An active inference controller selects prompts and search actions for an LLM agent, with experiments showing learned structure in observation matrices and an exploration-to-exploitation shift.
A Meta-Generation framework for Industrial System Generation
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abstract
Generative design is an increasingly important tool in the industrial world. It allows the designers and engineers to easily explore vast ranges of design options, providing a cheaper and faster alternative to the trial and failure approaches. Thanks to the flexibility they offer, Deep Generative Models are gaining popularity amongst Generative Design technologies. However, developing and evaluating these models can be challenging. The field lacks accessible benchmarks, in order to evaluate and compare objectively different Deep Generative Models architectures. Moreover, vanilla Deep Generative Models appear to be unable to accurately generate multi-components industrial systems that are controlled by latent design constraints. To address these challenges, we propose an industry-inspired use case that incorporates actual industrial system characteristics. This use case can be quickly generated and used as a benchmark. We propose a Meta-VAE capable of producing multi-component industrial systems and showcase its application on the proposed use case.
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cs.CL 1years
2024 1verdicts
REJECT 1representative citing papers
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Active Inference for Self-Organizing Multi-LLM Systems: A Bayesian Thermodynamic Approach to Adaptation
An active inference controller selects prompts and search actions for an LLM agent, with experiments showing learned structure in observation matrices and an exploration-to-exploitation shift.