Flow Intelligence: Knowledge Resilience Under Conditions of Information Degradation - Storage and Compression Series FI-SC-02 - FI-SC-09-1
Fuente:
Zenodo
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Recurso digital |
| Lingua: | inglese |
| Pubblicazione: |
Zenodo
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866901876965900288 |
|---|---|
| author | Smith, John Richard HATI |
| author_facet | Smith, John Richard HATI |
| contents | <p>Abstract<br>This paper examines semantic degradation as a systemic failure mode in knowledge<br>systems, with particular attention to recursive contamination in artificial intelligence.<br>The paper introduces the concept of knowledge prion risk to describe how meaning<br>can degrade even when syntax remains intact. Drawing carefully scoped analogies<br>from biological prion diseases, it describes how systems trained on their own<br>outputs—or on outputs from similar systems—propagate structural corruption over<br>time. The paper argues that downstream interventions such as filtering, alignment<br>training, or improved prompting cannot cure substrate contamination. The only<br>effective response is strict control of what enters the epistemic substrate—what the<br>paper terms epistemic hygiene. This analysis extends beyond AI systems to any<br>recursive knowledge environment where outputs become inputs. The paper does not<br>name specific vendors or propose specific architectures; it offers a diagnostic frame<br>applicable across contexts.<br>Keywords: semantic degradation; prion risk; recursive training; AI safety; meaning collapse;<br>contamination; epistemic hygiene; model collapse; recursive contamination; substrate<br>corruption</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18308554 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Flow Intelligence: Knowledge Resilience Under Conditions of Information Degradation - Storage and Compression Series FI-SC-02 - FI-SC-09-1 Smith, John Richard HATI semantic degradation; prion risk; recursive training; AI safety; meaning collapse; contamination; epistemic hygiene; model collapse; recursive contamination; substrate corruption <p>Abstract<br>This paper examines semantic degradation as a systemic failure mode in knowledge<br>systems, with particular attention to recursive contamination in artificial intelligence.<br>The paper introduces the concept of knowledge prion risk to describe how meaning<br>can degrade even when syntax remains intact. Drawing carefully scoped analogies<br>from biological prion diseases, it describes how systems trained on their own<br>outputs—or on outputs from similar systems—propagate structural corruption over<br>time. The paper argues that downstream interventions such as filtering, alignment<br>training, or improved prompting cannot cure substrate contamination. The only<br>effective response is strict control of what enters the epistemic substrate—what the<br>paper terms epistemic hygiene. This analysis extends beyond AI systems to any<br>recursive knowledge environment where outputs become inputs. The paper does not<br>name specific vendors or propose specific architectures; it offers a diagnostic frame<br>applicable across contexts.<br>Keywords: semantic degradation; prion risk; recursive training; AI safety; meaning collapse;<br>contamination; epistemic hygiene; model collapse; recursive contamination; substrate<br>corruption</p> |
| title | Flow Intelligence: Knowledge Resilience Under Conditions of Information Degradation - Storage and Compression Series FI-SC-02 - FI-SC-09-1 |
| topic | semantic degradation; prion risk; recursive training; AI safety; meaning collapse; contamination; epistemic hygiene; model collapse; recursive contamination; substrate corruption |
| url | https://doi.org/10.5281/zenodo.18308554 |