Dimensions overall score 7.0

• Ouroboros reduces training loss by 43.4% over an unmodified 17-layer baseline when applied to Qwen2.5-3B split into a Prelude/Recurrent/Coda architecture.result 95%
• Ouroboros recovers 51.3% of the performance gap caused by layer removal in the Qwen2.5-3B architecture.result 95%
• The full Ouroboros system adds only 9.2M trainable parameters (Controller, gate, and per-step norms).technical 95%
• Ouroboros outperforms equivalently-sized static per-step LoRA by 1.44 loss points at depth 1 and remains ahead across all tested depths (1, 4, 8, 16) and ranks (8, 32, 64).result 90%
• Gated recurrence is essential for Ouroboros, as without it, recursive layer application makes the model strictly worse.technical 90%
• On held-out text, the Controller does not yet improve over the baseline, which is attributed to frozen downstream layers.limitation 85%
• Ouroboros uses a Controller hypernetwork that observes the current hidden state and produces a per-step diagonal modulation vector applied to frozen SVD-initialized LoRA bases.method 90%
• Ouroboros combines dynamic weight generation with gated recurrence (bias-initialized to 88% retention) and per-step LayerNorm for stable deep iteration.method 85%

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