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:2601.12053v2 Announce Type: replace-cross Abstract: While foundation models have achieved remarkable results across a diversity of domains, they still rely on human-generated data, such as text...
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.
The paper proposes a single variational principle that explains how gating mechanisms, their dynamics, and neural implementations for behavioral composition can be unified. This principle yields softmax gating, an energy‑based dynamical system with guaranteed convergence, and a recurrent neural network model with context‑dependent, local interactions. Experiments across collective behavior, human decision‑making, and layered control show that the mechanism reproduces known behavioral patterns, offers interpretable accounts of behavior combination, and matches or outperforms existing methods.
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.
The paper investigates how multi‑neuromodulatory dynamics—driven by dopamine, acetylcholine, serotonin, and noradrenaline—can improve artificial neural networks’ continuous adaptive learning and mitigate catastrophic forgetting. It demonstrates that neuromodulators interact across multiple spatial and temporal scales, creating a complex many‑to‑many mapping between neuromodulators and tasks. The authors propose neuromodulation‑aware learning rules, architectural formulations, and a conceptual Go/No‑Go study to illustrate how biologically inspired mechanisms can enhance ANN robustness and adaptability.
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.