Can AI learn to optimize its own creative process for faster and higher-quality image generation? AdaGen is here to move beyond the rigid, manually designed rules currently used in models like Diffusion and Transformers. AdaGen uses a lightweight reinforcement learning policy
MACHINE LEARNING
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Six Dimensions Aligning Data Science Success Framework
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Data Science works when 6 dimensions align: Goals (value, decisions)
Methods (stats, ML/DL, A/B, viz)
People (DS+ML+Biz+Domain)
Processes (collect→clean→train→deploy→monitor)
Tech (Python/R, TF/PyTorch, cloud, SQL/NoSQL, BI)
Culture (collab, ethics, learning, -
Kimi K2.6 Tops SWE-Bench Pro, Beating Claude Opus 4.6
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Kimi K2.6 just dropped. And it crushed Claude Opus 4.6 on SWE-Bench Pro. – Kimi K2.6: 58.6
– GPT-5.4 xhigh: 57.7
– Gemini 3.1 Pro: 54.2
– Claude Opus 4.6: 53.4 An open-source model is now #1 on agentic coding. The moat around frontier labs is shrinking fast. -
Neural Network Disruption Through Sign-Bit Flip Attacks
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Maximal Brain Damage Without Data or Optimization Disrupting Neural Networks via Sign-Bit Flips paper: https://
huggingface.co/papers/2502.07
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SNR-t Bias in Diffusion Probabilistic Models Explained
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Elucidating the SNR-t Bias of Diffusion Probabilistic Models paper: https://
huggingface.co/papers/2604.16
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Practical AI in B2B Commerce: ROI Over Science Fiction
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Less Sci-Fi, More #ROI: The Case For Practical #AI In B2B Commerce
by Jary Carter @Forbes Learn more: https://
bit.ly/4ctYfFp #ArtificialIntelligence #MachineLearning #ML #DL -
Frontier Models Still Outperform Open Source AI Releases
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They're almost always much worse than frontier models (no matter what anyone/benchmarks say). The last few releases have been very solid though (but I suspect frontier will pull far ahead again soon, so this won't last long).
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AI Transformers Cut Cancer Trial Failure Rate to 5%
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🔬 Training Transformers to solve 95% failure rate of Cancer Trials
— Latent.Space (@latentspacepod) 20 avril 2026
the AI for Science pod is back with @RonAlfa, CEO of @NOETIK_ai, and Daniel Bear, VP Research at Noetik, explaining exactly how their team of top AI x Bio researchers and engineers (shoutout @owl_posting) will… pic.twitter.com/xdyYWraTkUTraining Transformers to solve 95% failure rate of Cancer Trials the AI for Science pod is back with @RonAlfa
, CEO of @NOETIK_ai
, and Daniel Bear, VP Research at Noetik, explaining exactly how their team of top AI x Bio researchers and engineers (shoutout @owl_posting
) will -
NVIDIA’s Self-Evolving Logic Synthesis Framework with Multi-Agent LLMs
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NEW paper from NVIDIA. EDA tools like ABC have been hand-tuned by humans for decades. New research from NVIDIA shows they can evolve themselves. The work introduces the first self-evolving logic synthesis framework: multi-agent LLMs autonomously refine the entire ABC codebase,
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Simple Prompting Trick Improves LLM Randomness and Output Diversity
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Getting LLMs to simulate “true” randomness or generate diverse outputs is surprisingly difficult. We found a simple prompting trick that solves this by having the model generate and manipulate a random string. To be presented at #ICLR2026 this week!
— hardmaru (@hardmaru) 20 avril 2026
Blog: https://t.co/CyevqqJ5ej https://t.co/dN0yZZ5MijGetting LLMs to simulate “true” randomness or generate diverse outputs is surprisingly difficult. We found a simple prompting trick that solves this by having the model generate and manipulate a random string. To be presented at #ICLR2026 this week! Blog: https://
pub.sakana.ai/ssot