'Neural howlround' in large language models: a self-reinforcing bias phenomenon, and a dynamic attenuation solution
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915236766810112 |
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| author | Drake, Seth |
| author_facet | Drake, Seth |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_07992 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 'Neural howlround' in large language models: a self-reinforcing bias phenomenon, and a dynamic attenuation solution Drake, Seth Computation and Language Artificial Intelligence Neural and Evolutionary Computing 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. |
| title | 'Neural howlround' in large language models: a self-reinforcing bias phenomenon, and a dynamic attenuation solution |
| topic | Computation and Language Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2504.07992 |