别凭记忆选模型，用实时数据说话：215个模型、7个免费候选，证据驱动的选型指南。
Most teams do not need the perfect AI model on day one. They need a first model they can explain. The mistake is starting from brand memory: choose a …
用证据驱动的LLM智能体，把C代码转成可综合C并自动验证，精准解决芯片设计中的转化难题。
arXiv:2606.28409v1 Announce Type: cross Abstract: Software-compilable C programs routinely fail to complete the four-stage pipeline of a high-level sy…
首个基于长时序个人相册进行个性化资料重建的基准，以证据驱动的方法评估跨时间维度的身份推测能力。
arXiv:2606.16175v1 Announce Type: new Abstract: Longitudinal personal albums are weak-schema multimodal databases: noisy perceptual records whose key …
一种注册绑定的LLM管道，为热带植物、水生动物及异宠的性状提取提供可溯源证据，方法论严谨且跨物种适用。
arXiv:2606.00994v1 Announce Type: new Abstract: We describe a registry-bound large-language-model extraction pipeline producing evidence-grounded stru…
让AI学会同行评审：基于执行验证论文声明，用证据驱动事实核查。
arXiv:2604.04074v3 Announce Type: replace Abstract: LLM-based reviewing systems typically take only the manuscript as input, leaving literature and co…
用自动化自测作为质量门控，实现LLM应用证据驱动的发布管理，论文提出系统方法与实证数据。
arXiv:2603.15676v2 Announce Type: replace-cross Abstract: LLM applications are AI systems whose nondeterministic outputs and evolving model behavior m…