mlsanityData to 14 Oct 2026

Bayesian Epistemology with Weighted Authority: A Formal Architecture for Truth-Promoting Autonomous Scientific Reasoning

arXiv 2506.16015 ↗First posted 19 Jun 2025cs.AI cs.CL cs.DBcited by 2

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

The exponential expansion of scientific literature has surpassed the epistemic processing capabilities of both human experts and current artificial intelligence systems. This paper introduces Bayesian Epistemology with Weighted Authority (BEWA), a formally structured architecture that operationalises belief as a dynamic, probabilistically coherent function over structured scientific claims. Each claim is contextualised, author-attributed, and evaluated through a system of replication scores, citation weighting, and temporal decay. Belief updates are performed via evidence-conditioned Bayesian inference, contradiction processing, and epistemic decay mechanisms. The architecture supports graph-based claim propagation, authorial credibility modelling, cryptographic anchoring, and zero-knowledge audit verification. By formalising scientific reasoning into a computationally verifiable epistemic network, BEWA advances the foundation for machine reasoning systems that promote truth utility, rational belief convergence, and audit-resilient integrity across dynamic scientific domains.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 2 of its 37 references that are in the map. First posted on arXiv 19 Jun 2025.

Builds on

2 of 37 references.

References · 2

  1. 2020CORD-19: The COVID-19 Open Research Datasetcited by 180
  2. 2020Learning from Noisy Labels with Noise Modeling Networkcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Belief Graphs with Reasoning Zones: Structure, Dynamics, and Epistemic Activationcited by 1
  2. 2026VeriBound: PAC-Bayesian Generalization Bounds for Process Reward Models Trained with Formal Verification Toolscited by 1

Similar, not cited

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