning · 4 min ·
[2603.23914] Attention-aware Inference Optimizations for Large Vision-Language Models with Memory-efficient Decoding
Llms

[2603.23914] Attention-aware Inference Optimizations for Large Vision-Language Models with Memory-efficient Decoding

Abstract page for arXiv paper 2603.23914: Attention-aware Inference Optimizations for Large Vision-Language Models with Memory-efficient ...

arXiv - Machine Learning · 4 min ·
[2603.23835] Beyond Consistency: Inference for the Relative risk functional in Deep Nonparametric Cox Models
Machine Learning

[2603.23835] Beyond Consistency: Inference for the Relative risk functional in Deep Nonparametric Cox Models

Abstract page for arXiv paper 2603.23835: Beyond Consistency: Inference for the Relative risk functional in Deep Nonparametric Cox Models

arXiv - Machine Learning · 4 min ·
[2603.23822] How Vulnerable Are Edge LLMs?
Llms

[2603.23822] How Vulnerable Are Edge LLMs?

Abstract page for arXiv paper 2603.23822: How Vulnerable Are Edge LLMs?

arXiv - Machine Learning · 3 min ·
[2603.23821] Perturbation: A simple and efficient adversarial tracer for representation learning in language models
Llms

[2603.23821] Perturbation: A simple and efficient adversarial tracer for representation learning in language models

Abstract page for arXiv paper 2603.23821: Perturbation: A simple and efficient adversarial tracer for representation learning in language...

arXiv - Machine Learning · 3 min ·
[2603.23800] Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection
Llms

[2603.23800] Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection

Abstract page for arXiv paper 2603.23800: Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt ...

arXiv - Machine Learning · 4 min ·
[2603.23794] Sparse Autoencoders for Interpretable Medical Image Representation Learning
Llms

[2603.23794] Sparse Autoencoders for Interpretable Medical Image Representation Learning

Abstract page for arXiv paper 2603.23794: Sparse Autoencoders for Interpretable Medical Image Representation Learning

arXiv - Machine Learning · 3 min ·
[2603.23785] Retinal Disease Classification from Fundus Images using CNN Transfer Learning
Machine Learning

[2603.23785] Retinal Disease Classification from Fundus Images using CNN Transfer Learning

Abstract page for arXiv paper 2603.23785: Retinal Disease Classification from Fundus Images using CNN Transfer Learning

arXiv - Machine Learning · 3 min ·
[2603.23722] Dual-Gated Epistemic Time-Dilation: Autonomous Compute Modulation in Asynchronous MARL
Machine Learning

[2603.23722] Dual-Gated Epistemic Time-Dilation: Autonomous Compute Modulation in Asynchronous MARL

Abstract page for arXiv paper 2603.23722: Dual-Gated Epistemic Time-Dilation: Autonomous Compute Modulation in Asynchronous MARL

arXiv - Machine Learning · 4 min ·
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