What if you could generate a fully rigged, animatable 3D character from just a single photo? Researchers from The University of Hong Kong, VAST, CUHK, and Tsinghua University present AniGen for exactly that. They created a unified system that simultaneously generates a 3D
RESEARCH
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Hyperloop Transformers: Memory-Efficient LLM via Looped Architecture
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"Hyperloop Transformers" This paper propose a memory-efficient LLM via looped Transformers. They basically reuse the middle block across depth, then add hyper-connections only between loops. Key result is that this restores flexibility lost from weight sharing, letting the
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DeepSeek V4 Flash vs Qwen 3.6: Size vs Efficiency Showdown
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MADNESS DeepSeek V4 Flash 284B
(MoE, 13B Active Params/Tok) Is only 1 point higher on the Artificial Analysis Intelligence Index than Qwen 3.6 27B (Dense, 27B Active Param/Tok) Qwen 3.6 size is double that of the active parameters and 1/10 of the full size of DeepSeek V4 Flash -
Low error rate achieved in AI system review
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I reviewed a few 100 and error rate is very very low.
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AI Recommendations Conflicting with Thermodynamics Laws
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What happens when your AI recommendation conflicts with the laws of thermodynamics? @IIoT_World @CRudinschi @agentic_factory @danielbastos @joannefriedman @AshokNellikar
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Eight-Step AI Process: From Problem Definition to Responsible Deployment
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AI isn’t magic. It’s a process. 8 steps:
define problem
collect/prepare data
choose model
train
evaluate
fine-tune
deploy
ensure ethics & safety Real value comes from running this loop well. #AI #MachineLearning #DataScience #ResponsibleAI -
Qwen 3.6 27B remains the top 2026 AI release with RTX 3090s
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Qwen 3.6 27B is still the release of 2026 for me despite everything else that has come out Pair it with a couple of RTX 3090s and you’re set even if they banned AI everywhere
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PARE Framework Evaluates Proactive AI Agents Anticipating User Needs
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Great paper on improving proactive agents. (bookmark it) Proactive agents act before you do. But how do you evaluate something that's supposed to anticipate needs you haven't expressed? This work introduces PARE, a framework that models applications as finite state machines
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Train-to-Test Scaling: Optimizing AI Compute Budget for Inference
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Train-to-Test scaling explained: How to optimize your end-to-end #AI compute budget for inference
by @bendee983 @VentureBeat Learn more: https://
bit.ly/4u2teQg #ArtificialIntelligence #MachineLearning #ML -
AI Paper Review: Addressing Hallucinations and Privacy Concerns in Models
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There is also too much focus on two issues in discussions of paper reviewing: hallucinations & privacy. Hallucinations are not gone, but the latest models rarely hallucinate sources (& it is relatively easy to make the human responsible) And you can get IP compliance easily now