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Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajectory steering for reasoning and inference acceleration. To overcome this, we present Sigma, the first large-scale (3B/8B) continuous dLM built on steerable, low-dimensional...
The first scaling law for continuous DLMs that rivals discrete DLMs is established: RePlaid exhibits a compute gap of only $20\times$ compared to autoregressive models, outperforms Duo while using fewer parameters, and outperforms MDLM in the over-trained regime.
SearchArt is introduced, a scalable framework for training long-horizon search agents through verification-driven task synthesis and a multi-stage post-training pipeline, which exhibits adaptive search planning, iterative evidence aggregation, and complex reasoning over extended interaction horizons.
A one-round study provides initial evidence for PRD-guided self-evolution, motivating validation at larger scales and in industrial settings, and presents AgentOmnia, a framework coordinating task-space definition, data synthesis, post-training, evaluation, and improvement across To-Consumer (ToC), To-Business (ToB), a...
A cheap density prior is introduced over natural protein activations and keeps only the candidates that remain typical under it, a training-free post-hoc step the authors call Mahalanobis filtering that improves both the property score and the structural plausibility of the sequences it keeps at negligible cost, withou...
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