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End-to-End Goal-Driven Web Navigation
Paper • 1602.02261 • Published -
Learning Language Games through Interaction
Paper • 1606.02447 • Published -
Naturalizing a Programming Language via Interactive Learning
Paper • 1704.06956 • Published -
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Paper • 1802.08802 • Published • 2
Collections
Discover the best community collections!
Collections including paper arxiv:2411.01747
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GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
Paper • 2503.14734 • Published • 7 -
Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
Paper • 2401.02117 • Published • 33 -
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
Paper • 2506.01844 • Published • 158 -
Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding
Paper • 2506.16035 • Published • 89
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DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 192 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 12
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If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 12 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
GAIA: a benchmark for General AI Assistants
Paper • 2311.12983 • Published • 247 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 192
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ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 34 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 12 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 192
-
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 192 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 12
-
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 192 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 12 -
ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 34
-
End-to-End Goal-Driven Web Navigation
Paper • 1602.02261 • Published -
Learning Language Games through Interaction
Paper • 1606.02447 • Published -
Naturalizing a Programming Language via Interactive Learning
Paper • 1704.06956 • Published -
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Paper • 1802.08802 • Published • 2
-
ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 34 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 12 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 192
-
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
Paper • 2503.14734 • Published • 7 -
Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
Paper • 2401.02117 • Published • 33 -
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
Paper • 2506.01844 • Published • 158 -
Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding
Paper • 2506.16035 • Published • 89
-
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 192 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 12
-
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 192 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 12
-
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 192 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 12 -
ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 34
-
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 12 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
GAIA: a benchmark for General AI Assistants
Paper • 2311.12983 • Published • 247 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 192