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Data centers expected to use 4x more electricity by 20351h◆Google releases three new Gemini models — but no 3.5 Pro2h◆Introducing the ChatGPT for small business program2h◆Anthropic’s $1.5 billion book piracy settlement approved by judge2h◆US threatens sanctions against Chinese AI models over IP theft4h◆Google launches a cheaper alternative to large AI security models like Mythos4h◆Music streamer Deezer says more than 50% of daily uploads are AI-generated6h◆Halliday’s latest smart glasses feature a much-improved display6h◆America needs to stop getting shocked by Chinese AI8h◆Advancing next-gen AI with materials science innovation9h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else9h◆Capacity and Redundancy Trade-offs in Multi-Task Learning15h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation15h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making15h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection15h◆Supervised Reward Inference15h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization15h◆Is Progressive Disclosure All You Need for Long-Context Agents?15h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability15h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification15h◆Data centers expected to use 4x more electricity by 20351h◆Google releases three new Gemini models — but no 3.5 Pro2h◆Introducing the ChatGPT for small business program2h◆Anthropic’s $1.5 billion book piracy settlement approved by judge2h◆US threatens sanctions against Chinese AI models over IP theft4h◆Google launches a cheaper alternative to large AI security models like Mythos4h◆Music streamer Deezer says more than 50% of daily uploads are AI-generated6h◆Halliday’s latest smart glasses feature a much-improved display6h◆America needs to stop getting shocked by Chinese AI8h◆Advancing next-gen AI with materials science innovation9h◆Gritt exits stealth with $32 million for robots to build solar plants — then, everything else9h◆Capacity and Redundancy Trade-offs in Multi-Task Learning15h◆Predictive Training with Latent Imagination for Visual Quadruped Navigation15h◆Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making15h◆Did We Actually Fix It? An Independent Adversarial Stress-Test of Post-Point-Adjustment Evaluation Metrics for Time-Series Anomaly Detection15h◆Supervised Reward Inference15h◆PPO-HSC: An Exploratory Reinforcement Learning Framework Based on Wide-Area Policy Coverage Optimization15h◆Is Progressive Disclosure All You Need for Long-Context Agents?15h◆It Depends on the Dataset: When a Brain-Encoding Model's Predicted Responses Beat Their Visual Backbone for Video Memorability15h◆DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification15h◆
News/model/Trinity-Large-Thinking

Trinity-Large-Thinking news

2 articles mentioning Trinity-Large-Thinking

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

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