arXiv:2605.26419v1 Announce Type: new Abstract: Amortized inference promises fast test-time Bayesian inference, but existing methods are inherently tied to fixed models. Extending amortization to unseen models typically requires retraining or costly test-time finetuning. In this paper, we ask: is it possible to build a single inference network capable of generalizing across varying priors, likelihoods, and dimensionality? We introduce Amortized Factor Inference Networks (AFINs), a family of encode-merge-decode inference networks built on dimension-independent modules that map a model specifica
The proliferation of complex AI models and the demand for adaptable, efficient inference mechanisms necessitate innovations like AFINs to overcome limitations of fixed model dependency.
This research addresses a fundamental bottleneck in Bayesian inference, enabling AI systems to generalize better across vastly different models without costly retraining, which accelerates development and deployment.
The ability to perform amortized inference across varying priors, likelihoods, and dimensionality means AI models can be more flexible and widely applicable to diverse, unseen problems.
- · AI researchers
- · Generative AI platforms
- · Data scientists
- · Robotics
- · Retraining-heavy inference pipelines
- · Fixed-model AI applications
More efficient and versatile Bayesian inference becomes broadly accessible for complex AI applications.
Reduced computational costs and development time for deploying AI models in dynamic environments due to lower retraining requirements.
Accelerated development of more adaptive and autonomous AI agents capable of reasoning under uncertainty in novel situations.
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Read at arXiv cs.LG