Memory-Aware Retention Schema (MaRS)

• Memory-Aware Retention Schema (MaRS) is a unified framework that defines explicit retention policies to control, optimize, and audit memory in adaptive machine learning systems.
• It employs techniques like feature-aware density scoring, forgetting curves, and adaptive gating to balance computational efficiency with privacy and resource constraints.
• MaRS enables scalable and privacy-preserving memory management, enhancing transformer models, generative agents, and neuromorphic systems through systematic policy enforcement.

Memory-Aware Retention Schema (MaRS) is a unified conceptual and algorithmic framework for controlling, optimizing, and auditing memory retention in adaptive machine learning systems and generative agents operating under resource, privacy, or contextual constraints. MaRS approaches are defined by their explicit policies for deciding what information to keep, summarize, or evict, driven either by cognitive analogies, formal optimization, or implementation-specific architectural mechanisms. The schema underpins a broad class of methods, including privacy-aware memory management, selective layerwise pruning, hierarchical memory gating, and forgetting-aware evaluation for artificial agents and models (Alqithami, 14 Dec 2025, Rafiuddin et al., 9 Oct 2025, Liang et al., 25 Mar 2025, Chakraborty et al., 2 Apr 2025, Yaslioglu, 15 Jan 2025).

1. Conceptual Foundations and Motivations

At its core, MaRS is motivated by the challenge of balancing persistent memory for learning and reasoning with constraints on storage, privacy, computational tractability, and information overload. The paradigm first appears as a broad vision in "Remembrance: The Unbearable Sentience of Being Digital," where the authors advocate for "data remembrance"—the ability for digital objects to retain the contextualized history of their past states, analogizing to biological memory's selective recall and forgetting mechanisms (0909.1763).

Subsequent instantiations anchor MaRS in several domains:

• Cognitive and Agentic AI: Emulation of human-like memory consolidation and decay, e.g., using forgetting curves or episodic/semantic separation (Liang et al., 25 Mar 2025, Alqithami, 14 Dec 2025).
• Efficiency for Transformers/Sequence Models: Selective retention or pruning of latent representations for tractable long-context modeling (Rafiuddin et al., 9 Oct 2025, Yaslioglu, 15 Jan 2025, Chakraborty et al., 2 Apr 2025).
• Human-Centered Generative Agents: Enforcement of both computational budgets and privacy preservation through audit-friendly, policy-driven memory management (Alqithami, 14 Dec 2025).
• Neuromorphic/Event-based Processing: Adaptive, spike-driven retention schemas for scalable memory in state-space systems (Chakraborty et al., 2 Apr 2025).

All MaRS approaches share three primary design axes: explicit retention policy, formalized scoring/ranking of retained content, and algorithmic enforcement under budget or privacy constraints.

2. Retention Policy Mechanisms and Formalization

MaRS is defined by a retention policy—a mapping $$f: \mathcal{M} \times \mathbb{R}_{>0} \rightarrow \mathcal{M}$$, where $\mathcal{M}$ is the memory store and the second argument is a budget or constraint parameter (Alqithami, 14 Dec 2025). Formal instantiations span several mechanisms:

• Feature-aware density scoring: Each candidate memory node $n_i$ is evaluated using a feature vector $\psi_i$ (recency, frequency, centrality, goal relevance, privacy sensitivity), mapped to a utility proxy $\widehat U_i = \theta^{(t_i)} \cdot \psi_i$. The final retention score includes privacy penalty and is normalized by token weight:

$$\mathrm{score}(i) = \frac{\widehat U_i - \lambda_{\mathrm{priv}} s_i}{w_i}$$

where $s_i$ is sensitivity, $w_i$ is weight, and $\lambda_{\mathrm{priv}}$ is a privacy parameter (Alqithami, 14 Dec 2025).

• Forgetting curves and exponential decay: Retention of an item $I_t$ with strength $S$ decays exponentially:

$R(I_t,T) = \exp(-S \cdot T)$

Thresholds are used to promote, demote, or evict memory entries (Liang et al., 25 Mar 2025).

• Adaptive gating and stochastic selection: For latent token representations in a Transformer, retention is controlled via a Bernoulli gating variable $z_t \sim \mathrm{Bernoulli}(p_t)$, with $p_t = \sigma(s_t)$ derived from a learned network and relaxed for gradient-based training:

$$\tilde z_t = \mathrm{Clamp}_{[0,1]}\big(\sigma(\frac{\log \alpha_t + \log u - \log(1-u)}{\beta})(\zeta-\gamma)+\gamma\big)$$

(Rafiuddin et al., 9 Oct 2025).

• State-space decay and event-based adaptation: In spiking models, memory retention is regulated by exponential decay in the HiPPO matrix according to inter-spike intervals:

$F_{ij}(\Delta t) = e^{-\alpha_{ij}\Delta t}$

(Chakraborty et al., 2 Apr 2025).

Six policy templates—FIFO, LRU, Priority-Decay, Reflection-Summary, Random-Drop, and staged Hybrid—are systematically described in (Alqithami, 14 Dec 2025), each balancing cost, privacy, and cache coherence.

3. Memory Architecture and Data Structures

The underlying memory in MaRS implementations is structured, typed, and indexed for retrieval, update, and eviction:

Memory Store Graph

• Nodes ($n_i$): Encapsulate content, memory type (episodic, semantic, social, task), timestamp, privacy sensitivity, weight, and provenance (Alqithami, 14 Dec 2025).
• Edges: Encode temporal succession, semantic relationships, social links, derivation, and task dependencies.
• Indices: Temporal (age/popularity), frequency, importance/centrality, entity (posting lists), and ANN over content embeddings.

Episodic Buffer and Layerwise Memories

• Transformer MaRS variants maintain a persistent episodic buffer—a bank $M = \{m_j\}$ of memory slots—that is read, written, and updated across layers or sessions (Yaslioglu, 15 Jan 2025).

Retention Modules in Architectures

• Adaptive Retention inserts a lightweight retention module after each encoder block, enforcing token masking by the budget while maintaining differentiability (Rafiuddin et al., 9 Oct 2025).
• Neuromorphic MaRS (e.g., FLAMES) integrates state-space dynamics and NPLR matrix decompositions for efficient update and recall (Chakraborty et al., 2 Apr 2025).

4. Algorithms and Policy Enforcement

MaRS provides explicit algorithms for insertion, update, and eviction, always seeking to restore budget/feasibility and maximize utility under constraints. Representative algorithms include:

Policy	Mechanism	Time Complexity
FIFO	Age-based deque pruning	$O(1)$ per update
LRU	Hash-linked list	$O(1)$ amortized
Priority-Decay	Heap on density score	$O(\log n)$ per update
Reflection-Summary	Clustering + summarization	$O(n\log n + n\alpha(n))$
Random-Drop	Uniform sampling	$O(1)$
Hybrid	Staged multi-policy	Sum of above

• Differential Privacy: For near-tie cases, the exponential mechanism is used to enforce $(\epsilon,\delta)$-DP, with utility loss bounded by $O(\frac{\Delta q}{\varepsilon}\log|\mathcal S|)$ (Alqithami, 14 Dec 2025).
• Budget Enforcement: At each update, total token weight cannot exceed $B$, with antimatroid closure to preserve dependencies (e.g., provenance or task hierarchy).
• Transformer Adaptive Retention: Uses Lagrangian optimization to constrain expected number of retained tokens, alternating SGD and $\lambda$ updates (Rafiuddin et al., 9 Oct 2025).
• Episodic Buffer Update: Gated, leaky updating of memory slots with per-slot or global coefficients (Yaslioglu, 15 Jan 2025).
• Event-based Systems: Matrix exponentiation and operator approximations implement retention in spiking models (SA-HiPPO) (Chakraborty et al., 2 Apr 2025).

5. Empirical Results and Benchmarks

The impact of MaRS is evaluated across domains and architectures:

• Forgetful but Faithful Agent (FiFA) Benchmark (Alqithami, 14 Dec 2025): Composite score combining narrative coherence, goal completion, social recall accuracy, privacy preservation, and cost efficiency. Hybrid retention policy achieves $\sim0.911$ composite score over 300 runs, surpassing FIFO/LRU in coherence and goal fulfillment, while FIFO/Random maximize cost efficiency.
• Memory-Efficient LLMs (Rafiuddin et al., 9 Oct 2025): Retaining 30–50% of active tokens preserves $\geq95\%$ of full-model performance, reduces a peak GPU memory by ~35–45%, and increases throughput by up to $1.8\times$.
• Reflective Memory Agents (Liang et al., 25 Mar 2025): Memory-aware retention yields up to $2.26\times$ agent improvement on GPT-4, $57.7$–$100\%$ accuracy gains on open-source LLMs, and doubles answer F1 on long-span reasoning tasks.
• Event-Based Spiking Models (Chakraborty et al., 2 Apr 2025): FLAMES, using MaRS (SA-HiPPO + NPLR), is state-of-the-art on Long Range Arena and event-based vision (DVS Gesture, HAR-DVS, Celex-HAR).
• Transformer-based Adaptive Retention (Yaslioglu, 15 Jan 2025, Rafiuddin et al., 9 Oct 2025): Memory-augmented architectures achieve $30$–$50\%$ perplexity reduction on long-range modeling and $5$–$10\%$ absolute gains on continual learning.

6. Privacy and Auditability

A distinctive feature of MaRS as formalized in (Alqithami, 14 Dec 2025) is its integration of privacy-awareness and audit trails:

• Sensitivity-Aware Scoring: High-sensitivity nodes are less likely to be evicted, and privacy penalties are tunable.
• Differential Privacy Guarantee: Near-ties in density score trigger the exponential mechanism, enforcing DP at the retention decision boundary.
• Audit Logs: Every memory update, eviction, or summarization operation is logged with operation type, policy, feature scores, and rationale, enabling both retrospective audit and compliance with privacy regulations.
• Provenance and Closure: Provenance-chaining preserves dependencies and ensures that no summary or goal-critical memory is evicted prematurely.

7. Limitations, Extensions, and Open Problems

• Limitations: MaRS efficiency and effectiveness depend on precise tuning of policy parameters (e.g., privacy weights, score weighting, thresholds). Computational overhead can increase, especially with reflection/summary mechanisms or frequent Lagrangian optimization. Policy-induced errors may reinforce incorrect memories if checker/feedback is flawed (Liang et al., 25 Mar 2025).
• Potential Extensions: Meta-learning for threshold/budget adaptation, joint learning of per-layer or per-type budgets, mixture-of-experts routing using retention, cross-agent memory sharing, and finer-grained decay (e.g., power-law models) are acknowledged as directions of active research (Alqithami, 14 Dec 2025, Rafiuddin et al., 9 Oct 2025, Liang et al., 25 Mar 2025).
• Open Problems: Scalable retrieval and summarization over billion-node memories, privacy guarantee composition across distributed agents, long-term utility estimation under nonstationary goals, and integration with dynamic attention/routing architectures remain as major open challenges.

• (Alqithami, 14 Dec 2025) Forgetful but Faithful: A Cognitive Memory Architecture and Benchmark for Privacy-Aware Generative Agents
• (Rafiuddin et al., 9 Oct 2025) Learning What to Remember: Adaptive Probabilistic Memory Retention for Memory-Efficient LLMs
• (Liang et al., 25 Mar 2025) MARS: Memory-Enhanced Agents with Reflective Self-improvement
• (Chakraborty et al., 2 Apr 2025) FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning
• (Yaslioglu, 15 Jan 2025) Attention is All You Need Until You Need Retention
• (0909.1763) Remembrance: The Unbearable Sentience of Being Digital