3 papers
cs.CY2026
Deliberative multi-agent large language models improve clinical reasoning in ophthalmology
Ehsan Misaghi, Sean T Berkowitz, Bing Yu Chen +10
Large language models (LLMs) show potential for ophthalmic clinical reasoning, yet individual models risk introducing harm. We evaluated whether multi-agent LLM deliberative counci…
cs.CV2026
Exploring parameter-efficient fine-tuning (PEFT) of billion-parameter vision models with QLoRA and DoRA: insights into generalization for limited-data image classification under a 98:1 test-to-train regime
Haiyu Yang, Sumit Sharma, Enhong Liu +1
Automated behavior classification is essential for precision livestock farming but faces challenges of high computational costs and limited labeled data. This study systematically…
cs.CL2025
Performance of GPT-5 Frontier Models in Ophthalmology Question Answering
Fares Antaki, David Mikhail, Daniel Milad +11
Large language models (LLMs) such as GPT-5 integrate advanced reasoning capabilities that may improve performance on complex medical question-answering tasks. For this latest gener…