Multi-Layered Memory Architectures for LLM Agents: An Experimental Evaluation of Long-Term Context Retention

1. Problem Statement

Large Language Models (LLMs) struggle with long-horizon dialogue systems due to two primary limitations:

• Semantic Drift: As dialogue sessions extend, earlier contextual signals are compressed or discarded, leading to loss of persona consistency, entity drift, and factual instability.
• Computational Inefficiency: Traditional approaches that concatenate full dialogue history result in quadratic context growth, increasing inference costs and causing memory interference.

Existing solutions (hierarchical working memory, retrieval-based consolidation, or context compression) often fail to balance cross-session persistence with computational efficiency. They either improve efficiency without stabilizing retention or improve recall without constraining memory drift. The core challenge is designing a structured memory mechanism that preserves semantic continuity across extended sessions without incurring quadratic context growth.

2. Methodology: Multi-Layer Memory Framework (MLMF)

The authors propose a Multi-Layer Memory Framework that decomposes dialogue history into three distinct layers, managed by adaptive retrieval gating and retention regularization.

A. Memory Architecture Decomposition

The memory state $M_t$ at session $t$ is defined as a tuple of three layers:

1. Working Memory ($M^{(w)}$): Preserves recent interaction tokens within a bounded window ($k$). It ensures temporal locality and active task-relevant signals.
2. Episodic Memory ($M^{(e)}$): Accumulates compact session summaries recursively. It uses a decay parameter $\alpha$ to blend previous summaries with new session data, preventing abrupt forgetting while balancing detail and abstraction.
3. Semantic Memory ($M^{(s)}$): Encodes stable, structured entity-event graphs derived from episodic summaries. This layer maintains persona consistency and long-term factual structures, reducing redundancy.

B. Adaptive Retrieval and Gating

Instead of retrieving from a flat history, the system employs an adaptive layer-weighting mechanism:

• Gating: A softmax-based function dynamically calculates weights ($\gamma_i$) for each memory layer based on the semantic relevance ($sim$) between the current input and the memory layers.
• Fusion: The retrieved information is fused with the current input via cross-attention to generate the response, ensuring the model does not over-rely on recent context while maintaining entity consistency.

C. Retention Stability Objective

To explicitly control semantic drift, a retention regularization term ($L_{ret}$) is introduced into the optimization objective:

• $L_{ret}$ penalizes abrupt structural shifts in the semantic memory embeddings between consecutive sessions.
• The total loss function combines generation quality ($L_{gen}$) and retention stability ($\lambda L_{ret}$), ensuring that fluency and long-term memory consistency are jointly optimized.

D. Algorithmic Procedure

The framework operates as a constrained state-transition process (Algorithm 1):

1. Update: Working memory is bounded; episodic memory is recursively accumulated; semantic memory is updated via graph stabilization and conflict resolution.
2. Retrieve: Adaptive weights are calculated to prioritize relevant layers.
3. Fuse & Generate: Information is fused under entropy constraints, and the response is maximized.
4. Regularize: Parameters are updated to minimize both generation loss and retention drift.

3. Key Contributions

• Structured Decomposition: Introduces a novel three-tier architecture (Working, Episodic, Semantic) that explicitly separates short-term interaction from long-term abstraction.
• Adaptive Gating: Develops a dynamic retrieval mechanism that weights memory layers based on semantic relevance, reducing irrelevant semantic drift.
• Retention Regularization: Proposes a mathematical objective to constrain semantic shifts across sessions, directly addressing the "catastrophic forgetting" and "drift" problems in long-horizon tasks.
• Efficiency: Achieves improved retention and reasoning stability without increasing computational overhead, effectively managing bounded context budgets.

4. Experimental Results

The framework was evaluated on three long-horizon benchmarks: LOCOMO, LOCCO, and LoCoMo.

Performance Metrics

• Success Rate (SR): Achieved 46.85% on LOCOMO (outperforming the baseline Hu et al. [10] at 42.00%).
• F1 Score: Achieved an overall F1 of 0.618 and a Multi-hop F1 of 0.594 on LoCoMo, surpassing the previous best of 0.583 (Shah et al. [18]).
• Retention: Achieved 56.90% retention after six temporal periods on LOCCO, a significant improvement over the 48.25% retention of parameter-based memory approaches (Jia et al. [14]).
• False Memory Rate (FMR): Reduced to 5.1% (compared to 6.8% in Phadke et al. [13]), indicating higher factual consistency.
• Context Efficiency: Reduced context usage to 58.40% (compared to 64.98% in hierarchical working memory systems) while achieving a 10.4× decoding speedup.

Ablation Study

Removing individual components confirmed the necessity of the full architecture:

• Removing the Semantic Layer caused the largest drop in retention.
• Removing Retention Regularization significantly increased the False Memory Rate.
• The full model consistently outperformed reduced variants across F1, retention, and stability metrics.

5. Significance and Conclusion

This paper presents a principled framework for sustaining semantic continuity in long-session conversational agents. By decoupling memory into hierarchical layers and enforcing stability through regularization, the Multi-Layer Memory Framework solves the trade-off between memory retention and computational cost.

The findings provide critical theoretical insights into hierarchical memory design and offer practical guidance for building memory-aware agent architectures. These systems can now maintain persona consistency, reasoning stability, and efficient context utilization in real-world applications requiring extended multi-session interactions, such as personalized assistants, long-term customer support, and complex task-oriented agents.