Practical reporting on AI systems, tools, and engineering execution.
Moshi's CEO Anekha Sokhal built a recommendation engine for commodity traders by combining market data, workflow tools, and personalized signals—here's how to approach similar systems.
DiffusionRVGE sidesteps backprop through diffusion sampling by regressing centered rewards on centered final denoised images, yielding reported speedups and lower compute vs prior diffusion-RL methods while preserving reward performance.
Normalizing Flow Maps (NFM) introduce exactly invertible flow-map integrators that preserve tractable likelihoods while enabling few-step generation via two complementary distillation objectives.
ReasonUni inserts a reasoning step and a TreeGRPO reinforcement‑learning objective into reconstructive post‑training to improve both image understanding and conditional image generation for unified multimodal models.
A new arXiv paper isolates transformer architecture changes—feature/query-key normalization, wider noise conditioning, calibrated gradient routing, and confidence-gated self-conditioning—that cut generation perplexity for continuous diffusion language models (CDLMs).
MSR is a test‑time pipeline that mines categorical attribute schemas from a corpus, samples configurations from corpus statistics, and uses a generative model as a conditional renderer to increase output diversity and corpus alignment.
VISTA picks which denoising steps to run expensive Sequential Monte Carlo guidance on by using value statistics from a few full-guidance warmups and exact dynamic programming to meet a fixed guidance budget.
Predictive-State Flows (PSF) move fine-grained token choice out of the diffusion prior into a block-causal decoder, reporting much lower sampling FLOPs and higher MAUVE on unconditional OpenWebText in the paper's experiments.
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