Reducing AI Entropy: The Information Dynamics of Model Safety
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2025
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| _version_ | 1866901670515965952 |
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| author | Kugelmass, Joe |
| author_facet | Kugelmass, Joe |
| contents | <p>Large language models (LLMs) exhibit a fundamental tension: fluent outputs coexist with unpredictable instabilities—hallucinations, semantic drift, and oscillatory responses. Existing safety mechanisms evaluate outputs post-hoc but do not monitor the underlying information dynamics during generation. We introduce ΔK, an information-theoretic metric measuring divergence and boundary flux across multiple stochastic samples of a model's output. Built atop this metric, the Synthesized Oracle implements a feedback control system for LLMs that identifies unstable segments, removes oscillatory components, and reconstructs coherent responses through entropy-aware optimization.</p> <p>Across 188 evaluations on a 3-billion parameter model (cogito:3b), the system demonstrates: (1) reliable instability detection through flux dynamics, (2) consistent entropy reduction via J-score minimization (53.5% of cases requiring optimization), and (3) successful reconstruction of approximately half (53.1%) of chaotic responses through surgical oscillator removal. These results suggest that model safety can be reframed from behavioral policing to dynamical regulation—treating entropy not as a defect to eliminate but as a signal to guide.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17873096 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Reducing AI Entropy: The Information Dynamics of Model Safety Kugelmass, Joe instability detection LLM alignment generative AI control theory cybernetics flux measurement entropy model safety dynamical systems large language models artificial intelligence LLMs <p>Large language models (LLMs) exhibit a fundamental tension: fluent outputs coexist with unpredictable instabilities—hallucinations, semantic drift, and oscillatory responses. Existing safety mechanisms evaluate outputs post-hoc but do not monitor the underlying information dynamics during generation. We introduce ΔK, an information-theoretic metric measuring divergence and boundary flux across multiple stochastic samples of a model's output. Built atop this metric, the Synthesized Oracle implements a feedback control system for LLMs that identifies unstable segments, removes oscillatory components, and reconstructs coherent responses through entropy-aware optimization.</p> <p>Across 188 evaluations on a 3-billion parameter model (cogito:3b), the system demonstrates: (1) reliable instability detection through flux dynamics, (2) consistent entropy reduction via J-score minimization (53.5% of cases requiring optimization), and (3) successful reconstruction of approximately half (53.1%) of chaotic responses through surgical oscillator removal. These results suggest that model safety can be reframed from behavioral policing to dynamical regulation—treating entropy not as a defect to eliminate but as a signal to guide.</p> |
| title | Reducing AI Entropy: The Information Dynamics of Model Safety |
| topic | instability detection LLM alignment generative AI control theory cybernetics flux measurement entropy model safety dynamical systems large language models artificial intelligence LLMs |
| url | https://doi.org/10.5281/zenodo.17873096 |