Dynamic Memory Management Governance

Updated 17 December 2025

Dynamic Memory Management Governance is a unified, auditable, and policy-driven framework that coordinates memory operations across diverse substrates including LLMs and heterogeneous hardware.
It employs a structured governance loop—admission, progressive rollout, monitoring, rollback, and audit—to enforce reproducibility, safe forgetting, and traceability.
Standardized metrics and detailed audit trails enable continual evaluation and rapid updates across systems such as LLM infrastructures, operating systems, and agent-based tools.

Dynamic Memory Management Governance (DMM Gov) denotes a unified, auditable, and policy-driven framework for managing the composition, updating, auditing, and revocation of memory across all substrates in modern computational systems—including LLMs, heterogeneous hardware, and agent-based tool memory. DMM Gov coordinates multiple forms of memory (parametric, contextual, external, procedural) via a structured governance loop covering thresholded admission, progressive deployment, live monitoring, automatic rollback, and versioned auditing. Its goal is to provide reproducibility, consistent evaluation, rapid updating, safe forgetting, and traceability of memory modifications under explicit operational constraints (Zhang et al., 23 Sep 2025).

1. Formal Foundations and Definitions

DMM Gov is mathematically characterized as a tuple: $\text{DMM Gov} = (M, \Theta, \text{Loop}, \text{Metrics}, \text{Audit})$ where:

$M = \{m_1, ..., m_N\}$ is the set of managed memory items, each specified by a memory quadruple $m_i = (\text{location}, \text{persistence}, \text{write\_path}, \text{controllability})$ .
$\Theta$ is a collection of admission and monitoring thresholds (e.g., minimum edit success rate, freshness requirements).
Loop denotes the governance process: admission $\rightarrow$ rollout $\rightarrow$ monitoring $\rightarrow$ rollback $\rightarrow$ audit.
Metrics include freshness hits, conflict consistency, refusal slices, and uncertainty scores.
Audit is the subsystem that produces certificates for traceability (version, evidence, reversibility) (Zhang et al., 23 Sep 2025).

The memory quadruple concretely specifies substrate, durability, operational path, and degree of manual or automated controllability, enabling fine-grained governance across parametric weights, retrieval indices, cached context, and episodic traces.

2. Governance Loop: Procedures and Control Flow

DMM Gov employs a staged control flow to govern all memory update or forget operations. Each event (update, addition, forgetting) is processed as follows:

Admission: Proposed changes must meet quantifiable criteria (e.g., projected edit success rate $\geq \theta_{\mathrm{adm}}$ ). AdmissionCheck evaluates whether the change is eligible for deployment.
Progressive Rollout: Approved changes are staged in fractions (traffic splitting or batch allocation), with live metrics collected at each stage. Rollout continues only if monitored metrics remain within bounds.
Online Monitoring: Governance metrics (e.g., freshness hit rate, conflict consistency, refusal rate) are tracked continuously. Exceeding any threshold triggers mitigation.
Rollback: If any constraint is violated, the system reverts to the last known good memory configuration.
Change Audit: All deployments are logged as audit certificates, including memory item IDs, pre/post-metrics, timestamps, and reversibility instructions (Zhang et al., 23 Sep 2025). This ensures forward and backward traceability and compliance with operational or regulatory requirements.

Abstracted pseudocode:

def DMM_Governance(update U, forget F):
    if AdmissionCheck(U, Θ):
        version ← NewVersion()
        for t in rollout_schedule:
            DeployVersion(version, fraction=t)
            metrics = ComputeMetrics(version)
            if metrics.violate(Θ):
                RevertToLastGoodVersion()
                break
        else:
            Certificate = CreateAuditCertificate(version, U, metrics)
            ArchiveCertificate(Certificate)

3. Memory Substrates and Mechanisms

DMM Gov is substrate-agnostic, supporting orthogonal governance of:

Parametric Memory: Knowledge encoded in model weights; updated via fine-tuning, PEFT, or model editing (ROME, MEND, MEMIT, SERAC).
Contextual Memory: Input context for inference (prompts, session state).
External Memory: Retrieval-augmented indexes, key-value stores, plaintext or structured knowledge bases.
Procedural/Episodic Memory: Long-term behavioral traces (cross-session consistency, history replays) (Zhang et al., 23 Sep 2025).

Pathways for memory management include:

DAPT/TAPT: Broad updates for domain/task adaptation.
PEFT: Modular, versioned adaptations that are fully revocable.
Model Editing: Direct low-rank parameter interventions with locality and drawdown controls.
RAG Overlays: Dynamic external knowledge, with governed index updates and freshness/citation constraints.

All mechanisms are unified under the DMM Gov loop with standardized metrics and rollback functionality (Zhang et al., 23 Sep 2025).

4. Metrics, Timeliness, and Consistency Constraints

DMM Gov employs a comprehensive metric set:

Metric	Description	Formula/Constraint
Freshness Hit Rate	Proportion of timely (post-update) answers	$\text{FreshHit} = \frac{\#\text{fresh}}{\#\text{queries}}$
Outdated Rate	Frequency of outdated memory usage	$\text{Outdated} = 1 - \text{FreshHit}$
Conflict Consistency	Alignment with majority/ground-truth when faced with conflicting evidence	$\text{CCR} = \frac{\#\text{consistent}}{\#Q_\text{conflict}}$
Long-Horizon Consistency	Bounded decay of accuracy or consistency across sessions	$\frac{d\text{Acc}}{dt} \geq -\varepsilon$ or $\text{half-life} \left( h_{1/2} \right)$
Auditability	Quality/completeness of change certificates	Field-completeness in audit logs
Refusal Slices	Selective accuracy and correct refusal on unanswerable queries	$\text{SelAcc}$ , $\text{Refusal}$

Governance targets not just pointwise accuracy but also temporal requirements (timeliness), stability under update, and compliance across potentially adversarial or privacy-sensitive requests (Zhang et al., 23 Sep 2025).

5. Application Domains and Systems Integration

DMM Gov has direct instantiations in modern LLM and serving infrastructures, heterogeneous hardware, and agentic tool memory management:

LLM Memory Control: DMM Gov orchestrates DAPT/TAPT, PEFT, editing, and RAG within standardized rollout-audit loops, enabling up-to-date, reversible, and auditable knowledge operations (Zhang et al., 23 Sep 2025).
Operating Systems for LLMs: MemOS implements the MemCube abstraction, enacting allocation, migration, quota, expiry, and evolution policies under explicit governance metadata and transactional guarantees (Li et al., 28 May 2025).
Elastic Serving Frameworks: eLLM employs an elastic memory mechanism (ballooning), virtual tensor abstractions, and SLO-aware scheduling, regulating inflation/deflation, offloading, and unified utilization of GPU/CPU memory (Xu et al., 18 Jun 2025).
Short-Term Agentic Memory: MemTool realizes DMM Gov in tool context management for multi-turn LLM agents, supporting Autonomous, Workflow, and Hybrid governance modes with formal constraints on toolset size, accuracy, and resource usage (Lumer et al., 29 Jul 2025).
Heterogeneous Hardware: RIMMS demonstrates governance by exposing explicit handles, tracking last_owner, and automating placement/coherence under a low-friction API with per-call performance contracts (Gener et al., 28 Jul 2025).

A common principle is that all state changes—allocation, update, migration, and revocation—proceed through a governed pipeline with enforceable constraints, continuous audit, and controllable rollback.

6. Testable Propositions, Best Practices, and Open Questions

The DMM Gov framework yields several empirically testable propositions:

Minimum Identifiability: Existence of loci supporting targeted, localized edits with bounded off-target effects.
Minimal Evaluation Card: Sufficiency of a core metric set (correctness, faithfulness, positional robustness, timeliness, refusal behavior) for failure detection.
Causally Constrained Editing: Null-space constrained algorithms can Pareto-dominate unconstrained edits in accuracy, locality, and forgetting guarantees.
Retrieval vs. Ultra-Long Context: For constrained compute, combined retrieval and small-window replay can outperform ultra-long context injection for accuracy and efficiency (Zhang et al., 23 Sep 2025).

Best practices emerging from current systems include:

Explicit admission and monitoring thresholds for updates/forgets.
Progressive, reversible rollout and atomic rollback mechanisms.
Persistent, versioned change certificates for all memory modifications.
Unified audit and policy meta-data surfaced in interfaces and governance logs.

This suggests that future systems may further automate governor selection, adaptive trade-off tuning, and integrate reinforcement-based policy evolution (Li et al., 28 May 2025).

7. Significance, Implications, and Future Trajectories

DMM Gov standardizes the management and evaluation of memory across AI, operating systems, and agentic infrastructures. By establishing unified procedural and metric protocols, it supports reproducibility, up-to-date controllability, safety, and systematized auditing of knowledge changes. This foundationally advances the reliability, interpretability, and tractability of complex autonomous systems, providing necessary infrastructure for continual learning, safe forgetting, temporally-aware retrieval, and multi-agent coordination (Zhang et al., 23 Sep 2025, Li et al., 28 May 2025, Gener et al., 28 Jul 2025).

Adoption of DMM Gov principles may accelerate progress toward robust, accountable memory in large-scale AI, heterogeneous computing, and agent-based workflows, while opening new questions on the automation of governor strategies, learned governance thresholds, and compositional auditability across modular or federated systems.