Banger paper from NVIDIA. Bigger models aren't always the answer. However, the default approach to improving AI systems today remains scaling up. More parameters, more compute, more cost. But many tasks don't require the full power of a massive model. This new research
MACHINE LEARNING
-
General Agentic Memory Via Deep Research Study
By
–
General Agentic Memory Via Deep Research Yan et al.: https://
arxiv.org/abs/2511.18423 #ArtificialIntelligence #DeepLearning #MachineLearning -
Video-As-Prompt: Semantic Video Generation Without Retraining
By
–
What if video generation could follow any semantic instruction without retraining or task-specific hacks? Enter Video-As-Prompt (VAP). By treating a reference video as an in-context semantic prompt and steering a frozen DiT with a plug-and-play MoT expert plus temporally
-
DeepSeek-Math-V2 Achieves Self-Verifiable Mathematical Reasoning
By
–
DeepSeek just released DeepSeek-Math-V2! DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning It shows LLMs can now self-verify proofs, not just output solutions. DeepSeekMath-V2 achieves gold-level IMO 2025, CMO 2024, and 118/120 Putnam 2024, pointing to a future of
-
Depth Anything 3: Unified 3D Reconstruction Model ByteDance
By
–
Depth Anything 3 Bytedance Seed proposes a simple & scalable unified model for recovering 3D visual space from any number of views with or without known camera poses. Absolute top notch quality with SoTA 3D reconstruction and camera pose estimation
-
AlphaFold 2 celebrates five years transforming global research
By
–
Thrilled to celebrate 5 years of AlphaFold 2! It’s now been used by over 3 million researchers around the world to accelerate their vital research – and it was an honour of a lifetime for our work to be recognised last year with the Nobel Prize! Proof of AI’s potential to enable
-
AlphaFold Five Years Impact and The Thinking Game Documentary
By
–
Read more about AlphaFold in our blog: https://
deepmind.google/blog/alphafold
-five-years-of-impact/
… Watch The Thinking Game on YouTube (a sort of sequel to the award-winning AlphaGo documentary – if you enjoyed that, you will enjoy this): -
ROOT: Robust Orthogonalized Optimizer for Neural Network Training
By
–
ROOT: Robust Orthogonalized Optimizer for Neural Network Training Huawei Noah's Ark Lab
Paper: https://
arxiv.org/abs/2511.20626
Code: https://
github.com/huawei-noah/no
ah-research/tree/master/ROOT
… Our report: https://
mp.weixin.qq.com/s/X7dNh8lwr0xV
W7TsuO4D2g
… -
Huawei ROOT: Robust Orthogonalized Optimizer for LLM Training
By
–
This new optimizer can make training giant LLMs both more stable and more precise, even under noise and extreme scale! Huawei just introduces ROOT, a Robust Orthogonalized Optimizer that tackles two big weaknesses in recent momentum-orthogonalized methods: – Dimensional
-
Faster R-CNN Wins NeurIPS Test of Time Paper Award
By
–
Congratulations to Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun! Their paper “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” has been awarded the Test of Time Paper Award at NeurIPS 2025. The Faster R-CNN paper has been cited more than