Generative Models

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A Compositional Framework for Open-ended Intelligence
Jun 13, 2026Ida Momennejad, Roberta RaileanuIntelligence Structural Generalization
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Real-Time Language Model Jamming: A Case Study for Live Music Accompaniment Generation
Jun 10, 2026Bowen Zheng, Andrew H. Yang, Jiaqi Ruan +5Conversational Music Recommendation Real-Time Systems
Flow Matching with In-Context Priors for Out-of-Distribution Brain Dynamics
Jun 10, 2026Sam Gijsen, Michał Łukomski, Marc-André Schulz +1Generative Models Human Brain Networks
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Jun 6, 2026Kelly McConvey, Jalehsadat Mahdavimoghaddam, Nima Jamali +8Multimedia Forensics Text Corpora
Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients
Jun 5, 2026Stefan Ivanovic, Ge Liu, Mohammed El-KebirIntermediate Latent States Latent Structure
Where Rectified Flows Leak: Characterising Membership Signals Along the Interpolation Path
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Jun 13, 2026 · cs.LGJ/K move · Enter open · S save

A Compositional Framework for Open-ended Intelligence

Ida Momennejad, Roberta Raileanu

Open-ended intelligence is the capacity to adapt to novel problems and environments that are substantially different from those in training. A mathematics of open-ended intelligence requires two pillars: first, a minimal set of representational primitives (e.g., states, actions) and algorithmic primitives (e.g., nearest neighbor); and second, an acquired compositional grammar for selection, recursion, and branching that produces sequences of operations and recurring motifs. We formalize open-ended intelligence in terms of the compositional closure induced by a finite primitive set

P

and a set of composition operators

C

. We characterize properties of the induced closure

\mathcal{L}(P,C)

that support unbounded compositional generation across families of tasks and worlds. The closure of the two pillars yields infinite adaptive responses across a wide range of settings. The mathematics supports complementary research agendas, including evaluation metrics for explanation and interpretability, and novel architectures where compositional generalization is native. We propose next primitive prediction (NPP) as a novel architectural objective, where training encourages the acquisition of reusable algorithmic primitives and their compositional grammar, such that new solutions are generated through recombination. Given such an objective, curriculum learning and self-play can enable lifelong learning, expanding the closure by discovering reusable primitives and transition motifs across settings. We ground the framework through case studies in physics, evolution, and neuroscience.

Intelligence Structural Generalization Generative Framework Closure

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