Belief Filtering for Epistemic Control in Linguistic State Space

TL;DR

This paper proposes belief filtering within a linguistic state space to control and regulate AI cognitive states, enhancing interpretability and safety through structured semantic interventions.

Contribution

It introduces a novel belief filtering mechanism embedded in a linguistic semantic framework for improved epistemic control of artificial agents.

Findings

Belief filtering enables content-aware regulation of internal cognitive states.

The approach enhances interpretability and modularity of agent control.

Potential applications in AI safety and alignment are demonstrated.

Abstract

We examine belief filtering as a mechanism for the epistemic control of artificial agents, focusing on the regulation of internal cognitive states represented as linguistic expressions. This mechanism is developed within the Semantic Manifold framework, where belief states are dynamic, structured ensembles of natural language fragments. Belief filters act as content-aware operations on these fragments across various cognitive transitions. This paper illustrates how the inherent interpretability and modularity of such a linguistically-grounded cognitive architecture directly enable belief filtering, offering a principled approach to agent regulation. The study highlights the potential for enhancing AI safety and alignment through structured interventions in an agent's internal semantic space and points to new directions for architecturally embedded cognitive governance.