'Neural howlround' in large language models: a self-reinforcing bias phenomenon, and a dynamic attenuation solution

'Neural howlround' in large language models: a self-reinforcing bias phenomenon, and a dynamic attenuation solution Seth Drake, PhD (Independent Researcher) April 14, 2025 Abstract Large language model (LLM)-driven AI systems may exhibit an inference failure mode we term'neural howlround,' a self-reinforcing cognitive loop where certain highly weighted inputs become dominant, leading to entrenched response patterns resistant to correction. This paper explores the mechanisms underlying this phenomenon, which is distinct from model collapse and biased salience weighting. We propose an attenuation-based correction mechanism that dynamically introduces counterbalancing adjustments and can restore adaptive reasoning, even in'locked-in' AI systems. Additionally, we discuss some other related effects arising from improperly managed reinforcement. Finally, we outline potential applications of this mitigation strategy for improving AI robustness in real-world decision-making tasks. 1 Introduction Many AI agents use large language models (LLMs) for input recognition and also output prediction: these models are trained on vast datasets and are based on probability weight assignments developed over the course of training. Research on the causes of AI bias and model reinforcement loops has identified numerous challenges, For example, model collapse, in which generative systems exhibit degradation in diversity and accuracy when outputs 1 arXiv:2504.07992v1 Furthermore, research into biased salience weighting suggests that excessive reinforcement of certain pathways leads to an'echo chamber' effect which induces a self-perpetuating self-reinforcement of certain outputs. 'Neural howlround,' the failure mode we describe here, is not merely a combination of multiple of these existing cases. While it may outwardly resemble existing AI bias phenomena, 'neural howlround' is a unique, emergent failure mode occurring during inference rather than during training. This runtime instability necessitates a dedicated intervention strategy distinct from traditional bias mitigation techniques. We feel additionally that this failure mode deserves particular attention as it is a runtime event. If left unchecked it could cause LLM-driven agents to become'locked-in,' unable to escape cognitive or ideological loops and thereby limited in their ability to respond with an appropriate level of critical thought, to adapt to novel or contradictive inputs or to maintain proper probabilistic output.