Mitigating LLM Hallucinations through Domain-Grounded Tiered Retrieval
Large Language Models (LLMs) have achieved unprecedented fluency but remain susceptible to "hallucinations" - the generation of factually incorrect or ungrounded content. This limitation is particularly critical in high-stakes domains where reliability is paramount. We propose a domain-grounded tiered retrieval and verification architecture designed to systematically intercept factual inaccuracies by shifting LLMs from stochastic pattern-matchers to verified truth-seekers. The proposed framework utilizes a four-phase, self-regulating pipeline implemented via LangGraph: (I) Intrinsic Verification with Early-Exit logic to optimize compute, (II) Adaptive Search Routing utilizing a Domain Det...
| Authors | Md. Asraful Haque, Aasar Mehdi, Maaz Mahboob et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 March 2026 |
| Last updated at source | 25 March 2026 |
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