CEGI-S: A Counterfactual Evidence-Gated Intelligence Framework for Reliable and Self-Correcting Large Language Models

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Large Language Models (LLMs) are increasingly being used for question answering, education, research assistance, document analysis, and other knowledge-based applications.Their ability to generate fluent and meaningful responses makes them useful in many practical situations.However, LLMs can also produce information that is incorrect, incomplete, or not supported by reliable evidence.These unsupported outputs, commonly called hallucinations, can reduce user trust and become a serious problem in applications where factual accuracy is important.Retrieval-Augmented Generation (RAG) improves LLM responses by providing external information during generation.However, the retrieval of relevant documents does not necessarily mean that the final answer is fully supported by those documents.Retrieved sources may be incomplete, contradictory, outdated, or only partially relevant to the claims generated by the model.Therefore, a separate verification mechanism is required before the final response is delivered.This paper proposes CEGI-S (Counterfactual Evidence-Gated Intelligence with Support-Conflict Scoring), an evidence-aware framework for improving the reliability of LLMgenerated responses.The framework introduces a verification layer between response generation and final answer delivery.It divides the generated response into individual claims, connects the claims with retrieved evidence, checks agreement and conflict among sources, and uses counterfactual challenges to examine the stability of the generated answer.These signals are combined into a proposed Support-Conflict Evidence Score (SCES).Based on SCES, CEGI-S can select one of three actions: accept the response when sufficient evidence is available, retrieve additional evidence and regenerate the response when the evidence is uncertain or incomplete, or abstain when a reliable answer cannot be established.The proposed framework will be evaluated against conventional LLM and RAGbased approaches using factuality, hallucination rate, evidence support, answer relevance, calibration, abstention quality, latency, and computational cost.The main aim of this research is to investigate whether combining multiple evidence-related signals can provide a practical decision layer for more reliable and responsible generative AI systems.