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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/Darwin-9B-Opus

Darwin-9B-Opus news

4 articles mentioning Darwin-9B-Opus

arxiv5d ago

DarwinLM: Evolutionary Structured Pruning of Large Language Models

arXiv:2502.07780v4 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved significant success across various NLP tasks. However, their massive computational costs limit their widespread use, particularly in real-time applications. Structured pruning offers an effective solution

arxivMay 16

Darwin Family: MRI-Trust-Weighted Evolutionary Merging for Training-Free Scaling of Language-Model Reasoning

arXiv:2605.14386v1 Announce Type: cross Abstract: We present Darwin Family, a framework for training-free evolutionary merging of large language models via gradient-free weight-space recombination. We ask whether frontier-level reasoning performance can be improved without additional training, by re

arxivMay 12

Direct From Darwin: Deriving Advanced Optimizers From Evolutionary First Principles

arXiv:2605.05284v2 Announce Type: replace-cross Abstract: Evolutionary computation has long promised to deliver both high-performance optimization tools as well as rigorous scientific simulations of Darwinian evolution. However, modern algorithms frequently abandon evolutionary fidelity for physics-

arxivApr 16

DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis

arXiv:2604.01236v3 Announce Type: replace-cross Abstract: Traditional network architectures suffer from severe protocol ossification and structural fragility due to their reliance on static, human-defined rules that fail to adapt to the emergent edge cases and probabilistic reasoning of modern auton

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