·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
US threatens sanctions against Chinese AI models over IP theft1h◆Google launches a cheaper alternative to large AI security models like Mythos1h◆Music streamer Deezer says more than 50% of daily uploads are AI-generated3h◆Halliday’s latest smart glasses feature a much-improved display3h◆America needs to stop getting shocked by Chinese AI5h◆Advancing next-gen AI with materials science innovation6h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else6h◆Capacity and Redundancy Trade-offs in Multi-Task Learning12h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation12h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making12h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection12h◆Supervised Reward Inference12h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization12h◆Is Progressive Disclosure All You Need for Long-Context Agents?12h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability12h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification12h◆Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time Collaboration12h◆Scientific reasoning does not reliably translate into scientific forecasting in frontier AI12h◆Language Triggers Hijack Language Circuits: A Mechanistic Analysis of Backdoor Behaviors in Large Language Models12h◆AI-Augmented Human Resource Management? Insights from German companies12h◆US threatens sanctions against Chinese AI models over IP theft1h◆Google launches a cheaper alternative to large AI security models like Mythos1h◆Music streamer Deezer says more than 50% of daily uploads are AI-generated3h◆Halliday’s latest smart glasses feature a much-improved display3h◆America needs to stop getting shocked by Chinese AI5h◆Advancing next-gen AI with materials science innovation6h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else6h◆Capacity and Redundancy Trade-offs in Multi-Task Learning12h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation12h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making12h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection12h◆Supervised Reward Inference12h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization12h◆Is Progressive Disclosure All You Need for Long-Context Agents?12h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability12h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification12h◆Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time Collaboration12h◆Scientific reasoning does not reliably translate into scientific forecasting in frontier AI12h◆Language Triggers Hijack Language Circuits: A Mechanistic Analysis of Backdoor Behaviors in Large Language Models12h◆AI-Augmented Human Resource Management? Insights from German companies12h◆
News/model/Trinity-Large-Thinking-GGUF

Trinity-Large-Thinking-GGUF news

2 articles mentioning Trinity-Large-Thinking-GGUF

arxivMay 28

Trinity: Unifying Class-Agnostic Terrain and Semantic Segmentation for Unstructured Outdoor Environments by Leveraging Synthetic Data

arXiv:2605.27644v1 Announce Type: cross Abstract: Terrain understanding is fundamental for mobile robots operating in unstructured outdoor environments. Existing vision-based traversability estimation methods rely on robot-specific annotations or semantic class mappings, limiting transferability acr

arxivApr 28

TRINITY: An Evolved LLM Coordinator

arXiv:2512.04695v3 Announce Type: replace Abstract: Combining diverse foundation models is promising, but weight-merging is limited by mismatched architectures and closed APIs. Trinity addresses this with a lightweight coordinator that orchestrates collaboration among large language models (LLMs). T

HomeModelsNews