Research Article

Self-Reflective Memory Consolidation in Agentic Architectures

by  Vijay Putta, Krishna Teja Areti, Ajay Guyyala, Prudhvi Ratna Badri Satya
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Issue 73
Published: January 2026
Authors: Vijay Putta, Krishna Teja Areti, Ajay Guyyala, Prudhvi Ratna Badri Satya
10.5120/ijca2026926236
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Vijay Putta, Krishna Teja Areti, Ajay Guyyala, Prudhvi Ratna Badri Satya . Self-Reflective Memory Consolidation in Agentic Architectures. International Journal of Computer Applications. 187, 73 (January 2026), 1-14. DOI=10.5120/ijca2026926236

                        @article{ 10.5120/ijca2026926236,
                        author  = { Vijay Putta,Krishna Teja Areti,Ajay Guyyala,Prudhvi Ratna Badri Satya },
                        title   = { Self-Reflective Memory Consolidation in Agentic Architectures },
                        journal = { International Journal of Computer Applications },
                        year    = { 2026 },
                        volume  = { 187 },
                        number  = { 73 },
                        pages   = { 1-14 },
                        doi     = { 10.5120/ijca2026926236 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2026
                        %A Vijay Putta
                        %A Krishna Teja Areti
                        %A Ajay Guyyala
                        %A Prudhvi Ratna Badri Satya
                        %T Self-Reflective Memory Consolidation in Agentic Architectures%T 
                        %J International Journal of Computer Applications
                        %V 187
                        %N 73
                        %P 1-14
                        %R 10.5120/ijca2026926236
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

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 ρ = 0.91, reflective drift Ψ = 0.048, and reflective efficiency Ω = 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 Lrec = 0.017 and Lenergy = 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.

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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Automated AI agents Adversarial Context Injection Model Context Protocol Robustness Anomaly Detection Defense Mechanisms

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