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- Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning249
A comprehensive data curation procedure that efficiently acquires rich medical knowledge data not only from medical imaging but also from extensive medical texts and general-domain data and synthesizes accurate medical captions, visual question answering (VQA), and reasoning samples is proposed.
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- Contrastive Chain-of-Thought Prompting66
This work proposes contrastive chain of thought, an automatic method to construct contrastive demonstrations that provides both valid and invalid reasoning demonstrations, to guide the model to reason step-by-step while reducing reasoning mistakes.
- SeaLLMs-Audio: Large Audio-Language Models for Southeast Asia13
The first large audio-language model (LALM) tailored for multiple Southeast Asian languages-Indonesian, Thai, Vietnamese, English, and Chinese-alongside English and Chinese is introduced, showing that SeaLLMs-Audio achieves competitive performance compared with other LALMs on SEA languages.
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- Unsupervised person re-identification based on distribution regularization constrained asymmetric metric learning5
This work proposes an asymmetric metric learning method based on distribution regularization constraints for unsupervised person re-identification that not only alleviates the bias caused by different views, but also effectively overcomes the issue of low recognition accuracy caused by the interference of scenes and clothing changes other than views.
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- Comparison of efficacy of acupuncture-related therapy in the treatment of perimenopausal obesity: a network meta-analysis of randomized controlled trials2
Several acupuncture-related therapies may be more effective than WM or HM for perimenopausal obesity and could serve as alternative treatments, with method selection based on individual clinical needs, though confirmation through higher-quality trials is warranted.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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