美团LongCat团队推出UNO-Bench基准测试，系统评估多模态模型能力。该基准覆盖44种任务类型和5种模态组合，包含1250个全模态样本（跨模态可解性98%）及2480个增强单模态样本，全面测试模型在单模态与全模态场景下的性能表现。
美团开源多模态大模型LongCat-Flash-Omni实现技术突破，在多项基准测试中超越闭源竞品，达到业界领先水平。该模型支持文本、语音、图像、视频的实时融合处理，具备近乎零延迟的交互能力，将本地化多模态AI应用推向新高度。
美团正式发布自研LongCat大模型官方App，支持安卓和iOS系统下载。该应用具备联网搜索、语音通话功能，未来还将加入视频通话。通过文本处理和多模态理解技术，帮助用户高效获取信息，体现美团在人工智能领域的重要进展。
新研究警告：大语言模型持续接触无意义网络内容可能导致性能显著下降，推理能力和自信心受损。多所美国大学团队提出“LLM脑衰退假说”，类比人类过度接触无脑内容造成的认知损害。
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