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标星Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework
The rapid growth of scientific literature has made it increasingly difficult for researchers to efficiently discover, evaluate, and synthesize relevant work. Recent advances in multi-agent large language models (LLMs) h…
备注：Anchor paper for the multi-agent discovery workflow; compare its planner design with newer agent benchmarks.
下一步：compare planner design with newer agent benchmarks
最晚处理：2026-04-18
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待跟进ClinicRealm: Re-evaluating large language models with conventional machine learning for non-generative clinical prediction tasks.
Large Language Models (LLMs) are increasingly deployed in medicine. However, their utility for non-generative clinical prediction is under-evaluated, and they are often assumed to be inferior to specialized models, crea…
备注：Recheck whether ClinicRealm still beats classical clinical baselines under the same task framing.
下一步：recheck benchmark framing against classical baselines
最晚处理：2026-04-20
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