🤖 AI资讯日报

云计算GPU成本降低50%，相比AWS为开发者节省50%

AI的进步速度是否呈指数级增长？

探讨当前AI算法的使用痛点

NLP、AI、ML、机器人——是短暂趋势还是更多？你的看法是什么？

谷歌提供的Common Lisp与机器学习实习机会

初次接触人工智能，应该期待什么？

AI领域的非主流观点索引

AI领域的下一个比尔·盖茨或爱因斯坦——Chris Clark

对研究生院（CS博士）的看法？

询问对纽约市地方法律144的关注

生物信息学家

初创公司通过书籍销售筹集资金

大型语言模型文本生成推理工具

PyTorch实现的YOLOv3，支持多平台转换

高质量图像语义分割的PyTorch U-Net实现

A collaboration friendly studio for NeRFs

生产级数据科学工具箱，应用软件工程最佳实践

MIT深度学习相关课程的教程、作业和竞赛

斯坦福大学TensorFlow深度学习研究课程代码示例

Python中使用Scikit-learn和TensorFlow的机器学习与深度学习基础教程

基于PyTorch的语音工具包

内容感知图像调整大小库

机器学习实践者的开源标注工具

分析了强化学习在数学推理任务中由于数据污染导致的不可靠结果，提出了使用合成数据集的解决方案。

研究提出了一种自我监督学习方法，用于从无标签的相机陷阱 footage 中学习黑猩猩面部嵌入，展示了在生物多样性监测中的潜力。

Large Language Models (LLMs) have significantly advanced the state-of-the-art in various coding tasks. Beyond directly answering user queries, LLMs can also serve as judges, assessing and comparing the quality of responses generated by other models. Such an evaluation capability is crucial both for benchmarking different LLMs and for improving response quality through response ranking. However, despite the growing adoption of the LLM-as-a-Judge paradigm, its effectiveness in coding scenarios remains underexplored due to the absence of dedicated benchmarks. To address this gap, we introduce CodeJudgeBench, a benchmark explicitly designed to evaluate the performance of LLM-as-a-Judge models across three critical coding tasks: code generation, code repair, and unit test generation. Through comprehensive benchmarking of 26 LLM-as-a-Judge models, we find that recent thinking models significantly outperform non-thinking models on our carefully designed code judging tasks. Notably, even relatively small thinking models, such as Qwen3-8B, can outperform specially trained LLM-as-a-Judge models up to 70B in size. Nevertheless, all models still exhibit significant randomness in their judgment of coding tasks. For pairwise judging tasks, simply changing the order in which responses are presented can substantially impact accuracy. In addition, when judging code and unit tests written by different LLMs, LLM-as-a-Judge models also show variance in performance. This sensitivity raises concerns about the reliability and consistency of LLM-as-a-Judge in coding scenarios. Lastly, we study optimal prompting strategies for LLM-as-a-Judge. We find that using pair-wise comparison outperforms scalar point-wise judging. Furthermore, retaining comments and reasoning in the full, unprocessed LLM response leads to improved judge performance.

介绍了TS-GEN，一种条件流匹配生成模型，用于准确生成化学反应过渡态结构。

提出了构建生物学AI模型基准测试框架的建议，以促进高质量数据管理和标准化工具的使用。

介绍了EmRACE-3K数据集，用于评估视觉语言模型在嵌入式环境中的推理能力，展示了在交互环境中的挑战。

提出了一种方法，通过局部定义的面部表情与3D高斯溅射结合，创建高保真、表达丰富的3D头像。

介绍了DeepResearch$^{ ext{Eco}}$，一个基于LLM的系统，用于自动化科学合成，支持对原始研究问题的递归探索。

探讨了AI视频聊天作为实时通信新范式的挑战，提出了减少比特率同时保持MLLM准确性的方法。

介绍了SC-ADAS，一个集成生成AI组件的框架，支持基于视觉和传感器上下文的自然语言推荐和ADAS控制。

提出了一种新的解缠方法，用于改善神经DNF模型的性能，通过分离编码嵌套规则的节点来实现。

介绍了WildFX，一个用于生成多轨音频混合数据集的管道，支持无缝集成跨平台商业插件。