Governing Evolving Memory in LLM Agents: Risks, Mechanisms, and the Stability and Safety Governed Memory (SSGM) Framework

TL;DR

Through formal analysis and architectural decomposition, it is shown how SSGM can mitigate topology-induced knowledge leakage where sensitive contexts are solidified into long-term storage, and help prevent semantic drift where knowledge degrades through iterative summarization.

Abstract

Long-term memory has emerged as a foundational component of autonomous Large Language Model (LLM) agents, enabling continuous adaptation, lifelong multimodal learning, and sophisticated reasoning. However, as memory systems transition from static retrieval databases to dynamic, agentic mechanisms, critical concerns regarding memory governance, semantic drift, and privacy vulnerabilities have surfaced. While recent surveys have focused extensively on memory retrieval efficiency, they largely overlook the emergent risks of memory corruption in highly dynamic environments. To address these emerging challenges, we propose the Stability and Safety-Governed Memory (SSGM) framework, a conceptual governance architecture. SSGM decouples memory evolution from execution by enforcing consistency verification, temporal decay modeling, and dynamic access control prior to any memory consolidation. Through formal analysis and architectural decomposition, we show how SSGM can mitigate topology-induced knowledge leakage where sensitive contexts are solidified into long-term storage, and help prevent semantic drift where knowledge degrades through iterative summarization. Ultimately, this work provides a comprehensive taxonomy of memory corruption risks and establishes a robust governance paradigm for deploying safe, persistent, and reliable agentic memory systems.

Figures (4)

• Figure 1: The Lifecycle of Memory Evolution and Emergent Risks. Unlike static RAG, evolving memory systems introduce a feedback loop where errors can accumulate. We identify three critical failure points: (1) Memory Poisoning during input ingestion, (2) Semantic Drift during consolidation updates, and (3) Conflict/Hallucination during retrieval. SSGM aims to govern these interfaces.
• Figure 2: An Illustrative Case of Semantic Drift. Iterative summarization gradually distorts a specific user preference through lossy compression and semantic intensification. For example, an originally mild preference (e.g., "I like mild spicy food") may be progressively rewritten as "likes spicy food" and later "loves very spicy food," ultimately causing a preference violation (e.g., suggesting ghost pepper wings).
• Figure 3: Logical Mechanism of Memory Drift vs. Anchored Stability. The left path illustrates how naive iterative summarization causes semantic drift (loss of nuance). The right path demonstrates the SSGM approach, where periodic reconciliation ($\mathcal{R}$) with an immutable anchor log ($\mathcal{K}_{\text{ledger}}$) bounds the distortion relative to the ground truth ($\mathcal{K}_{\text{true}}$).
• Figure 4: The conceptual architecture of the SSGM framework, featuring a decoupled left-to-right processing pipeline. The Governance Middleware intercepts memory interactions between the LLM Agent and the Memory Substrate. The upper pathway governs memory consolidation (Write Validation) to ensure logical consistency and mitigate drift, while the lower pathway manages memory retrieval (Read Filtering) by enforcing access scopes and temporal relevance. The right-side dual-memory substrate further supports reversible reconciliation by pairing a mutable active graph with an immutable episodic log.