Khastgir Algorithm: A Digital Development Goals-Aligned Framework for Real-Time Hallucination Detection and Reporting

Abstract The exponential deployment of large language models (LLMs) across commercial and public sectors in 2026 and beyond has created an urgent accountability gap that traditional IP frameworks were never designed to address. When these systems generate hallucinations around resultant data in the form of fabricated citations, non-existent precedents, or fictional technical specifications, the resulting misinformation doesn't merely distort discourse but creates chaos to perform the task again from scratch. Factually incorrect output data currently pose significant risks across critical domains including healthcare, legal systems, financial services, and public policy. The hallucination crisis forces us to confront a deeper question: what does it mean to build "responsible" technology when our most advanced creations confidently lie? Through the Khastgir Algorithm's 103 sub-modules, I've always argued that responsibility isn't a compliance checkbox but a living system of interlocking accountabilities—IP rights protecting human creativity, carbon-negative operations preserving planetary boundaries, and DAO governance ensuring no single entity monopolizes truth. Hallucinations expose the moral vacuum in models trained without such frameworks. The current expanded architecture discussed in this paper therefore treats each fabricated output as a teachable moment and not merely flagged and logged, but routed through a global network of stakeholder DAOs who collectively determine the remediation protocol, transforming error correction into a democratic exercise that strengthens rather than erodes public trust in AI systems. This paper expands the foundational Khastgir Algorithm originally designed for AI-mediated intellectual property dispute resolution and time is apt to incorporate Digital Development Goals (DDGs) as one of the governing frameworks for identifying and reporting hallucinated data in real-time. The enhanced comprehensive architecture framework integrates token-level hallucination probes, blockchain-anchored verification mechanisms, and automated reporting pipelines aligned with UNDP's Digital Strategy 2022-2025 objectives and United Nations Sustainable Development Goals. The proposed framework leverages advanced machine learning techniques including attention mechanism analysis, semantic consistency validation, and cross-reference verification, combined with a permissioned blockchain architecture running on the Impula Network. Keywords: Hallucination detection, blockchain, artificial intelligence governance, real-time validation, edge computing, digital development goals, ITU-T X.1819, zero-knowledge proofs, Impula Network, Industry 4.0