Tool Attention Is All You Need
RESEARCH
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DeepSeek V4 Launch: 1.6T MoE Model With 1M Context Window
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While everyone watched GPT-5.5 launch, DeepSeek quietly shipped V4 the next morning. V4-Pro: 1.6T total / 49B active, MIT license.
V4-Flash: 284B total / 13B active.
Both with native 1M-token context. At 1M tokens, V4-Pro runs at 27% of V3.2's FLOPs and 10% of the KV cache. -
World Engine: Synthetic Edge Cases for Autonomous Driving Training
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What if you could train a self-driving car on its hardest moments, not just its longest drives? OpenDriveLab, Huawei, NVIDIA & others present World Engine. Instead of just adding more miles of normal data, it generates massive volumes of synthetic edge cases—like cut-ins and
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Can LLMs Truly Emulate Individual Human Online Personas?
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Can LLMs truly think and act like a specific person online? Researchers from Northeastern, USC, Columbia & others present OPeRA, a new dataset that captures real people’s shopping habits—their persona, screen view, action, and internal reasoning. It’s the first public
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Today’s AI Skills Become Tomorrow’s Training Targets
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btw the hot skills of today become the posttrain targets of tomorrow
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AI tackles healthcare’s trillion-dollar administrative burden problem
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There's a system quietly at work before patients receive care: the administrative spending that costs the U.S. $1T annually. A research team led by @StanfordHAI Affiliate @drnigam tests AI on this invisible layer of healthcare. Read more via @StanfordMed
: https://
medicine.stanford.edu/content/sm/med
icine/news/current-news/standard-news/the–1-trillion-problem-ai-still-can-t-yet-solve.html
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Context Unrolling in Omni Models Paper
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Context Unrolling in Omni Models
— AK (@_akhaliq) 24 avril 2026
paper: https://t.co/Nmyybmhv5c pic.twitter.com/7yAjjLP5xqContext Unrolling in Omni Models paper: https://
huggingface.co/papers/2604.21
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UniT: Unified Physical Language for Humanoid Policy Learning
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UniT Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling paper: https://
huggingface.co/papers/2604.19
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LLMs in Code Development: Quality Concerns vs Photography
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The difference, of course, is that the world needs relatively few professional photographers, compared to developers. The pics I take of friends skiing with my iPhone are more than good enough; the code I write with an LLM may not be. But his general point stands: AI doesn't
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Sim2Reason Trains LLMs on Physics Without Human Annotation
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AI can now learn physics the way Newton did — by experiencing it.
— AlphaSignal AI (@AlphaSignalAI) 24 avril 2026
Training LLMs on physics problems hits a wall fast.
Human-labeled question-answer data is scarce and narrow.
Less than 2% of DeepSeek-R1's training pairs touch STEM.
Sim2Reason skips annotation entirely.… pic.twitter.com/SSPs33du51AI can now learn physics the way Newton did — by experiencing it. Training LLMs on physics problems hits a wall fast. Human-labeled question-answer data is scarce and narrow. Less than 2% of DeepSeek-R1's training pairs touch STEM. Sim2Reason skips annotation entirely.