arXiv:2605.23395v1 Announce Type: new Abstract: Compositional energy-based models can generalize to larger combinatorial reasoning problems by reusing a learned factor energy across many local constraints. In our paper, we show that a key bottleneck in compositional reasoning is not composition itself, but the non-convex geometry of the learned energy landscape. To solve this problem, we introduce Convex Compositional Energy Minimization (CCEM), a framework that parameterizes each factor with an input-convex neural network and optimizes the composed energy over a tight convex relaxation of the
The paper, published in 2026, reflects ongoing academic research into fundamental improvements for AI compositional reasoning, a critical area for more robust and generalizable AI systems.
This research addresses a key bottleneck in AI — the ability to reliably generalize learned factors to more complex problems, which has implications for the efficiency and capability of future AI models.
The introduction of Convex Compositional Energy Minimization (CCEM) provides a framework that could lead to more stable and scalable compositional AI, improving reasoning capabilities beyond current limitations.
- · AI researchers
- · AI developers
- · Robotics sector
- · Complex systems automation
- · Developers of non-convex compositional models
Improved compositional reasoning could lead to more efficient and reliable AI agents and systems.
Enhanced agentic AI capabilities could accelerate automation in various white-collar workflows and industrial processes.
More robust, generalizable AI could fundamentally alter design, production, and decision-making across numerous sectors, pushing new forms of intelligence integration.
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Read at arXiv cs.LG