What does this research mean for the field?

The Topic-Aware Inference Boost architecture reduces hallucinations in large language models by providing topic-specific inference augmentation, achieving over 90% inference quality with low latency. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The research aims to minimize hallucinations in large language models by introducing a new architecture for topic-aware inference.

March 2, 2026Open Access

Topic-Aware Inference Boost: A Fast Microservice Architecture for Reducing Large Language Model Hallucinations

Key Points

The research aims to minimize hallucinations in large language models by introducing a new architecture for topic-aware inference.
Developed a modular microservice architecture called Topic-Aware Inference Boost.
Utilized a lightweight API for delivering expert responses from subject-matter-expert models.
Achieved rapid topic-specific inference without the need for retraining or extensive prompt engineering.
Demonstrated an end-to-end latency of 1 to 7 seconds on standard CPUs.
Achieved over 90% inference quality across various domain tasks.
Enabled models to potentially self-evaluate confidence and invoke specific solutions for low-confidence responses.

Abstract

Large language models (LLMs) often hallucinate—producing plausible but inaccurate responses—particularly when misjudging their own confidence arXiv:2401.01313. This paper introduces Topic-Aware Inference Boost, a modular microservice architecture designed to mitigate hallucinations through rapid, topic-specific inference augmentation. The system delivers just-in-time expert-level responses from curated subject-matter-expert (SME) models through a lightweight API, without requiring retraining or prompt engineering. The prototype demonstrates end-to-end latency of 1 to 7 seconds on standard CPUs with over 90 % inference quality for multiple domain tasks. By decoupling topic specialization from monolithic LLMs, this solution enables any client model to enhance its reliability through targeted grounding. Phase 2 will extend the framework to allow models to self-evaluate confidence and selectively invoke this solution for low-confidence inferences, maintaining real-time performance and high accuracy. Note To Readers This document, formerly titled "InferBoost," has been renamed to Topic-Aware Inference Boost to improve technical clarity and to disambiguate the research from external websites currently utilizing the "InferBoost" term. The underlying architecture, topic-identification methodology, and performance metrics remain unchanged.

Topic-Aware Inference Boost: A Fast Microservice Architecture for Reducing Large Language Model Hallucinations

Key Points

Abstract

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