…While diffusion language models attempt to break this barrier via parallel generation, they suffer from significant performance degradation, high training costs, and a lack of rigorous convergence guarantees. Orthrus resolves this dichotomy…
…provides the greatest improvements while proposing entropy-based diagnostics to determine when such costly rewrites are beneficial. AI-generated summary Embedding-based code retrieval often suffers when encoders overfit to surface syntax…
…attention with a routed mixture-of-experts approach that reduces computational cost while maintaining performance and handling long contexts effectively. AI-generated summary DeepSeek Sparse Attention (DSA) sets the state of the…
…To address the first challenge, we propose Lite3R, a model-agnostic teacher-student framework that replaces dense attention with Sparse Linear Attention to preserve important geometric interactions while reducing attention cost. To…
…We further propose an amortized formulation that exploits the affine structure of the loss used in DiffusionNFT, to reduce the per-step cost from K+1 backward passes to near single-reward…
…some require costly retraining to accommodate variable-length outputs, while others depend solely on local confidence signals during decoding. Such local criteria fail to capture the evolving structure of the sequence, often…
…Get this paper in your agent: hf papers read 2605.13030 Don't have the latest CLI? curl -LsSf https://hf.co/cli/install.sh | bash No dataset linking this paper Cite…
…View arXiv page View PDF Add to collection Community https://x.com/MFarajtabar/status/2054275640946458785 Get this paper in your agent: hf papers read 2605.10889 Don't have the latest CLI…
…Listed API prices vary by over an order of magnitude across providers, but we use price dispersion only as directional motivation, not as causal evidence of marginal cost. The core physical question…
…AI-generated summary Adapting pretrained models typically involves a trade-off between the high training costs of backpropagation and the heavy inference overhead of memory-based or in-context learning . We propose…