What question did this study set out to answer?

The central aim is to establish a control framework for managing hallucination risk in language models during inference.

May 1, 2026Open Access

AKRM-RIR: Recursive Instability Regulation for Hallucination-Aware Language Model Inference

Key Points

The central aim is to establish a control framework for managing hallucination risk in language models during inference.
Developed a model-agnostic inference-time control system named AKRM-RIR.
Estimated epistemic reliability scores and computed instability functions to regulate outputs.
Implemented techniques like refusal gating and recursive smoothing under uncertainty conditions.
Preliminary findings indicate effective management of hallucination risks during inference.
Results are based on a small controlled benchmark and are not fully validated on a larger scale.

Abstract

This record contains the v1. 0 technical note and reproducibility package for AKRM-RIR: Recursive Instability Regulation for Hallucination-Aware Language Model Inference. AKRM-RIR is a lightweight, model-agnostic inference-time control framework that treats hallucination risk as a graded instability phenomenon during language model generation. The framework estimates an epistemic reliability score μₜ, computes an instability functional K (μ) =4μ (1−μ), and applies refusal gating, recursive smoothing, and a Proper Exit mechanism under high-uncertainty conditions. This release includes the preprint PDF, LaTeX source, a minimal Python prototype, a 40-prompt benchmark sample, evaluation outputs, and trace logs. The reported results should be interpreted as preliminary findings on a small controlled benchmark, not as a fully validated large-scale evaluation. Version: v1. 0 Status: Preprint / Not peer reviewed Code status: Minimal reproducibility package included License: CC-BY 4. 0 for paper and text materials; MIT License for code

AKRM-RIR: Recursive Instability Regulation for Hallucination-Aware Language Model Inference

Key Points

Abstract

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