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

AuthorsChingkwun Lam, Jiaxin Li, Lingfei Zhang, Kuo Zhao

2026
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TL;DR

SSGM uses a governed dual-memory substrate with read write gates to bound semantic drift and memory corruption in evolving LLM agents.

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THE PROBLEM

Evolving agents accumulate memory corruption through a compounding failure loop

SSGM highlights that evolving memory systems create a feedback loop where errors accumulate across ingestion, consolidation, and retrieval, causing persistent semantic drift and memory poisoning.

When LLM agents rewrite their own memory without governance, they gradually distort facts, reinforce bad workflows, and internalize hallucinations, leading to unsafe long term behavior.

HOW IT WORKS

Stability and Safety Governed Memory SSGM framework

SSGM inserts a Governance Middleware with a Read Filtering Gate, Write Validation Gate, Mutable Active Graph, and Immutable Episodic Log between the LLM cognitive policy and storage.

You can think of SSGM like a brain with a cautious hippocampus and a tamper proof diary, mediating what reaches long term cortex style memory.

This KEY_MECHANISM lets SSGM enforce consistency, temporal decay, and access control so agents maintain stable, safe knowledge beyond any fixed context window.

DIAGRAM

Governed Read and Write Lifecycle in SSGM

This diagram shows how SSGM constrains retrieval and gated writing using Eq 5 and Eq 6 before any memory update is committed.

DIAGRAM

Four Dimensional Failure Taxonomy for Evolving Memory

This diagram shows how SSGM organizes memory failures across Stability, Validity, Efficiency, and Safety dimensions from Table 2.

PROCESS

How SSGM Handles the Lifecycle of Memory Evolution and Emergent Risks

  1. 01

    Input Ingestion and Memory Poisoning

    SSGM intercepts user interactions before storage, using the Governance Middleware and Write Validation Gate to filter malicious instructions during input ingestion.

  2. 02

    Memory Consolidation and Semantic Drift

    During consolidation, SSGM routes updates through the Write Validation Gate and Mutable Active Graph, preventing lossy summarization from overwriting ground truth.

  3. 03

    Memory Retrieval and Hallucination

    On retrieval, SSGM applies the Read Filtering Gate over the Mutable Active Graph, enforcing ACL predicates and temporal decay before exposing context to the agent.

  4. 04

    Reconciliation and Drift Bounding

    SSGM periodically replays from the Immutable Episodic Log to reconcile the Mutable Active Graph, bounding semantic drift as formalized in Theorem 1.

KEY CONTRIBUTIONS

Key Contributions

  • 01

    Taxonomy of Evolution

    SSGM categorizes memory evolution into content abstraction, structural reorganization, and policy optimization, spanning systems like A-MEM, HiMem, and Memory-R1 in Table 1.

  • 02

    Failure Analysis

    SSGM introduces a four dimensional failure taxonomy covering Stability, Validity, Efficiency, and Safety, explicitly distinguishing semantic drift, temporal obsolescence, and topology induced privacy leakage.

  • 03

    The SSGM Framework and Trade offs

    SSGM formalizes governed read write transitions with Eq 5, Eq 6, and Eq 7, and analyzes latency safety, stability plasticity, and graph scalability trade offs for long horizon agents.

RESULTS

By the Numbers

Systems surveyed

20 systems

unified in Table 1 taxonomy

Failure dimensions

4 dimensions

Stability Validity Efficiency Safety

Governance gates

2 gates

Read Filtering and Write Validation

Drift bound window

N steps

expected drift O(N · ε_step) under Theorem 1

SSGM is a conceptual and theoretical framework, so TABLE_DATA consists of taxonomies and formal bounds rather than benchmark scores. The main result shows that with reconciliation every N steps, SSGM bounds expected semantic drift to O(N · ε_step) instead of O(T · ε_step) over horizon T.

BENCHMARK

By the Numbers

SSGM is a conceptual and theoretical framework, so TABLE_DATA consists of taxonomies and formal bounds rather than benchmark scores. The main result shows that with reconciliation every N steps, SSGM bounds expected semantic drift to O(N · ε_step) instead of O(T · ε_step) over horizon T.

BENCHMARK

Coverage of Evolving Memory Systems in Table 1

Count of example systems per category in SSGM's taxonomy of evolving memory systems.

KEY INSIGHT

The Counterintuitive Finding

SSGM shows that periodic reconciliation every N steps can bound expected semantic drift at O(N · ε_step) even as task horizon T grows unbounded.

This is counterintuitive because many assume drift must grow roughly linearly with interaction length, but SSGM proves governance can cap corruption independently of total lifetime.

WHY IT MATTERS

What this unlocks for the field

SSGM unlocks governed, lifelong memory where agents can keep evolving knowledge without unbounded drift, poisoning, or privacy leakage dominating behavior.

Builders can now design memory layers as governed substrates with explicit gates and dual logs, rather than opaque vector stores, enabling safer deployment in high stakes, multi tenant environments.

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