From Hallucination to Reliability: Path-Level Mechanisms for Trustworthy Knowledge Generation in Foundation Models (International Journal of Artificial Intelligence Engineering and Systems, 2026)
Foundation models have demonstrated remarkable generative capabilities across modalities, yet their propensity to produce factually incorrect or logically inconsistent outputs—commonly termed hallucination—threatens their deployment in safety-critical and knowledge-intensive domains. This paper reframes the problem of hallucination not merely as a training data or fine-tuning deficiency but as a systemic challenge rooted in the architectural pathways through which internal representations are composed into outputs. We argue that achieving trustworthy knowledge generation requires moving beyond surface-level output filtering to path-level mechanisms that intervene directly on the internal circuits, attention patterns, and activation subspaces responsible for factual reasoning. Drawing on recent advances in mechanistic interpretability, activation steering, and circuit-level editing, we examine how such path-level strategies reconfigure the reliability trade space. We present a system-level analysis of the structural trade-offs involved in implementing path-level interventions, including computational overhead, latency, modularity, and compatibility with existing alignment pipelines. The discussion extends to infrastructure requirements for real-time path-level monitoring, governance challenges related to auditability and transparency, and fairness implications arising from representational steering. We further consider the deployment lifecycle, sustainability of intervention mechanisms under distribution shift, and the role of regulatory frameworks in certifying path-level safety guarantees. By synthesizing cross-disciplinary perspectives from systems engineering, machine learning interpretability, and AI policy, this paper offers a comprehensive roadmap for transitioning foundation models from hallucination-prone generators to reliable, auditable, and controllable knowledge systems. We conclude that path-level mechanisms represent a necessary architectural evolution for foundation model trustworthiness, demanding new infrastructural investments, governance protocols, and interdisciplinary evaluation standards.
Keywords: foundation models, hallucination, reliability, path-level mechanisms, trustworthy AI, mechanistic interpretability, safety