arXiv:2604.24276v1 Announce Type: new
Abstract: Instance-sensitive losses for semantic segmentation such as blob loss and CC loss were designed to address instance imbalance, ensuring small lesions generate the same gradient as large ones, but operate…
arXiv:2604.23678v1 Announce Type: new
Abstract: Accurate modeling of human mobility is critical for tackling urban planning and public health challenges. In undeveloped regions, the absence of comprehensive travel surveys necessitates reconstructing m…
arXiv:2604.23903v1 Announce Type: cross
Abstract: Large-scale neuroscience is generating rich datasets across animals, brain areas and behavioral contexts, yet our modeling efforts remains fragmented across isolated experiments. We argue that understa…
arXiv:2601.18399v2 Announce Type: replace
Abstract: Separating liquid-liquid dispersions in gravity settlers is critical in chemical, pharmaceutical, and recycling processes. The dense-packed zone height is an important performance and safety indicato…
Microsoft and OpenAI on Monday again revised their agreement, softening their exclusivity and revenue-sharing conditions in the process. These changes underscore how critical it is for enterprises to work with as many AI vendors …
arXiv:2604.22517v1 Announce Type: new
Abstract: Evaluating LLM-generated business ideas is often harder to scale than generating them. Unlike standard NLP benchmarks, business idea evaluation relies on multi-dimensional criteria such as feasibility, n…
arXiv:2604.22520v1 Announce Type: new
Abstract: Large Language Models (LLMs) have achieved remarkable performance in Machine Translation (MT), but deploying them at scale remains prohibitively expensive. A widely adopted remedy is the hybrid system pa…
arXiv:2602.06019v2 Announce Type: replace
Abstract: Existing techniques for accelerating language model inference, such as speculative decoding, require training auxiliary speculator models and building and deploying complex inference pipelines. We co…
arXiv:2604.22750v1 Announce Type: cross
Abstract: The wide adoption of AI agents in complex human workflows is driving rapid growth in LLM token consumption. When agents are deployed on tasks that require a significant amount of tokens, three question…
arXiv:2604.10079v4 Announce Type: replace
Abstract: Supervised Fine-Tuning (SFT) is the standard approach for adapting large language models (LLMs) to downstream tasks. However, we observe a persistent failure mode: even after convergence, models ofte…