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

Chingkwun Lam; Jiaxin Li; Lingfei Zhang; Kuo Zhao

cs.AIMar 12, 2026

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

Authors: Chingkwun Lam, Jiaxin Li, Lingfei Zhang, Kuo Zhao

Organizations: College of Intelligent Science and Engineering, Jinan University

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 & tables

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.

System	Memory Structure	Evolution Policy	Refinement & Stability	Safety & Access	Target Domain
Adaptive & Learning-Based Systems
Memory-R1 ( Yan et al., 2026 )	Flat Vector DB	RL (PPO)	Feedback-driven Updates	–	Open-Domain QA
MemAgent ( Yu et al., 2025 )	Semantic Slots	RL (DAPO)	Selective Overwriting	–	Long-Context QA
AtomMem ( Huo et al., 2026 )	Vector + Buffer	Atomic Ops	Needle-in-Haystack Training	–	Multi-Turn Reasoning
LEGOMem ( Han et al., 2025 )	Procedural Graph	Failure-driven	Rule Verification	–	Workflow Automation
AgentSM ( Biswal et al., 2026 )	SQL-based Knowledge	Agentic Tuning	Schema Evolution	Strict Schema Constraints	Text-to-SQL (Enterprise)

Table 1: Taxonomy of Evolving Memory Systems. We categorize recent systems by their storage structure, the policy governing memory evolution, mechanisms for maintaining stability, and safety provisions. Newly integrated approaches like HippoRAG , DarwinMem , and VideoARM emphasize the shift towards neurobiology, adaptive policies, and multimodal environments.

Category	Failure Mode	Mechanism & Manifestation	Representative Contexts	SSGM Mitigation Strategy
Stability	Semantic Drift	Iterative summarization causes gradual nuance loss	Recursive Summarization ( Park et al., 2023 )	Ground Truth Anchoring ( $\mathcal{R}$ )
	Procedural Drift	Reinforcement of suboptimal/outdated workflows	Workflow Automation ( Han et al., 2025 ; Fang et al., 2025 )	Rule Verification
	Goal/Role Drift	Alignment shift due to accumulated interaction bias	Long-term Role-play ( Yuen et al., 2026 )	Role Partitioning
Validity	Memory Hallucination	Retrieval of non-existent or fabricated facts	Open-domain QA ( Du et al., 2025 )	Consistency Verifier (TMS)
Validity	Temporal Obsolescence	Conflict between stale memories and new states	User Personalization ( Rasmussen et al., 2025 )	Weibull Decay Function
Efficiency	Retrieval Latency	Search time scales linearly/quadratically with history	Real-time Interaction ( Chhikara et al., 2025 )	Hierarchical Indexing

Table 2: Comprehensive Taxonomy of Evolving Memory Failures. We expand the taxonomy to include Efficiency failures (Latency, Bloat), which are critical in long-horizon agents. Validity failures now explicitly distinguish between hallucination and temporal obsolescence (fact conflict over time), citing recent evidence from Rasmussen et al. (2025) .

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.

LLM Agent Memory

Large language model agent memory lets agents retain, organize, retrieve, and update information across interactions and long-running tasks.

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The emergence of writable, cross-session persistent memory in LLM agents introduces a qualitatively different threat landscape from conventional input-centric security concerns, characterized by three properties: persistence, statefulness, and propagation. To systematically characterize this landscape, we propose a Memory Lifecycle Framework that organizes attacks, defenses, and their cross-phase dependencies along two axes: six lifecycle phases (Write, Store, Retrieve, Execute, Share & Propagate, Forget & Rollback) and four security objectives (Integrity, Confidentiality, Availability, Governance). This analysis in turn exposes the need for explicit security requirements at the system level, motivating Verifiable Memory Governance (VMG), a framework of five architectural primitives for specifying and evaluating auditable, recoverable control over memory state within declared system and threat-model boundaries. Our analysis motivates integrating provenance, versioning, and policy-aware retention with retrieval and execution defenses to support auditable prevention, containment, and recovery across the lifecycle.

Abstract

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