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DA-DLM is proposed, a model that adapts DAG-based dependency modeling to DLMs'iterative setting through a position-oriented DAG design, and consistently outperforms Block Diffusion, especially under fewer denoising steps, and matches autoregressive models while preserving the parallel generation advantage.
S$^4$R is proposed, which builds low-rank subspaces from selectively sampled tokens and computes attention over a sparsely reconstructed KV representation, combining the efficiency of fixed compression with the adaptability of prompt-dependent methods.
GRKV (Global Regression for KV Cache), a training-free KV-cache merging method that directly minimizes the discrepancy between compressed-cache and full-cache attention outputs, is proposed.
Experiments show that AP-REASONER outperforms baseline subsamplers on structure-sensitive downstream tasks and enables controllable recovery of alternative protein conformations, highlighting the value of modeling MSA subsampling as a controllable optimization problem, where factor-graph reasoning offers an effective a...
Hierarchical Landmark Sparse Attention is proposed, a chunk-wise sparse attention mechanism that learns chunk selection end-to-end under the language-modeling (LM) loss, enabling long-context LLMs that are both more efficient and more effective on general long-context tasks than their full-attention counterparts.
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