时长:
7分钟
播放:
112
发布:
2周前
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【赞助商】
通勤路上就听AI每周谈。AI每周谈,每周带你回顾上周AI大事
传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd
【目录】
本期的 8 篇论文如下:
[00:30] ⚙ ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development(ABC-Bench:面向真实世界开发的智能体后端编码基准测试)
[01:15] 🧠 Multiplex Thinking: Reasoning via Token-wise Branch-and-Merge(多路思考:基于词元级分支与合并的推理方法)
[02:13] 🕺 CoDance: An Unbind-Rebind Paradigm for Robust Multi-Subject Animation(CoDance:一种用于鲁棒多主体动画的解绑-重绑范式)
[03:01] 🧭 The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models(助手轴:定位与稳定语言模型的默认人格)
[03:30] 🧠 Spurious Rewards Paradox: Mechanistically Understanding How RLVR Activates Memorization Shortcuts in LLMs(虚假奖励悖论:从机制上理解RLVR如何激活LLM中的记忆捷径)
[04:21] 🔬 SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature(SIN-Bench:在长上下文多模态科学交织文献中追踪原生证据链)
[05:08] 🧭 YaPO: Learnable Sparse Activation Steering Vectors for Domain Adaptation(YaPO:用于领域适应的可学习稀疏激活导向向量)
[05:56] 🧬 Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation(Medical SAM3:面向通用提示驱动医学图像分割的基础模型)
【关注我们】
您还可以在以下平台找到我们,获得播客内容以外更多信息
小红书: AI速递
通勤路上就听AI每周谈。AI每周谈,每周带你回顾上周AI大事
传送门 🔗https://www.xiaoyuzhoufm.com/podcast/688a34636f5a275f1cba40fd
【目录】
本期的 8 篇论文如下:
[00:30] ⚙ ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development(ABC-Bench:面向真实世界开发的智能体后端编码基准测试)
[01:15] 🧠 Multiplex Thinking: Reasoning via Token-wise Branch-and-Merge(多路思考:基于词元级分支与合并的推理方法)
[02:13] 🕺 CoDance: An Unbind-Rebind Paradigm for Robust Multi-Subject Animation(CoDance:一种用于鲁棒多主体动画的解绑-重绑范式)
[03:01] 🧭 The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models(助手轴:定位与稳定语言模型的默认人格)
[03:30] 🧠 Spurious Rewards Paradox: Mechanistically Understanding How RLVR Activates Memorization Shortcuts in LLMs(虚假奖励悖论:从机制上理解RLVR如何激活LLM中的记忆捷径)
[04:21] 🔬 SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature(SIN-Bench:在长上下文多模态科学交织文献中追踪原生证据链)
[05:08] 🧭 YaPO: Learnable Sparse Activation Steering Vectors for Domain Adaptation(YaPO:用于领域适应的可学习稀疏激活导向向量)
[05:56] 🧬 Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation(Medical SAM3:面向通用提示驱动医学图像分割的基础模型)
【关注我们】
您还可以在以下平台找到我们,获得播客内容以外更多信息
小红书: AI速递
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