| 1 |
Situated Instruction Following |
提出情境化指令跟随，解决具身智能体在真实场景中理解人类意图的挑战 |
instruction following |
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| 2 |
Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows? |
Spider2-V：评估多模态Agent在自动化数据科学与工程工作流中的能力 |
multimodal |
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| 3 |
Transforming Agency. On the mode of existence of Large Language Models |
分析大型语言模型（LLMs）的本体论特征，并探讨其作为智能体的存在模式 |
large language model |
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| 4 |
Building Intelligence Identification System via Large Language Model Watermarking: A Survey and Beyond |
基于大语言模型水印技术构建智能身份识别系统：综述与展望 |
large language model |
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| 5 |
IDEAL: Leveraging Infinite and Dynamic Characterizations of Large Language Models for Query-focused Summarization |
IDEAL：利用大语言模型的无限和动态特性进行面向查询的摘要生成 |
large language model |
✅ |
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| 6 |
Making New Connections: LLMs as Puzzle Generators for The New York Times' Connections Word Game |
利用大型语言模型生成《纽约时报》Connections文字游戏谜题 |
large language model |
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| 7 |
AstroMLab 1: Who Wins Astronomy Jeopardy!? |
AstroMLab 1：构建天文学基准数据集，评估大型语言模型在天文学领域的表现。 |
large language model |
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| 8 |
Sibyl: Simple yet Effective Agent Framework for Complex Real-world Reasoning |
Sibyl：一种简单而有效的Agent框架，用于复杂的现实世界推理 |
large language model |
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