Self-Reflective Memory Consolidation in Agentic Architectures

This work introduces a Self-Reflective Memory Architecture (SRMA) that maintains coherence and retention across long reasoning cycles by integrating episodic encoding, reflection scoring, adaptive retrieval, and energy-based correction into a unified consolidation process.SRMA preserved alignment between stored and retrieved representations, yielding a retention alignment of = 0.91, reflective drift = 0.048, and reflective efficiency = 0.89 across MemoryBank, LME, and DuLeMon.Standard evaluation metrics remained consistently high, with accuracy 0.91, precision 0.91, recall 0.89, and F1 0.90/0.87.Reconstruction and energy losses were reduced to L rec = 0.017 and L energy = 0.014, indicating stable consolidation over repeated updates.Ablation analysis showed measurable degradation when reflective modules were removed, and robustness tests confirmed stable retention under noise.These results demonstrate that structured reflection enables durable memory consolidation and controlled adaptability for long-context agentic reasoning.