explore | Grilled Cheese

UK says it will monitor AI-based medical devices continually #machinelearning #ai

GLM 5.3 by Z.ai is now generally available on Amazon Bedrock #machinelearning #ai

Claude Sonnet 5.5 vs. Opus 5.5: 42% cheaper and perfect on every run #machinelearning #ai

Announcing Amazon Nova 2.5 Sonic with improved reasoning for voice agents #machinelearning #ai

AI Agents Are Joining Your Microsoft 365 Tenant. Is Your Security Model Ready? #machinelearning #ai

🤖 Google is launching a one-stop Gemini agent for your work tasks

Google is launching a "universal" Gemini AI agent that can work across apps and devices in the background. The to...

https://tinyurl.com/28uyjqup #AINews #MachineLearning #CrustyTLDR

🤖 Anthropic bans ‘abusive or cruel behavior’ towards Claude

Anthropic is making changes to its usage policy for the first time in over a year to reflect new and high-risk cases of...

https://tinyurl.com/25j3ltka #AINews #MachineLearning #CrustyTLDR

OTel is an open telecom AI resource releasing datasets and 30 post-trained baselines for embedding, reranking, and language modeling tasks. Models have been downloaded over 16 million times since release, with…

#OpenSourceAI #AIResearch #TelecomAI #MachineLearning
https://arxiv.org/abs/2610.07766

New arXiv work shows rectified flow models can achieve ~Õ(ε⁻²) sample complexity, improving on prior ε⁻⁴ bounds for flow matching and matching the optimal rate for mean estimation. The gains come from rectified flows’…

#AI #MachineLearning #GenerativeModels #ArXiv
https://arxiv.org/abs/2601.20250

A 180B-parameter LLM topped legal reasoning benchmarks without ever seeing legal training data—raising questions about the…

https://dev.to/ai_openfree_b23025ef075cf/darwin-180b-rsi-tops-swiss-legal-reasoning-benchmarks-without-domain-specific-training-12m3

#AI #MachineLearning

Appleが発表した「Normalizing Trajectory Models (NTM)」：確率的軌跡モデリングの限界を突破する高効率生成AIアーキテクチャ

Appleが発表したNormalizing Trajectory Models (NTM)の技術解説。正確な尤度を維持しつつ数ステップでの高速生成を実現。

#GenerativeModels #NormalizingFlows #DiffusionModels #ComputerVision #MachineLearning

New research cracks 1-bit KV cache quantization for LLMs, slashing memory bottlenecks in long-context inference while doubling throughput. A practical step toward scaling inference efficiency.

https://arxiv.org/abs/2610.03027

#AI #MachineLearning

📰 New article by Giuseppe Angelo Porcelli, Mayank Gupta

Share GPU clusters across teams with isolation and fairness using Amazon SageMaker HyperPod

#AWS #AI #MachineLearning

Sub-1-Bit LLM Compression via Latent Factorization
Discussion | hackernews | Author: brainless

#MachineLearning

Comparing decision models against classifiers and generative LLMs on workflow, intent, emotion, and social-science tasks, a fine-tuned DeBERTa-v3-large leads labeled benchmarks while no decision model beats Jev…

#AI #DevTools #MachineLearning #SoftwareEngineering
https://arxiv.org/abs/2610.00346

🤖 r/MachineLearning

1行目: NVIDIA論文、ICML採択に疑義

2〜3行目: ロボット向け世界モデル「dreamDojo」がSpotlight選出。Cosmos 2.5派生で引用100件超も新規性に疑問の声。

🔗 https://www.reddit.com/r/MachineLearning/comments/1x0i6b5/nvidias_erroneous_paper_accepted_as_icmls/ #AI #人工知能 #MachineLearning

How do large language models “think”? Researchers are trying to uncover the hidden algorithms inside AI.

www.nexthorizon.space/2025/04/how-...

#AI #Science #Technology #Research #LLM #ChatGPT #MachineLearning #NLP #Interpretability #Data #Future #NeuralNets

SpAx makes LLM inference 3–5× faster on consumer GPUs by smartly mixing weight skipping, compression, and full reads based on activation magnitude. Real gains for practitioners running models on limited hardware.

https://arxiv.org/abs/2610.02598

#AI #MachineLearning