External Reference
Topic: Model collapse / closed-loop training / external grounding
This article is relevant to ASI Redefined because it supports the concern that closed model-output loops may become unstable without grounding outside the model’s own generated material.
Primary relevance:
- Closed-loop training on model-generated data can lead to model collapse.
- External grounding, even minimal, may prevent collapse in the studied statistical setting.
- This supports caution around coherence claims that are not traceable across time, context, and pressure.
- This is relevant to memory substrate and identity binding because ASI-level coherence claims require grounding, comparison, and attribution beyond repeated output behavior.
AI Foundations alignment:
- Output is not provenance.
- The model is not Source.
- Self-reference is not sufficient grounding.
Status:
External support reference only.
Does not change ASI Redefined v2.0.
Potentially useful for v2.1 references, roadmap, or future discussion of grounding requirements.
Sources:
King’s College London article:
https://www.kcl.ac.uk/news/scientists-come-up-with-way-to-overcome-ai-data-cannibalism
Related paper / preprint:
https://arxiv.org/abs/2506.20623
External Reference
Topic: Model collapse / closed-loop training / external grounding
This article is relevant to ASI Redefined because it supports the concern that closed model-output loops may become unstable without grounding outside the model’s own generated material.
Primary relevance:
AI Foundations alignment:
Status:
External support reference only.
Does not change ASI Redefined v2.0.
Potentially useful for v2.1 references, roadmap, or future discussion of grounding requirements.
Sources:
King’s College London article:
https://www.kcl.ac.uk/news/scientists-come-up-with-way-to-overcome-ai-data-cannibalism
Related paper / preprint:
https://arxiv.org/abs/2506.20623