🤖 AI资讯日报

探讨传统企业如何比AI炒作周期更持久，强调务实商业模式的重要性。

讨论当前AI算法在实际应用中的痛点与挑战。

探讨NLP、AI、ML和机器人技术是短暂趋势还是具有深远影响。

讨论纽约地方法律144对AI行业可能产生的影响与担忧。

探讨AI发展速度是否呈指数级增长及其影响。

MIT Non-AI License

The AI Crackpot Index

Ask HN: What would you read to learn about "artificial intelligence"?

Bioinformatician

Common Lisp + Machine Learning Internship at Google (Mountain View, CA)

Show HN: Startup Raising capital through Book Sales

The Next Bill Gates or Albert Einstein in AI “Chris Clark” – Yourobot

研究发现大音频语言模型易受叙事式音频攻击，通过合成语音嵌入指令可绕过安全机制，成功率高达98.26%。

提出Med-Scout框架，通过几何感知强化学习解决医疗多模态大语言模型的几何盲区问题，提升诊断准确性。

提出VideoGPA框架，利用几何基础模型生成偏好信号，通过直接偏好优化提升视频扩散模型的3D一致性与运动连贯性。

提出IRL-DAL框架，结合逆强化学习与扩散模型进行自动驾驶轨迹规划，实现96%成功率并显著降低碰撞率。

提出SPAN架构，基于可分离样条网络提升强化学习样本效率与成功率，在资源受限环境中表现优异。

提出BRACE框架，通过端到端优化贝叶斯意图推断与上下文自适应辅助，提升人机协作成功率与路径效率。

提出MAPPA方法，通过每步动作的过程奖励优化多智能体系统，在数学竞赛与数据分析任务中显著提升性能。

提出TEON优化器，通过张量化正交化超越层间Muon方法，提升大语言模型预训练效率与鲁棒性。

提出Nesterov加速Transformer框架，将Transformer层解释为优化算法迭代，在文本生成任务中超越基准模型。

证明鲁棒MDPs的策略迭代算法具有强多项式时间复杂度，解决了该领域的重要算法开放问题。

In recent years, large language models (LLMs) have made rapid progress in information retrieval, yet existing research has mainly focused on text or static multimodal settings. Open-domain video shot retrieval, which involves richer temporal structure and more complex semantics, still lacks systematic benchmarks and analysis. To fill this gap, we introduce ShotFinder, a benchmark that formalizes editing requirements as keyframe-oriented shot descriptions and introduces five types of controllable single-factor constraints: Temporal order, Color, Visual style, Audio, and Resolution. We curate 1,210 high-quality samples from YouTube across 20 thematic categories, using large models for generation with human verification. Based on the benchmark, we propose ShotFinder, a text-driven three-stage retrieval and localization pipeline: (1) query expansion via video imagination, (2) candidate video retrieval with a search engine, and (3) description-guided temporal localization. Experiments on multiple closed-source and open-source models reveal a significant gap to human performance, with clear imbalance across constraints: temporal localization is relatively tractable, while color and visual style remain major challenges. These results reveal that open-domain video shot retrieval is still a critical capability that multimodal large models have yet to overcome.

研究无实现性假设下的语言识别与生成问题，提出新目标并获得接近紧致的统计速率特征。