arXiv:2604.19606v2 Announce Type: replace
Abstract: Systematic ablations are essential to attribute performance gains in AI Virtual Cells, yet they are rarely performed because biological repositories are under-standardized and tightly coupled to doma…
arXiv:2505.17056v2 Announce Type: replace-cross
Abstract: As large language models (LLMs) are increasingly integrated into educational tools, current evaluations on standardized tests predominantly focus on binary outcome accuracy. Instead, an effecti…
arXiv:2604.27117v1 Announce Type: cross
Abstract: Recommender systems increasingly incorporate textual reviews to enrich user and item representations. However, most review-aware models remain optimized for rating prediction rather than ranking qualit…
arXiv:2508.21787v2 Announce Type: replace-cross
Abstract: Best-of-n sampling improves the accuracy of large language models (LLMs) and large reasoning models (LRMs) by generating multiple candidate solutions and selecting the one with the highest rewa…
arXiv:2510.12476v3 Announce Type: replace-cross
Abstract: Large language models (LLMs) have grown more powerful in language generation, producing fluent text and even imitating personal style. Yet, this ability also heightens the risk of identity impe…
arXiv:2510.20933v2 Announce Type: replace-cross
Abstract: Medical image segmentation is essential for clinical applications such as disease diagnosis, treatment planning, and disease development monitoring because it provides precise morphological and…
arXiv:2604.27115v1 Announce Type: new
Abstract: Neuron pruning is widely used to reduce the computational cost and parameter footprint of large language models, yet it remains unclear whether neurons in task-specific models contribute uniformly to tas…
arXiv:2604.27043v1 Announce Type: new
Abstract: Today’s AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move beyond professional setti…
arXiv:2604.27232v1 Announce Type: new
Abstract: Models of sign language have historically lagged behind those for spoken language (text and speech). Recent work has greatly improved their performance on tasks like sign language translation and isolate…
arXiv:2604.27251v1 Announce Type: new
Abstract: Large Language Models (LLMs) are known to acquire reasoning capabilities through shared inference patterns in pre-training data, which are further elicited via Chain-of-Thought (CoT) practices. However, …