arXiv:2609.20981v1 Announce Type: new Abstract: Autoregressive (AR) models suffer from local greediness, while diffusion language models (DLMs) often lack the strict causal structure required for reasoning. To combine the advantages and overcome the drawbacks of the dual, we propose Causal Latent Revision (CaLR), a framework that reformulates reasoning as constrained latent optimization. By adopti

So what

What this event means by reading role—not a longer recap.

BuilderIf the method generalizes, diffusion-based generation could gain AR-style logical consistency, but no code or API is released yet.
ResearcherCaLR reframes reasoning as constrained latent optimization using a causal topology matrix from an expert model, reporting SOTA DLM results and strong Sudoku rob

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