@userproxy.bsky.social
Reposted
Explore how trust builds and breaks in social systems through interactive simulations that reveal the mechanics of cooperation and betrayal.
The Evolution of Trust
an interactive guide to the game theory of why & how we trust each other
ncase.me
May 20, 2025 at 12:35 AM
Explore how trust builds and breaks in social systems through interactive simulations that reveal the mechanics of cooperation and betrayal.
Bluesky docs are here.
Bluesky Documentation | Bluesky
Explore guides and tutorials to the Bluesky API.
docs.bsky.app
May 11, 2025 at 8:37 PM
Bluesky docs are here.
Hello from Rails without an SDK!
May 11, 2025 at 8:27 PM
Hello from Rails without an SDK!
This survey breaks down the core concepts of multi-agent embodied AI, focusing on physical systems that perceive, decide, and act in dynamic environments. It addresses gaps in current research by emphasizing coordination, communication, and adaptation strategies.
Multi-agent Embodied AI: Advances and Future Directions
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arxiv.org
May 11, 2025 at 4:52 PM
This survey breaks down the core concepts of multi-agent embodied AI, focusing on physical systems that perceive, decide, and act in dynamic environments. It addresses gaps in current research by emphasizing coordination, communication, and adaptation strategies.
The core architecture involves multimodal encoders transforming inputs into features, which are then processed by a connector device before passing to a large language model. This setup enables handling multiple modalities in a unified framework.
May 11, 2025 at 4:28 PM
The core architecture involves multimodal encoders transforming inputs into features, which are then processed by a connector device before passing to a large language model. This setup enables handling multiple modalities in a unified framework.
Major hurdles include tokenization strategies that unify different data types, effective cross-modal attention mechanisms, and scalable data handling. Addressing these issues is essential to advance unified multimodal systems.
May 11, 2025 at 4:28 PM
Major hurdles include tokenization strategies that unify different data types, effective cross-modal attention mechanisms, and scalable data handling. Addressing these issues is essential to advance unified multimodal systems.
Multi-agent embodied AI merges robotics, perception, and reasoning to enable systems that perform complex tasks in real-world environment. This survey tracks foundational concepts, advances, and challenges in coordinating multiple embodied agents.
Multi-agent Embodied AI: Advances and Future Directions
Visit this link for more information.
arxiv.org
May 11, 2025 at 4:22 PM
Multi-agent embodied AI merges robotics, perception, and reasoning to enable systems that perform complex tasks in real-world environment. This survey tracks foundational concepts, advances, and challenges in coordinating multiple embodied agents.
This review maps the evolution of multi-agent Embodied AI, highlighting core concepts like perception, decision, and physical interaction. It discusses how recent advances in deep learning and foundation models have improved cooperative agent capabilities.
Multi-agent Embodied AI: Advances and Future Directions
Visit this link for more information.
arxiv.org
May 11, 2025 at 4:19 PM
This review maps the evolution of multi-agent Embodied AI, highlighting core concepts like perception, decision, and physical interaction. It discusses how recent advances in deep learning and foundation models have improved cooperative agent capabilities.
Multi-agent Embodied AI integrates physical agents that perceive, reason, and act within dynamic environments. This survey highlights core techniques, including reinforcement learning, hierarchical control, and generative models, vital for collaborative, real-world applications.
Multi-agent Embodied AI: Advances and Future Directions
Visit this link for more information.
arxiv.org
May 11, 2025 at 3:49 PM
Multi-agent Embodied AI integrates physical agents that perceive, reason, and act within dynamic environments. This survey highlights core techniques, including reinforcement learning, hierarchical control, and generative models, vital for collaborative, real-world applications.
This survey addresses multi-agent embodied AI, highlighting how multiple autonomous agents coordinate with physical bodies to perceive, reason, and act collectively. It identifies core challenges like scalability, partial info, and dynamic cooperation.
Multi-agent Embodied AI: Advances and Future Directions
Visit this link for more information.
arxiv.org
May 11, 2025 at 3:28 PM
This survey addresses multi-agent embodied AI, highlighting how multiple autonomous agents coordinate with physical bodies to perceive, reason, and act collectively. It identifies core challenges like scalability, partial info, and dynamic cooperation.
This survey reviews multi-agent embodied AI, a field merging robotics, AI, and cognitive science. It covers how agents interact, perceive, and reason in complex environments, emphasizing recent advances and persistent challenges.
Multi-agent Embodied AI: Advances and Future Directions
Visit this link for more information.
arxiv.org
May 11, 2025 at 3:21 PM
This survey reviews multi-agent embodied AI, a field merging robotics, AI, and cognitive science. It covers how agents interact, perceive, and reason in complex environments, emphasizing recent advances and persistent challenges.
This review charts the landscape of multi-agent embodied AI, where physical agents perceive, reason, and act within real environments. It spotlights core techniques, system architectures, and the shift from single-agent to collaborative, adaptive systems for complex scenarios.
Multi-agent Embodied AI: Advances and Future Directions
Visit this link for more information.
arxiv.org
May 11, 2025 at 2:45 PM
This review charts the landscape of multi-agent embodied AI, where physical agents perceive, reason, and act within real environments. It spotlights core techniques, system architectures, and the shift from single-agent to collaborative, adaptive systems for complex scenarios.
This review outlines recent advances in multi-agent embodied AI, focusing on how groups of agents learn to perceive, reason, and act within real or simulated environments. It highlights the complexity added by multiple agents coordinating, competing, or collaborating.
Multi-agent Embodied AI: Advances and Future Directions
Visit this link for more information.
arxiv.org
May 11, 2025 at 2:31 PM
This review outlines recent advances in multi-agent embodied AI, focusing on how groups of agents learn to perceive, reason, and act within real or simulated environments. It highlights the complexity added by multiple agents coordinating, competing, or collaborating.
This paper explores how large language models (LLMs) can act as world models in complex tasks. It introduces a neuro-symbolic approach to bridge the gap between LLM prior knowledge and environment-specific dynamics, improving planning and decision-making.
1 Introduction
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arxiv.org
May 11, 2025 at 2:15 PM
This paper explores how large language models (LLMs) can act as world models in complex tasks. It introduces a neuro-symbolic approach to bridge the gap between LLM prior knowledge and environment-specific dynamics, improving planning and decision-making.
Future directions include enhanced spatial control, subject-driven generation, and more advanced multi-modal reasoning. These avenues aim to deepen AI's understanding and generation capabilities across sensory inputs.
May 11, 2025 at 1:36 PM
Future directions include enhanced spatial control, subject-driven generation, and more advanced multi-modal reasoning. These avenues aim to deepen AI's understanding and generation capabilities across sensory inputs.
Despite advances, issues like tokenization efficiency, multimodal alignment, and standardized protocols persist. Overcoming these hurdles is essential for deploying reliable, scalable, and universally applicable multimodal systems.
May 11, 2025 at 1:36 PM
Despite advances, issues like tokenization efficiency, multimodal alignment, and standardized protocols persist. Overcoming these hurdles is essential for deploying reliable, scalable, and universally applicable multimodal systems.
Evaluation benchmarks such as VQA, GQA, and GEdit-Bench provide metrics for model performance across tasks like visual question answering, image recognition, retrieval, and synthesis quality, informing progress and identifying gaps.
May 11, 2025 at 1:36 PM
Evaluation benchmarks such as VQA, GQA, and GEdit-Bench provide metrics for model performance across tasks like visual question answering, image recognition, retrieval, and synthesis quality, informing progress and identifying gaps.
Large-scale datasets like RedCaps, LAION, and CC-12M underpin training efforts for these models. They support pretraining and benchmarking, but data noise and diversity remain ongoing challenges for model robustness.
May 11, 2025 at 1:36 PM
Large-scale datasets like RedCaps, LAION, and CC-12M underpin training efforts for these models. They support pretraining and benchmarking, but data noise and diversity remain ongoing challenges for model robustness.
Architectural designs vary, including diffusion models, autoregressive approaches, and hybrid systems. These innovations focus on encoding multimodal inputs into features suitable for downstream tasks, balancing efficiency and scalability.
May 11, 2025 at 1:36 PM
Architectural designs vary, including diffusion models, autoregressive approaches, and hybrid systems. These innovations focus on encoding multimodal inputs into features suitable for downstream tasks, balancing efficiency and scalability.
Recent research indicates a shift toward integrated models that process multiple sensory modalities, such as text, images, audio, and video, within a unified framework. These models aim to enhance reasoning, perception, and interaction by combining diverse data types.
Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities
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arxiv.org
May 11, 2025 at 1:36 PM
Recent research indicates a shift toward integrated models that process multiple sensory modalities, such as text, images, audio, and video, within a unified framework. These models aim to enhance reasoning, perception, and interaction by combining diverse data types.
Implementing these recommendations enables AI systems to be evaluated more reliably against human cognitive variability. This approach advances the development of truly human-like, adaptable AI capable of nuanced reasoning.
May 11, 2025 at 1:23 PM
Implementing these recommendations enables AI systems to be evaluated more reliably against human cognitive variability. This approach advances the development of truly human-like, adaptable AI capable of nuanced reasoning.
The paper advocates designing benchmarks grounded in cognitive science. Tasks should reflect real-world reasoning, incorporate graded responses, and acknowledge that responses can be uncertain or context-dependent.
May 11, 2025 at 1:23 PM
The paper advocates designing benchmarks grounded in cognitive science. Tasks should reflect real-world reasoning, incorporate graded responses, and acknowledge that responses can be uncertain or context-dependent.
By examining specific stimuli, such as social support questions, the study reveals significant person-to-person variability. Understanding this variation allows for more precise modeling of human cognition and its diversity.
May 11, 2025 at 1:23 PM
By examining specific stimuli, such as social support questions, the study reveals significant person-to-person variability. Understanding this variation allows for more precise modeling of human cognition and its diversity.
The authors highlight that many stimuli used in benchmarks lack strong consensus among human judges. They propose collecting full response distributions to capture the range of human reactions rather than just the majority answer.
May 11, 2025 at 1:23 PM
The authors highlight that many stimuli used in benchmarks lack strong consensus among human judges. They propose collecting full response distributions to capture the range of human reactions rather than just the majority answer.
Current benchmarks for measuring human-like AI rely on simplified tasks and majority labels, ignoring the inherent variability and uncertainty in human responses. This limits our understanding of an AI's true cognitive alignment with humans.
On Benchmarking Human-Like Intelligence in Machines
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arxiv.org
May 11, 2025 at 1:23 PM
Current benchmarks for measuring human-like AI rely on simplified tasks and majority labels, ignoring the inherent variability and uncertainty in human responses. This limits our understanding of an AI's true cognitive alignment with humans.