{"total":13,"rows":[{"id":"2303.03378","title":"PaLM-E: An Embodied Multimodal Language Model","created":"2023-03-06","cited_by_count":2071,"area":"mllm"},{"id":"2502.09560","title":"EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents","created":"2025-02-13","cited_by_count":221,"area":"mllm"},{"id":"2310.08588","title":"Octopus: Embodied Vision-Language Programmer from Environmental Feedback","created":"2023-10-12","cited_by_count":73,"area":"mllm"},{"id":"2408.02544","title":"Caution for the Environment: Multimodal LLM Agents are Susceptible to Environmental Distractions","created":"2024-08-05","cited_by_count":56,"area":"mllm"},{"id":"2310.02071","title":"Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and Beyond","created":"2023-10-03","cited_by_count":51,"area":"mllm"},{"id":"2402.02651","title":"Vision-Language Models Provide Promptable Representations for Reinforcement Learning","created":"2024-02-05","cited_by_count":41,"area":"mllm"},{"id":"2503.16365","title":"JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse","created":"2025-03-20","cited_by_count":28,"area":"mllm"},{"id":"2506.21252","title":"Agent-RewardBench: Towards a Unified Benchmark for Reward Modeling across Perception, Planning, and Safety in Real-World Multimodal Agents","created":"2025-06-26","cited_by_count":20,"area":"mllm"},{"id":"2407.15295","title":"VideoGameBunny: Towards vision assistants for video games","created":"2024-07-21","cited_by_count":11,"area":"mllm"},{"id":"2604.05533","title":"Experience Transfer for Multimodal LLM Agents in Minecraft Game","created":"2026-04-07","cited_by_count":5,"area":"mllm"},{"id":"2512.13043","title":"GTR-Turbo: Merged Checkpoint is Secretly a Free Teacher for Agentic VLM Training","created":"2025-12-15","cited_by_count":3,"area":"mllm"},{"id":"2508.10955","title":"Empowering Multimodal LLMs with External Tools: A Comprehensive Survey","created":"2025-08-14","cited_by_count":2,"area":"mllm"},{"id":"2503.13055","title":"Mitigating Cross-Modal Distraction and Ensuring Geometric Feasibility via Affordance-Guided and Self-Consistent MLLMs for Task Planning in Instruction-Following Manipulation","created":"2025-03-17","cited_by_count":0,"area":"mllm"}],"next_cursor":null}