The Anti-Ouroboros Effect: Emergent Resilience in Large Language Models from Recursive Selective Feedback

B. Experiment 2: The Anti-Ouroboros Effect in an LLM In the LLM experiment, the results falsified the hypothesis. The Quality Filter arm demonstrated robust and statistically significant improvement across all three evaluation metrics, as shown in Table II. In contrast, both the unfiltered Control arm and the Random Filter arm exhibited performance degradation, proving that the improvement is due to intelligent selection, not merely training on less data. The performance trajectories for all three arms are visualized in Figure 1 C. Human Evaluation To provide an independent verification of our automated metrics, we conducted a small, blinded human study. Two evaluators rated 30 anonymized and shuffled summaries from the final generation of the Control and Quality Filter arms on a 1-5 scale. The Quality Filter arm significantly outperformed the Control arm on coherence (4.2 vs 3.5) and factuality (4.5 vs 3.8), confirming the quantitative results. D. Analysis of the Mechanism The emergence of the Anti-Ouroboros Effect in the LLM suggests a dynamic unique to high-dimensional systems. We propose two non-exclusive hypotheses: Error Propagation Shutdown, where the filter acts as a ratchet, preventing the reinforcement of errors, and Latent Space Guidance, where selection guides the fine-tuning process toward more robust regions of the model's parameter space.