The Anti-Ouroboros Effect: Emergent Resilience in Large Language Models from Recursive Selective Feedback
This addresses AI safety by showing that systemic resilience can emerge in LLMs, potentially enabling safer and more robust systems, though it appears incremental as it builds on existing theories of model collapse.
The paper tackled the problem of model collapse in recursively trained large language models by introducing a selective feedback mechanism, resulting in a statistically significant performance improvement of 6.6% in ROUGE-L F1 score over five generations, while unfiltered controls degraded.
The stability of recursively trained large language models (LLMs) is a foundational problem for AI safety. Prevailing theory predicts model collapse, a progressive degradation when models are trained on their own output. We challenge this narrative by introducing a selective feedback mechanism. Contrary to expectation, instead of merely slowing decay, our experiments provide strong evidence that this pressure reverses it, inducing a statistically significant performance improvement in a Gemma 2B model on a complex summarization task. We name this phenomenon the Anti-Ouroboros Effect. We contrast this with a foundational experiment using a simple classifier, where the theoretical degenerative loop was validated, highlighting the unique dynamics of high-dimensional models. Our findings establish that systemic resilience can be an emergent property of LLMs under simple selection pressure, suggesting a powerful and scalable principle for developing safer and more robust AI systems. Across five generations, a quality-filtered condition improved by 6.6% in ROUGE-L F1 score, whereas an unfiltered control degraded by 3.5% and a random-filter control degraded by 4.2%