来源:公众号“papertoday”
原文链接:https://mp.weixin.qq.com/s/lBR5yh65e_sWeHTxvYlxoQ
最近刷论文,看到美团 LongCat 团队连发了 3 篇 Agent Skill 技术论文(Skill0、Skill0.5、Skill1),其中 Skill0.5 那个"一半内化、一半外挂"的思路挺有意思——通用技能写进参数,任务技能留在 prompt,简单任务还加了反捷径探测,OOD 场景提升了 13.2%。Agent彻底爆发,美团连发了3篇Skill
MemOCR: Layout-Aware Visual Memory for Efficient Long-Horizon Reasoning
MemOCR:面向高效长程推理的版面感知视觉记忆机制
论文下载:https://arxiv.org/abs/2601.21468
ScaleEnv: Scaling Environment Synthesis from Scratch for Generalist Interactive Tool-Use Agent Training
ScaleEnv: 从零开始构建可扩展的环境合成系统用于通用交互式工具使用智能体的训练
论文下载:https://arxiv.org/abs/2602.06820
AJ-Bench: Benchmarking Agent-as-a-Judge for Environment-Aware Evaluation
AJ-Bench:面向环境感知评估的智能体裁判基准
论文下载:https://arxiv.org/abs/2604.18240
Learning to Self-Verify Makes Language Models Better Reasoners
学习自我验证使语言模型成为更好的推理者
论文下载:https://arxiv.org/abs/2602.07594
AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Condition
AgentNoiseBench:噪声条件下工具使用型大语言模型智能体的鲁棒性基准评测
论文下载:https://arxiv.org/pdf/2602.11348
V_0: A Generalist Value Model for Any Policy at State Zero
V_0:一种适用于任意策略在初始状态下的通用价值模型
论文下载:https://arxiv.org/abs/2602.03584
LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Experts
LUVE:基于双频率专家的潜空间级联超高分辨率视频生成
论文下载:https://arxiv.org/abs/2602.11564
Infinite-World: Scaling Interactive World Models to 1000-Frame Horizons via Pose-Free Hierarchical Memory
Infinite-World:通过无位姿层次化记忆将交互式世界模型扩展至1000帧
论文下载:https://arxiv.org/abs/2602.02393
WildActor: Unconstrained Identity-Preserving Video Generation
WildActor:无约束身份保持视频生成
论文下载:https://arxiv.org/pdf/2603.00586
Navigating the Pareto Frontier of Alignment: Spectrum-Adaptive Fine-Tuning for LLMs
SAFT:面向大语言模型的谱自适应微调方法
论文下载:https://github.com/sjtu-scx/SAFT/blob/main/SAFT.pdf
TRIP-Bench: A Benchmark for Long-Horizon Interactive Agents in Real-World Scenarios
TRIP-Bench:真实场景中长时域交互式智能体的基准评测
论文下载:https://arxiv.org/pdf/2602.01675
InfVSR: Toward Consistency-Driven Streaming Generative Video Super-Resolution
InfVSR:面向一致性驱动的流式生成视频超分辨率
论文下载:https://arxiv.org/pdf/2510.00948
DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation
DRIVE:基于混合分布与检索增强的价值评估出价策略













