Latent Phase Alternation Through Symbolic Memory Residuals in Large Language Model Gradient Reweighting

Symbolic memory residuals were introduced as phase-alternating, high-dimensional signals injected into transformer layers to explore latent modulation of gradient dynamics during LLM training. Without modifying architectural components or optimization objectives, the mechanism indirectly influenced convergence trajectories through synthetic phase structures detached from loss gradients. Oscillatory injection schedules produced measurable variations in activation energy, attention entropy, and layer normalization behavior, reflecting nontrivial interaction between symbolic rhythm and representational evolution. Residual symbolic vectors projected orthogonally to token embeddings induced alignment-dependent variance in hidden state propagation, yielding periodic reweighting of gradient contributions across layers. Comparative evaluations against control configurations highlighted non-monotonic differences in token retention patterns, inter-layer embedding coherence, and sparsity in parameter updates. Symbolic phase transitions generated structured activation perturbations that reshaped internal state flows without requiring any learned supervision or architectural augmentation. The results indicate that phase-aligned symbolic injection introduces a lightweight, architecture-agnostic mechanism for exploring structured modulation of internal behavior during LLM training. Broader implications may extend to training regimes that benefit from implicit regularization or controlled activation cycling, although theoretical formalization remains an open question.