2026.03.06 | MOOSE-Star打破科学发现训练壁垒;DARE让LLM秒变严谨统计助手

2026.03.06 | MOOSE-Star打破科学发现训练壁垒;DARE让LLM秒变严谨统计助手

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【目录】

本期的 15 篇论文如下:

00:32 🚀 MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier(MOOSE-Star:通过打破复杂性壁垒解锁科学发现的可处理训练)

01:50 📊 DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval(DARE:通过分布感知检索实现LLM智能体与R统计生态系统的对齐)

02:39 🧠 SkillNet: Create, Evaluate, and Connect AI Skills(SkillNet:创建、评估与连接AI技能)

03:28 📱 RoboPocket: Improve Robot Policies Instantly with Your Phone(RoboPocket:用手机即时提升机器人策略)

04:15 🎨 HiFi-Inpaint: Towards High-Fidelity Reference-Based Inpainting for Generating Detail-Preserving Human-Product Images(HiFi-Inpaint:面向高保真参考的图像修复,用于生成细节保留的人-物图像)

04:59 🔍 AgentVista: Evaluating Multimodal Agents in Ultra-Challenging Realistic Visual Scenarios(AgentVista:在超挑战性真实视觉场景中评估多模态智能体)

05:37 🔬 SageBwd: A Trainable Low-bit Attention(SageBwd:一种可训练的低比特注意力机制)

06:21 🧠 Large Multimodal Models as General In-Context Classifiers(大型多模态模型作为通用上下文分类器)

07:01 ⚖ MASQuant: Modality-Aware Smoothing Quantization for Multimodal Large Language Models(MASQuant:面向多模态大语言模型的模态感知平滑量化)

07:54 🌍 DreamWorld: Unified World Modeling in Video Generation(DreamWorld:视频生成中的统一世界建模)

08:34 🎬 RealWonder: Real-Time Physical Action-Conditioned Video Generation(RealWonder:基于物理仿真的实时动作条件视频生成)

09:35 🧠 Towards Multimodal Lifelong Understanding: A Dataset and Agentic Baseline(迈向多模态终身理解:数据集与智能体基线)

10:14 🧠 On-Policy Self-Distillation for Reasoning Compression(基于策略自蒸馏的推理压缩方法)

10:55 🤖 KARL: Knowledge Agents via Reinforcement Learning(KARL:基于强化学习的知识智能体)

11:39 🔍 Locality-Attending Vision Transformer(局部性感知视觉Transformer)

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