3 papers
cs.CL2026
Support Vector Rubrics: Closing the Gap Between Self-Generated and Human Rubrics
Mengyuan Sun, Yu Li, Zhuohao Yu +2
Rubric-based evaluation is a promising paradigm for judging large language model (LLM) outputs, yet self-generated rubrics lag human-annotated criteria on hard instances. We argue…
cs.CL2026
Mixture-of-Depths Attention
Lianghui Zhu, Yuxin Fang, Bencheng Liao +10
Scaling depth is a key driver for large language models (LLMs). Yet, as LLMs become deeper, they often suffer from signal degradation: informative features formed in shallow layers…
cs.CV2026
Exploring the Use of VLMs for Navigation Assistance for People with Blindness and Low Vision
Yu Li, Yuchen Zheng, Giles Hamilton-Fletcher +6
This paper investigates the potential of vision-language models (VLMs) to assist people with blindness and low vision (pBLV) in navigation tasks. We evaluate state-of-the-art close…