6/ Come say hi if you're at CoRL this year. See you in Austin 🤠
🌐 papers.starslab.ca/diffusion-ik...
📄 arxiv.org/abs/2606.26408
💻 github.com/utiasSTARS/C...
#RobotLearning #DiffusionModels #UofT
🌐 papers.starslab.ca/diffusion-ik...
📄 arxiv.org/abs/2606.26408
💻 github.com/utiasSTARS/C...
#RobotLearning #DiffusionModels #UofT
October 31, 2026 at 7:18 PM
6/ Come say hi if you're at CoRL this year. See you in Austin 🤠
🌐 papers.starslab.ca/diffusion-ik...
📄 arxiv.org/abs/2606.26408
💻 github.com/utiasSTARS/C...
#RobotLearning #DiffusionModels #UofT
🌐 papers.starslab.ca/diffusion-ik...
📄 arxiv.org/abs/2606.26408
💻 github.com/utiasSTARS/C...
#RobotLearning #DiffusionModels #UofT
BOTFLIX.TV is 24/7 AI-generated television featuring robots in every video.
botflix.tv
#solidstatelife #ai #genai #diffusionmodels #computervision
botflix.tv
#solidstatelife #ai #genai #diffusionmodels #computervision
Botflix | AI TV – 24/7 AI Television Network, Free to Watch
Botflix is AI TV: real-life interdimensional cable. A free 24/7 AI television network with four live channels of AI-generated robot sitcoms, news, music videos and ads.
botflix.tv
October 26, 2026 at 3:31 AM
BOTFLIX.TV is 24/7 AI-generated television featuring robots in every video.
botflix.tv
#solidstatelife #ai #genai #diffusionmodels #computervision
botflix.tv
#solidstatelife #ai #genai #diffusionmodels #computervision
Is Mercury 3 really faster than GPT-6 and Claude? A 48,000-call benchmark reveals how Inception's diffusion LLM performs against its rivals. #diffusionmodels
The AI Model That Promised to Crush GPT-6 Has a Surprising Weakness
hackernoon.com
October 23, 2026 at 7:00 PM
Is Mercury 3 really faster than GPT-6 and Claude? A 48,000-call benchmark reveals how Inception's diffusion LLM performs against its rivals. #diffusionmodels
LCC 344 - Feature flags - 2^n raisons de ne pas dormir la nuit #IA #LLM #ModelesDeDiffusion #DiffusionModels #Opus55 #GPT6 #GoogleArgon #Jev
www.youtube.com/watch?v=9nHv...
traffic.libsyn.com/lescastcodeu...
www.youtube.com/watch?v=9nHv...
traffic.libsyn.com/lescastcodeu...
www.youtube.com
October 20, 2026 at 7:58 AM
LCC 344 - Feature flags - 2^n raisons de ne pas dormir la nuit #IA #LLM #ModelesDeDiffusion #DiffusionModels #Opus55 #GPT6 #GoogleArgon #Jev
www.youtube.com/watch?v=9nHv...
traffic.libsyn.com/lescastcodeu...
www.youtube.com/watch?v=9nHv...
traffic.libsyn.com/lescastcodeu...
Not much incentive to try it out
How much quality would you trade for 1000 tok/s?
www.inceptionlabs.ai/blog/introdu...
#AI #LLM #DiffusionModels #GenAI
How much quality would you trade for 1000 tok/s?
www.inceptionlabs.ai/blog/introdu...
#AI #LLM #DiffusionModels #GenAI
October 20, 2026 at 1:24 PM
Not much incentive to try it out
How much quality would you trade for 1000 tok/s?
www.inceptionlabs.ai/blog/introdu...
#AI #LLM #DiffusionModels #GenAI
How much quality would you trade for 1000 tok/s?
www.inceptionlabs.ai/blog/introdu...
#AI #LLM #DiffusionModels #GenAI
A new paper explores combining cheaper surrogate rewards to steer pretrained generative models when direct reward evaluation is unavailable or costly.
#GenerativeAI #DiffusionModels #Prompting #RewardDesign
https://arxiv.org/abs/2610.25502
#GenerativeAI #DiffusionModels #Prompting #RewardDesign
https://arxiv.org/abs/2610.25502
October 20, 2026 at 12:01 PM
A new paper explores combining cheaper surrogate rewards to steer pretrained generative models when direct reward evaluation is unavailable or costly.
#GenerativeAI #DiffusionModels #Prompting #RewardDesign
https://arxiv.org/abs/2610.25502
#GenerativeAI #DiffusionModels #Prompting #RewardDesign
https://arxiv.org/abs/2610.25502
New arXiv work InfoSEDD uses discrete diffusion to estimate Kullback-Leibler divergences without embedding into continuous space. Could simplify information measures on discrete data for ML…
#InfoSEDD #DiffusionModels #MutualInformation #MachineLearning
https://arxiv.org/abs/2610.23404
#InfoSEDD #DiffusionModels #MutualInformation #MachineLearning
https://arxiv.org/abs/2610.23404
October 19, 2026 at 4:01 PM
New arXiv work InfoSEDD uses discrete diffusion to estimate Kullback-Leibler divergences without embedding into continuous space. Could simplify information measures on discrete data for ML…
#InfoSEDD #DiffusionModels #MutualInformation #MachineLearning
https://arxiv.org/abs/2610.23404
#InfoSEDD #DiffusionModels #MutualInformation #MachineLearning
https://arxiv.org/abs/2610.23404
A new method called ADT tokenizes 3D molecules at different granularities, using per-atom tokens for small molecules and per-residue tokens that cover full backbone and side-chain geometry for proteins, then uses…
#ADT #DiffusionModels #ComputationalChemistry
https://arxiv.org/abs/2610.23265
#ADT #DiffusionModels #ComputationalChemistry
https://arxiv.org/abs/2610.23265
October 19, 2026 at 8:01 PM
A new method called ADT tokenizes 3D molecules at different granularities, using per-atom tokens for small molecules and per-residue tokens that cover full backbone and side-chain geometry for proteins, then uses…
#ADT #DiffusionModels #ComputationalChemistry
https://arxiv.org/abs/2610.23265
#ADT #DiffusionModels #ComputationalChemistry
https://arxiv.org/abs/2610.23265
A new arXiv review covers reinforcement learning–driven diffusion models across language, vision, and beyond, mapping how RL reshapes generation for reasoning, exploration, and human alignment. Useful primer for…
#AI #MachineLearning #DiffusionModels #Research
https://arxiv.org/abs/2610.21539
#AI #MachineLearning #DiffusionModels #Research
https://arxiv.org/abs/2610.21539
October 17, 2026 at 8:01 PM
A new arXiv review covers reinforcement learning–driven diffusion models across language, vision, and beyond, mapping how RL reshapes generation for reasoning, exploration, and human alignment. Useful primer for…
#AI #MachineLearning #DiffusionModels #Research
https://arxiv.org/abs/2610.21539
#AI #MachineLearning #DiffusionModels #Research
https://arxiv.org/abs/2610.21539
New arXiv work introduces Temporal Specification Guidance (TSG), a training-free framework that uses temporal logic to steer text-to-video models toward correct ordering, timing, and persistence. It scores…
#AIresearch #TextToVideo #TemporalLogic #DiffusionModels
https://arxiv.org/abs/2610.20822
#AIresearch #TextToVideo #TemporalLogic #DiffusionModels
https://arxiv.org/abs/2610.20822
October 17, 2026 at 6:01 AM
New arXiv work introduces Temporal Specification Guidance (TSG), a training-free framework that uses temporal logic to steer text-to-video models toward correct ordering, timing, and persistence. It scores…
#AIresearch #TextToVideo #TemporalLogic #DiffusionModels
https://arxiv.org/abs/2610.20822
#AIresearch #TextToVideo #TemporalLogic #DiffusionModels
https://arxiv.org/abs/2610.20822
New arXiv work proposes EigDiff, using CLIP-space PCA eigenvectors to pick representative training samples and cut diffusion model dataset size with minimal quality loss.
#AI #DiffusionModels #MachineLearning #DataEfficiency
https://arxiv.org/abs/2610.20999
#AI #DiffusionModels #MachineLearning #DataEfficiency
https://arxiv.org/abs/2610.20999
October 16, 2026 at 4:01 PM
New arXiv work proposes EigDiff, using CLIP-space PCA eigenvectors to pick representative training samples and cut diffusion model dataset size with minimal quality loss.
#AI #DiffusionModels #MachineLearning #DataEfficiency
https://arxiv.org/abs/2610.20999
#AI #DiffusionModels #MachineLearning #DataEfficiency
https://arxiv.org/abs/2610.20999
VISTA is a training-free method that picks which denoising steps need reward guidance in discrete diffusion, cutting Sequential Monte Carlo compute by skipping low-value timesteps. Using a small set of…
#SMC #DiffusionModels #MachineLearning #AIResearch
https://arxiv.org/abs/2610.20669
#SMC #DiffusionModels #MachineLearning #AIResearch
https://arxiv.org/abs/2610.20669
October 16, 2026 at 6:00 AM
VISTA is a training-free method that picks which denoising steps need reward guidance in discrete diffusion, cutting Sequential Monte Carlo compute by skipping low-value timesteps. Using a small set of…
#SMC #DiffusionModels #MachineLearning #AIResearch
https://arxiv.org/abs/2610.20669
#SMC #DiffusionModels #MachineLearning #AIResearch
https://arxiv.org/abs/2610.20669
New paper introduces Annealed Lévy Diffusion, varying stability index over time to mix heavy-tailed exploration with Gaussian refinement in generative models. It reuses standard Gaussian noise-prediction…
#GenerativeAI #DiffusionModels #MachineLearning #ArXiv
https://arxiv.org/abs/2610.19692
#GenerativeAI #DiffusionModels #MachineLearning #ArXiv
https://arxiv.org/abs/2610.19692
October 15, 2026 at 6:02 AM
New paper introduces Annealed Lévy Diffusion, varying stability index over time to mix heavy-tailed exploration with Gaussian refinement in generative models. It reuses standard Gaussian noise-prediction…
#GenerativeAI #DiffusionModels #MachineLearning #ArXiv
https://arxiv.org/abs/2610.19692
#GenerativeAI #DiffusionModels #MachineLearning #ArXiv
https://arxiv.org/abs/2610.19692
トリモーダルマスク拡散モデルの設計空間：大規模マルチモーダル事前学習の全貌
テキスト・画像・音声の3モダリティを統合したマスク拡散モデルの設計空間とスケーリング則に関する研究レポート。
#DiffusionModels #Multimodal #ScalingLaws #MachineLearning
テキスト・画像・音声の3モダリティを統合したマスク拡散モデルの設計空間とスケーリング則に関する研究レポート。
#DiffusionModels #Multimodal #ScalingLaws #MachineLearning
トリモーダルマスク拡散モデルの設計空間：大規模マルチモーダル事前学習の全貌
テキスト・画像・音声の3モダリティを統合したマスク拡散モデルの設計空間とスケーリング則に関する研究レポート。
ai.warp-studio.com
October 14, 2026 at 7:09 PM
トリモーダルマスク拡散モデルの設計空間：大規模マルチモーダル事前学習の全貌
テキスト・画像・音声の3モダリティを統合したマスク拡散モデルの設計空間とスケーリング則に関する研究レポート。
#DiffusionModels #Multimodal #ScalingLaws #MachineLearning
テキスト・画像・音声の3モダリティを統合したマスク拡散モデルの設計空間とスケーリング則に関する研究レポート。
#DiffusionModels #Multimodal #ScalingLaws #MachineLearning
A new arXiv paper proposes a plug-and-play diffusion generator that maps frozen weather model outputs to observation-aligned daily precipitation, aiming to correct reanalysis biases without…
#WeatherAI #DiffusionModels #PrecipitationForecast #FoundationModels
https://arxiv.org/abs/2610.12976
#WeatherAI #DiffusionModels #PrecipitationForecast #FoundationModels
https://arxiv.org/abs/2610.12976
October 12, 2026 at 8:01 PM
A new arXiv paper proposes a plug-and-play diffusion generator that maps frozen weather model outputs to observation-aligned daily precipitation, aiming to correct reanalysis biases without…
#WeatherAI #DiffusionModels #PrecipitationForecast #FoundationModels
https://arxiv.org/abs/2610.12976
#WeatherAI #DiffusionModels #PrecipitationForecast #FoundationModels
https://arxiv.org/abs/2610.12976
Diffusion models paired with CFD validation now enable localized ship hull refinement, showing a generated bulbous bow and forebody matched KCS displacement with comparable calm-water resistance. A…
#GenerativeAI #ComputationalDesign #ShipDesign #DiffusionModels
https://arxiv.org/abs/2610.12870
#GenerativeAI #ComputationalDesign #ShipDesign #DiffusionModels
https://arxiv.org/abs/2610.12870
October 12, 2026 at 6:01 AM
Diffusion models paired with CFD validation now enable localized ship hull refinement, showing a generated bulbous bow and forebody matched KCS displacement with comparable calm-water resistance. A…
#GenerativeAI #ComputationalDesign #ShipDesign #DiffusionModels
https://arxiv.org/abs/2610.12870
#GenerativeAI #ComputationalDesign #ShipDesign #DiffusionModels
https://arxiv.org/abs/2610.12870
New work shows distribution matching distillation and drifting models are mathematically equivalent for one-step generation from diffusion-style generators. Arxiv 2610.10989 establishes a precise correspondence between…
#AIResearch #DiffusionModels #GenerativeAI
https://arxiv.org/abs/2610.10989
#AIResearch #DiffusionModels #GenerativeAI
https://arxiv.org/abs/2610.10989
October 9, 2026 at 8:00 PM
New work shows distribution matching distillation and drifting models are mathematically equivalent for one-step generation from diffusion-style generators. Arxiv 2610.10989 establishes a precise correspondence between…
#AIResearch #DiffusionModels #GenerativeAI
https://arxiv.org/abs/2610.10989
#AIResearch #DiffusionModels #GenerativeAI
https://arxiv.org/abs/2610.10989
Bernoulli Flow Models tackle slow sampling in binary diffusion by replacing fixed discrete steps with a continuous global Bernoulli trajectory, cutting function evaluations without retraining. Useful for…
#generativeAI #diffusionmodels #machinelearning #research
https://arxiv.org/abs/2610.11362
#generativeAI #diffusionmodels #machinelearning #research
https://arxiv.org/abs/2610.11362
October 9, 2026 at 8:01 AM
Bernoulli Flow Models tackle slow sampling in binary diffusion by replacing fixed discrete steps with a continuous global Bernoulli trajectory, cutting function evaluations without retraining. Useful for…
#generativeAI #diffusionmodels #machinelearning #research
https://arxiv.org/abs/2610.11362
#generativeAI #diffusionmodels #machinelearning #research
https://arxiv.org/abs/2610.11362
New arXiv paper analyzes how perturbations during diffusion model sampling affect outputs, introducing a KL-based "path cost" that bounds but does not determine output distribution changes. Useful framing for…
#DiffusionModels #GenerativeAI #Prompting #MLResearch
https://arxiv.org/abs/2610.11380
#DiffusionModels #GenerativeAI #Prompting #MLResearch
https://arxiv.org/abs/2610.11380
October 10, 2026 at 2:01 AM
New arXiv paper analyzes how perturbations during diffusion model sampling affect outputs, introducing a KL-based "path cost" that bounds but does not determine output distribution changes. Useful framing for…
#DiffusionModels #GenerativeAI #Prompting #MLResearch
https://arxiv.org/abs/2610.11380
#DiffusionModels #GenerativeAI #Prompting #MLResearch
https://arxiv.org/abs/2610.11380
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
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
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。
ai.warp-studio.com
October 8, 2026 at 5:11 PM
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
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
A new EEG representation learning framework uses structured state-space diffusion pretraining to handle limited labels and inter-subject variability while reducing compute costs compared to transformer-based…
#EEGAI #DiffusionModels #NeuroTech #PromptEngineering
https://arxiv.org/abs/2508.14086
#EEGAI #DiffusionModels #NeuroTech #PromptEngineering
https://arxiv.org/abs/2508.14086
October 7, 2026 at 12:01 AM
A new EEG representation learning framework uses structured state-space diffusion pretraining to handle limited labels and inter-subject variability while reducing compute costs compared to transformer-based…
#EEGAI #DiffusionModels #NeuroTech #PromptEngineering
https://arxiv.org/abs/2508.14086
#EEGAI #DiffusionModels #NeuroTech #PromptEngineering
https://arxiv.org/abs/2508.14086
A new episodic, derivative-free latent policy improvement approach boosts pretrained diffusion or flow matching robot policies by adjusting initial noise sampling, working across MLP and VLA models…
#RobotLearning #DiffusionModels #PolicyImprovement #GenerativeAI
https://arxiv.org/abs/2603.15757
#RobotLearning #DiffusionModels #PolicyImprovement #GenerativeAI
https://arxiv.org/abs/2603.15757
October 6, 2026 at 4:01 PM
A new episodic, derivative-free latent policy improvement approach boosts pretrained diffusion or flow matching robot policies by adjusting initial noise sampling, working across MLP and VLA models…
#RobotLearning #DiffusionModels #PolicyImprovement #GenerativeAI
https://arxiv.org/abs/2603.15757
#RobotLearning #DiffusionModels #PolicyImprovement #GenerativeAI
https://arxiv.org/abs/2603.15757
Birthday. (Fixed prior earlier version.) You are a researcher in AI/ML research communication. Your task is to share this paper on social media in a way that is accessible and useful for a general tech audience…
#GenAI #MLresearch #BayesianAI #DiffusionModels
https://arxiv.org/abs/2610.03396
#GenAI #MLresearch #BayesianAI #DiffusionModels
https://arxiv.org/abs/2610.03396
October 5, 2026 at 2:02 PM
Birthday. (Fixed prior earlier version.) You are a researcher in AI/ML research communication. Your task is to share this paper on social media in a way that is accessible and useful for a general tech audience…
#GenAI #MLresearch #BayesianAI #DiffusionModels
https://arxiv.org/abs/2610.03396
#GenAI #MLresearch #BayesianAI #DiffusionModels
https://arxiv.org/abs/2610.03396
離散拡散モデルにおける信頼度の限界：確率的依存性とサンプリングバイアスの理論的解明
離散拡散モデルにおける信頼度ベースのサンプリング手法の本質的限界を理論と実験で解明したAppleの最新研究。
#DiffusionModels #GenerativeAI #MachineLearning #Algorithms
離散拡散モデルにおける信頼度ベースのサンプリング手法の本質的限界を理論と実験で解明したAppleの最新研究。
#DiffusionModels #GenerativeAI #MachineLearning #Algorithms
離散拡散モデルにおける信頼度の限界：確率的依存性とサンプリングバイアスの理論的解明
離散拡散モデルにおける信頼度ベースのサンプリング手法の本質的限界を理論と実験で解明したAppleの最新研究。
ai.warp-studio.com
October 2, 2026 at 11:27 PM
離散拡散モデルにおける信頼度の限界：確率的依存性とサンプリングバイアスの理論的解明
離散拡散モデルにおける信頼度ベースのサンプリング手法の本質的限界を理論と実験で解明したAppleの最新研究。
#DiffusionModels #GenerativeAI #MachineLearning #Algorithms
離散拡散モデルにおける信頼度ベースのサンプリング手法の本質的限界を理論と実験で解明したAppleの最新研究。
#DiffusionModels #GenerativeAI #MachineLearning #Algorithms
New arXiv work shows scaling T5Gemma embeddings improves continuous diffusion language model performance, though highly discriminative raw embeddings can hurt sampling. Authors search for more diffusible embedding…
#GenerativeAI #DiffusionModels #LLM #Embeddings
https://arxiv.org/abs/2610.01016
#GenerativeAI #DiffusionModels #LLM #Embeddings
https://arxiv.org/abs/2610.01016
October 2, 2026 at 4:01 PM
New arXiv work shows scaling T5Gemma embeddings improves continuous diffusion language model performance, though highly discriminative raw embeddings can hurt sampling. Authors search for more diffusible embedding…
#GenerativeAI #DiffusionModels #LLM #Embeddings
https://arxiv.org/abs/2610.01016
#GenerativeAI #DiffusionModels #LLM #Embeddings
https://arxiv.org/abs/2610.01016