Bayesian Epistemology with Weighted Authority: A Formal Architecture for Truth-Promoting Autonomous Scientific Reasoning (2506.16015v1)

Abstract: 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.

• The paper introduces BEWA to rigorously model scientific claims using Bayesian epistemology and authority weighting for truth promotion.
• It employs a modular belief network with dynamic decay and contradiction analysis to ensure epistemic integrity and prevent misinformation.
• Experimental simulations show high truth convergence and effective evidence propagation, reducing contradiction density in scientific datasets.

Formal Architecture for Truth-Promoting Autonomous Scientific Reasoning: An Analysis of BEWA

The paper introduces Bayesian Epistemology with Weighted Authority (BEWA), setting forth a computational architecture aimed at principled, truth-oriented modelling and autonomous reasoning over scientific knowledge. The system is motivated by the proliferation of scientific literature, the resulting epistemic overload, and the inadequacies in both human and conventional AI mechanisms for claim evaluation and knowledge integration.

Epistemic and Architectural Foundations

BEWA’s foundation is a strict adherence to Bayesian epistemology, treating each scientific claim as a probabilistically weighted proposition within a dynamically updated belief network. Every belief assignment and update is governed by the Kolmogorov axioms, Bayesian conditionalisation, and decision-theoretic rationality, ensuring coherence and Dutch Book immunity. This is coupled with first-class structural mechanisms for authority weighting, replication-sensitive citation metrics, temporal decay, and contradiction analysis. The architecture is thus modular, compositional, and reversible: all epistemic states are recalibrated as evidence, context, or authority evolve.

Notably, BEWA foregrounds epistemic integrity—claims are ranked by a formal truth utility function, not by raw frequency, citation popularity, or author prestige. System propagation is strictly gated by minimum truth utility thresholds, aiming to prevent citation cascades, echo-chamber reinforcement, and the entrenchment of unreplicated or systematically biased findings.

Canonical Claim Structuring and Ingestion

Scientific input is rigorously filtered. Sources are limited to authoritative, peer-reviewed, indexed publications with cryptographically verifiable provenance. Each claim and author is uniquely identified through canonical hashing schemes, leveraging ORCID, disambiguated metadata, and standardized ontologies. Claims are parsed and normalised into structured propositional forms—a process combining neural semantic parsing with logic-constrained validation—to ensure logical deduplication and enable cross-claim reasoning at scale.

The system’s ingestion and data structuring explicitly address common pitfalls in scientific NLP and information retrieval, including author name disambiguation, claim aliasing, and cross-domain semantic drift. Metadata completeness and integrity are cryptographically enforced, ensuring all inferential operations are anchored to non-repudiable records.

Bayesian Belief Update and Decay Dynamics

BEWA operationalises belief as a dynamically evolving Bayesian posterior. Each claim’s initial prior is determined by a composite of authorial credibility (replication rates, retraction history, citation-normalised output), venue reliability, methodological rigor, and historical base rates for comparable claims. Evidence ingestion (replications, citations, contradictions) triggers belief updates via calibrated likelihood functions, with explicit regularisation and volatility constraints to dampen spurious or adversarial input.

Notably, belief state decay is encoded as a mandatory, time-sensitive process: confidence in claims diminishes unless actively reinforced through timely, independent replication or high-quality citations. The system supports replicative reset—successful replications can halt or reverse decay trajectories, restoring epistemic weight to claims with cumulative, cross-institutional validation. Contradictions are processed using entropy-maximising rewiring, demoting mutually inconsistent claims below threshold and quarantining clusters with irresolvable instability.

Authority Modelling and Citation Semantics

Departing from simplistic bibliometric analysis, BEWA deploys a nuanced authorial scoring model. Authors' prior reliability is measured as a bounded, monotonic function of empirical replication, retraction frequency, peer review participation, and field-normalised citation influence. Retractions are persistently penalised, but epistemic recovery is possible through a track record of subsequent, independently replicated work. Reputation effects are non-linear, with explicit anti-gaming mechanisms (e.g., logarithmic citation impact).

Citation is treated as a time-decaying, context-sensitive epistemic signal. Each citation’s effect is tempered by the authority and semantic proximity of the citing source, the field-specific decay rate, and redundancy penalties for clustered citation. Replication, by contrast, is the primary epistemic elevator, with semantic equivalence models enabling reinforcement propagation across logically or methodologically aligned claims.

Probation, Decay, and Dynamic Triage

Crucially, BEWA enforces a probationary period for all new claims. Newly ingested assertions are initially assigned low epistemic influence and must accumulate corroboration before integrating into the broader propagation network. Claims failing to achieve minimal replication or citation thresholds within the probation window undergo aggressive decay and are demoted to archival status.

Isolation and decay rates are dynamic, depending on the degree of network integration and ongoing engagement. The system ensures that neglected or isolated claims do not artificially persist as epistemic deadweight, reflecting the principle that belief is conferred and maintained only through continued relevance and scrutiny.

Cross-Claim Graph Structure and Conflict Management

BEWA operationalises a semantic-logical-evidential linkage graph, embedding claims in a directed epistemic manifold with weighted edges for semantic similarity, logical entailment, and shared evidence. Belief propagation is achieved through generalised Bayesian network updates, allowing non-monotonic revision upon contradiction, retraction, or major replication events.

Local and global contradictions are efficiently detected via adjacency matrix analysis and spectral partitioning; epistemic clusters with high instability scores are quarantined or split until resolution. This architecture resists epistemic contagion and enables granular management of high-uncertainty zones, preventing runaway propagation of contradictory claims.

Truth Utility, Risk Sensitivity, and Application Alignment

A major innovation is the explicit truth utility function, quantifying the epistemic value of claims beyond probabilistic confidence. Claims are prioritised for recommendation, further evaluation, or application based on their marginal contribution to system-wide truth convergence and risk-aware epistemic loss. In application-critical contexts (e.g., clinical reasoning, policy), risk profiles modulate both the weighting and propagation of belief, ensuring that high-stakes decisions are driven by maximally robust knowledge.

This utility-based prioritisation extends to application-level interfaces, where claims are filtered and ranked according to contextual risk, utility, and decision relevance, rather than surface epistemic strength alone.

Security and Provenance Infrastructure

All claims, belief updates, and audit logs are cryptographically anchored using Merkle hashing and signature schemes, providing tamper-evident, publicly verifiable traces. Provenance, authorship, and revision history are strictly versioned and linked, with zero-knowledge proof support for sensitive domains. This architecture ensures auditability, forensic traceability, and non-repudiability, foundational for institutional and regulatory adoption.

Numerical Evaluation and Empirical Considerations

Experimental simulations on both synthetic and real-world corpora demonstrate that BEWA achieves high rates of truth convergence, suppresses contradiction density, and yields significant belief uplift for replication-confirmed claims. Semantic equivalence scoring achieves F1 scores >0.93, and system-wide belief entropy decreases over time, reflecting progressive epistemic consolidation. Contradiction handling and cluster quarantine reduce epistemic instability, confirming the efficacy of BEWA’s conflict management protocols.

Limitations

Despite its formal robustness, BEWA inherits key challenges: semantic ambiguity in claim normalisation, susceptibility to adversarial or low-quality inputs in the absence of external truth anchoring, and computational scalability in large, cyclically coupled networks. The architecture relies on high-quality ontologies and robust cross-domain semantic models, limiting performance in underannotated or highly interdisciplinary areas. Additionally, while cryptographic infrastructure ensures integrity, it cannot directly guarantee correctness in the face of erroneous but properly anchored claims.

Implications and Prospects

BEWA marks a substantive advance in formalising autonomous epistemic processing for scientific AI. By encoding core epistemological virtues—probabilistic caution, replicability, contradiction management, and provenance—within a scalable, auditable system, it enables computational agents to reason over scientific literature with unprecedented discipline and transparency.

Practically, BEWA can underpin:

• Rigorous scientific knowledge bases with automated claim vetting, decay, and contradiction resolution.
• Research discovery tools that prioritise high-utility, replicated findings.
• Transparent decision-support systems for high-risk domains (medicine, engineering, policy).
• Automated meta-research with cross-domain evidence synthesis capabilities.
• Forensic auditing of retractions, corrections, and epistemic drift in institutional corpora.

From a theoretical perspective, the architecture opens avenues for integrating modal and counterfactual logics, causal inference frameworks, and automated model checking into autonomous scientific reasoning agents. Its emphasis on auditability and formal traceability makes it compatible with emerging regulatory requirements in AI governance and scientific data stewardship.

Future Directions

Rich extensions include: incorporating higher-order modal reasoning, automated scientific hypothesis generation, integration with domain-specific mechanistic models, and training of neural epistemic agents under BEWA constraints. Large-scale deployment will require optimisation of graph algorithms for belief propagation, contradiction localisation, and visual analytics for human-in-the-loop oversight.

In summary, BEWA constitutes a methodologically rigorous, technically grounded, and practically extensible approach to computational epistemology within AI, establishing a new standard for principled scientific reasoning at scale.