{"slug": "neural-bayesian-sequential-routing", "title": "Neural Bayesian Sequential Routing", "summary": "Researchers have introduced Neural Bayesian Sequential Routing (NBSR), a framework that models neural inference as active evidence accumulation over a hierarchical Directed Acyclic Graph using a Dirichlet-Categorical conjugate system. The method enables uncertainty quantification, entropy-based early exiting, and cost-aware evidence acquisition by updating belief states through exact conjugate addition from a persistent knowledge oracle. Empirical tests across visual categorization, medical diagnosis, and language modeling show NBSR achieves competitive performance while providing transparent routing traces and resource-rational inference.", "body_md": "arXiv:2605.26147v1 Announce Type: new\nAbstract: Human decision-making is sequential and uncertainty-aware, yet standard neural networks often rely on static, dense forward computation with limited visibility into evidence acquisition, uncertainty evolution, or when computation should stop. We introduce \\textbf{Neural Bayesian Sequential Routing (NBSR)}, a framework that models neural inference as active evidence accumulation over a hierarchical Directed Acyclic Graph (DAG). Within a Dirichlet--Categorical conjugate framework, neural experts query a persistent global knowledge oracle to extract positive evidence vectors, which act as pseudo-counts and update a Dirichlet belief state by exact conjugate addition. Coupled with a Gumbel-Softmax Straight-Through estimator, this update enables hard, path-dependent routing while preserving surrogate gradients for end-to-end training. The resulting Dirichlet precision and entropy provide mechanisms for uncertainty quantification, entropy-based early exiting, OOD abstention, and cost-aware evidence acquisition. We prove that, under strictly positive evidence extraction, total Dirichlet precision increases monotonically along any valid trajectory and marginal predictive variance is bounded, formalizing sequential ``hypothesis sharpening''; under idealized capacity and optimization assumptions, the terminal Dirichlet expectation recovers the Bayes-optimal conditional distribution. Empirical evaluations across visual categorization, structured medical diagnosis, language modeling, partially observable control, and cost-aware Bayesian experimental design show that NBSR achieves competitive predictive performance while providing transparent routing traces, path-dependent evidence attribution, uncertainty-aware decision control, and resource-rational inference. Overall, NBSR offers a mathematically grounded framework for interpretable, modular, and resource-rational agentic AI.", "url": "https://wpnews.pro/news/neural-bayesian-sequential-routing", "canonical_source": "https://arxiv.org/abs/2605.26147", "published_at": "2026-05-27 04:00:00+00:00", "updated_at": "2026-05-27 04:28:36.077735+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "artificial-intelligence", "ai-research"], "entities": ["Neural Bayesian Sequential Routing", "NBSR", "Dirichlet", "Gumbel-Softmax"], "alternates": {"html": "https://wpnews.pro/news/neural-bayesian-sequential-routing", "markdown": "https://wpnews.pro/news/neural-bayesian-sequential-routing.md", "text": "https://wpnews.pro/news/neural-bayesian-sequential-routing.txt", "jsonld": "https://wpnews.pro/news/neural-bayesian-sequential-routing.jsonld"}}