arXiv AI
Jul 10
Architecture Generalization with MetaNCA
arXiv:2607. 07743v1 Announce Type: cross Abstract: Self-organization is an emergent property of life, driven by the collective behavior of individual components acting on local information.
Intelligence Foundation Model: A New Perspective to Approach Artificial General Intelligence
arXiv:2511. 10119v4 Announce Type: replace Abstract: We propose a new perspective for approaching artificial general intelligence (AGI) through an intelligence foundation model (IFM).
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arXiv AI
Jul 16
Grounded world models in biological organisms and future embodied AI
arXiv:2607. 13560v1 Announce Type: cross Abstract: Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data.
arXiv AI
Jun 17
Critique of World Model: A Generative Latent Prediction Architecture for World Modeling
arXiv:2507. 05169v4 Announce Type: replace-cross Abstract: World Model, the algorithmic simulator of the real-world environment which biological agents experience and act upon, has been an emerging topic in recent years due to the rising need to develop virtual agents with artificial (general) intelligence.
arXiv AI
Jun 19
Critique of World Model
arXiv:2507. 05169v5 Announce Type: replace-cross Abstract: World Model, the algorithmic simulator of the real-world environment which biological agents experience and act upon, has been an emerging topic in recent years due to the rising need to develop virtual agents with artificial (general) intelligence.
arXiv Machine Learning
Jun 2
LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification
arXiv:2412. 12036v2 Announce Type: replace Abstract: System identification, the process of deriving mathematical models of dynamical systems from observed input-output data, has undergone a paradigm shift with the advent of learning-based methods.
arXiv Machine Learning
Jun 2
World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications
arXiv:2606. 00133v1 Announce Type: new Abstract: World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artificial general intelligence, enabling agents to predict, plan, and reason within learned representations.
arXiv AI
Jun 24
Topological Neural Dynamics: A Neuron-wise Framework for Sequence Modeling
arXiv:2606. 21295v2 Announce Type: replace-cross Abstract: Existing sequence models, including RNNs, LSTMs, continuous-time networks, and Transformers, share a common structural principle: layer-wise dynamics, where all neurons in the same layer co-evolve through a shared parameterized operator, leaving individual neurons no freedom to evolve independently.
arXiv AI
Jun 12
A Tutorial on World Models and Physical AI
arXiv:2606. 12783v1 Announce Type: new Abstract: World modeling is emerging as a central principle for building intelligent systems capable of prediction, reasoning, and decision making.
arXiv AI
Jul 31
Cognitive Convergence: Deep Similarities Between Large Language Models and Human Cognition
arXiv:2607. 26179v1 Announce Type: cross Abstract: LLMs are widely regarded as alien intelligences, systems whose cognitive operations are fundamentally unlike our own.
arXiv AI
Jun 18
Structured Representation Learning with Locally Linear Embeddings and Adaptive Feature Fusion
arXiv:2606. 18469v1 Announce Type: cross Abstract: Neuroscientific research has revealed that the brain encodes complex behaviors by leveraging structured, low-dimensional manifolds and dynamically fusing multiple sources of information through adaptive gating mechanisms.
arXiv AI
Jun 30
Unified Complex-valued Neural Network: A Magnitude-Phase Computational Model for Event-Driven Neuromorphic Learning
arXiv:2606. 29099v1 Announce Type: cross Abstract: Artificial neural networks (ANN) provide accurate continuous-valued representation, whereas spiking neural networks (SNN) offer event-driven temporal processing, yet both paradigms face limitations when value encoding and timing dynamics must be learned within a single computational structure.
arXiv AI
Jun 16
Harnessing cortical geometry, wiring, and function as inductive biases for recurrent neural networks
arXiv:2606. 14975v1 Announce Type: cross Abstract: How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning.