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Announcing SimReady Central: An Open Library of Simulation-Ready 3D Assets for Robotics and AI Factories2h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response9h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making9h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents9h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD9h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value9h◆PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models9h◆WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations9h◆When Synthetic Speech Is All You Have: Better Call GRPO9h◆Reducing belief in conspiracy theories as they unfold using large language models9h◆Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors9h◆EDISCO: Equivariant DIScrete Diffusion for Euclidean Combinatorial Optimization9h◆Composing What Each Teacher Learned: Delta Distillation for Multi-Teacher On-Policy Learning9h◆CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding9h◆WeGR: Towards Unified Generative Retrieval via Behavior-aware Tokenization and Preference Alignment9h◆StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions9h◆Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents9h◆Secure Agentic Coding through Counterexample-Grounded Feedback9h◆Policy Search in Code Space for Multi-Agent Pathfinding9h◆TamedFlow: Trajectory-Aware Distillation for Teacher-Assisted Diffusion Sampling9h◆Announcing SimReady Central: An Open Library of Simulation-Ready 3D Assets for Robotics and AI Factories2h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response9h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making9h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents9h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD9h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value9h◆PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models9h◆WoW-Bench: Evaluating Fine-Grained Acoustic Perception in Audio-Language Models via Marine Mammal Vocalizations9h◆When Synthetic Speech Is All You Have: Better Call GRPO9h◆Reducing belief in conspiracy theories as they unfold using large language models9h◆Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors9h◆EDISCO: Equivariant DIScrete Diffusion for Euclidean Combinatorial Optimization9h◆Composing What Each Teacher Learned: Delta Distillation for Multi-Teacher On-Policy Learning9h◆CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding9h◆WeGR: Towards Unified Generative Retrieval via Behavior-aware Tokenization and Preference Alignment9h◆StartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions9h◆Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents9h◆Secure Agentic Coding through Counterexample-Grounded Feedback9h◆Policy Search in Code Space for Multi-Agent Pathfinding9h◆TamedFlow: Trajectory-Aware Distillation for Teacher-Assisted Diffusion Sampling9h◆

News/model/Ornstein-Hermes-3.6-27b-SABER-GGUF

Ornstein-Hermes-3.6-27b-SABER-GGUF news

1 articles mentioning Ornstein-Hermes-3.6-27b-SABER-GGUF

arxivOct 1

Ornstein-Uhlenbeck Is Hard to Beat, Yet Superlinear Drift Ships Lower Transport Costs

arXiv:2609.37579v1 Announce Type: cross Abstract: Bre\v{s}ar and Mijatovi\'c \cite{bresar2025} show that Ornstein--Uhlenbeck diffusion is hard to beat in forward convergence under assumptions that exclude superlinear drift. We instead test superlinear Langevin diffusions for score-based image genera

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