Architecting Multi-Agent Transactive Memory: Non-Temporal Mechanisms for Consolidation, Revision, and Decay

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The Paradigm of Statelessness and the Architectural Limits of Chronometry

The fundamental architecture of Large Language Models (LLMs) dictates an inherent statelessness; any semantic or episodic information residing outside the active context window effectively ceases to exist from the perspective of the inference engine during a given computational cycle1. This deliberate design choice prioritizes horizontal scalability, concurrent hosting, and deterministic reasoning boundaries, but it imposes severe, often intractable limitations on the deployment of autonomous agents requiring continuous, long-term operational capabilities1. To circumvent this cognitive amnesia, contemporary AI architectures externalize state management through memory systems, conventionally categorizing knowledge retention into working, episodic, semantic, and procedural layers1. Historically, the governance of these externalized memory systems has relied heavily on chronological metadata, utilizing absolute wall-clock timestamps to dictate relevance, resolve contradictory data, and schedule the eviction of stale context4. The standard engineering approach assumes a linear relationship between recency and truth, enforcing a paradigm where temporal validity windows dictate the accuracy of a stored fact5. However, strict reliance on temporal metadata introduces severe architectural fragility, particularly in distributed, multi-agent environments. Arbitrary chronological decay ignores the structural utility of foundational knowledge, leading to the premature eviction of critical facts simply because they were encoded in the distant past6. Conversely, chronometry permits the retention of recent but highly anomalous or hallucinated data, a phenomenon categorized as "hallucination laundering," where fabricated content is retrieved and treated as ground truth merely because of its recent timestamp1. Furthermore, reliance on basic vector databases for retrieval introduces a static memory structure that lacks the capacity for dynamic adaptation, multi-hop temporal reasoning, or contradiction resolution3. To construct a robust, continuously evolving ecosystem of autonomous agents, the architecture must transition away from absolute temporal markers. Long-term memory must be maintained, cleaned, and updated through non-temporal mechanisms that rely on structural causality, logical dependencies, topological shifts, and cognitive access patterns. By synthesizing multi-agent transactive memory theories with distributed truth maintenance protocols, neurobiologically inspired synaptic plasticity, and utility-based governance, it is possible to engineer an agentic memory system that self-curates based on functional utility, frequency, and semantic coherence rather than the arbitrary passage of time.

Multi-Agent Transactive Memory (MATM) Foundations

The theoretical foundation for non-temporal memory management in decentralized agent systems is derived from Transactive Memory Systems (TMS), a sociological theory originally formulated by Daniel Wegner in the 1980s8. Wegner developed TMS in direct response to outdated "group mind" theories, proposing instead that groups develop a shared cognitive system exceeding the capacity of any single individual by distributing knowledge across participants8. This distribution relies on a collective meta-knowledge—a shared understanding of "who knows what"8. A transactive memory ecosystem consists of encoding, storing, and retrieving processes, differing fundamentally from standard institutional memory, which focuses on generational transfer rather than current, specialized distribution8. When transposed into the domain of artificial intelligence, Multi-Agent Transactive Memory (MATM) manifests as a population-level storage and retrieval infrastructure where heterogeneous agents dynamically share procedural knowledge, interaction trajectories, and semantic facts11. Within a MATM framework, agents operate in a dual-sided marketplace composed of producers and consumers11. Producer agents generate valuable artifacts—such as successful action-observation trajectories, logical deductions, or complex procedural workflows—which are ingested into a collective repository11. Consumer agents subsequently query this repository to bypass redundant problem-solving efforts, utilizing the transactive directory to locate the highly specialized expertise of other agents in the network12. Crucially, MATM bypasses the need for absolute chronological timestamps by utilizing step-based sequential indexing. Instead of marking an event with a wall-clock timestamp, trajectories are stored as ordered sequences of discrete interaction steps, [Figure omitted from source export], where time is represented purely as a relative sequence of states rather than a measurable chronological metric12. This sequential progression ensures that the causal chain of an agent's actions is preserved structurally, allowing agents to retrieve agent-generated artifacts that differ fundamentally from human-written text11.

Mitigating Cognitive Overload via Transactive Networks

The integration of MATM directly addresses Cognitive Load Theory (CLT) within LLM operations. Individual LLMs possess a bounded effective working memory, analogous to human cognitive limits, heavily restricted by fixed-size context windows2. When a task exhibits high element interactivity, it induces severe intrinsic cognitive load, leading to a state of cognitive overload characterized by hallucination, reasoning degradation, and a failure to follow multiple constraints10. Architectures such as CoThinker operationalize CLT principles to mitigate this overload by distributing intrinsic cognitive load across specialized agents and managing transactional load via a collective working memory10. Instead of relying on rigid, pre-defined roles, the system employs dynamic thinking style assignments based on a general base thinking style inventory, promoting a parallel division of cognitive labor10. The inter-agent information flow is governed by a communication moderator that establishes a cognitive small-world network, balancing local cohesion via strong cognitive ties with global integration via weak ties14. By pulling specific, pre-computed trajectories from the shared MATM repository based on the structural similarity of their current state prefix rather than temporal proximity, consumer agents successfully offload cognitive demands, preventing the context fragmentation and staleness that typically paralyze production systems2.

Distributed Truth Maintenance and Belief Revision

A core challenge in operating a memory system without timestamps is the resolution of contradictory facts. In a standard database, if an agent records a specific state and later records a contradictory state, the system resolves the conflict by favoring the record with the latest timestamp. In a non-temporal architecture, belief revision must rely on logical dependencies and causal justifications, a paradigm formalized by Truth Maintenance Systems (TMS)15. A TMS functions as a specialized cognitive layer that records the justifications for every derived fact in a knowledge base, enabling reasoning with incomplete or uncertain information16. Rather than permanently deleting obsolete facts, the TMS maintains a dependency network, marking nodes as actively believed (IN) or disbelieved (OUT) based on the validity of their foundational premises18. When a contradiction is detected, the system utilizes dependency-directed backtracking to identify the foundational assumption responsible for the conflict, automatically invalidating all downstream deductions without requiring temporal decay17.

Justification-Based versus Assumption-Based Modalities

The architecture of a TMS typically follows one of two primary modalities: Justification-based (JTMS) or Assumption-based (ATMS). A JTMS maintains a single, globally consistent context by ensuring that no conflicting beliefs coexist16. Every node is supported by a justification consisting of a believed list and a disbelieved list; if the premises in the believed list are satisfied, the derived conclusion is valid18. Conversely, an ATMS is designed for multi-contextual operations, making it highly optimal for decentralized, multi-agent ecosystems where multiple explanations or hypotheses must be explored in parallel16. Instead of associating a belief directly with its immediate logical parent, an ATMS traces every deduction back to its minimal set of foundational assumptions18. This allows the system to hold multiple, potentially contradictory contexts simultaneously without stopping on unresolved circularities or triggering catastrophic backtracking failures20.

Enforcing Local-and-Shared Consistency

When scaled to a population of interacting LLMs, the architecture evolves into a Distributed Assumption-based Truth Maintenance System (DATMS)20. In a DATMS, agents generate both private data and shared external data. The system enforces "Local-and-Shared Consistency," which dictates that an individual agent must remain locally logically consistent, while any data shared across the network must be mutually consistent among the specific agents utilizing it19. If an agent encounters a new piece of evidence that contradicts a shared semantic fact, the DATMS does not rely on a timestamp to determine priority. Instead, it substitutes the traditional "Priority of the Incoming Information" principle with the "Recoverability Principle," evaluating the logical depth and the justification network of the conflicting nodes15. The system utilizes blame assignment mechanisms to designate reliability values to the agents communicating data, tracking the dependency trace of a received message22. If a foundational assumption is retracted by one agent, the DATMS propagates a specialized label update through the distributed network. Any downstream memories derived from that assumption transition instantly from an IN state to an OUT state, ensuring the long-term memory remains clean, accurate, and perfectly synchronized across the multi-agent population without a single temporal calculation17.

Hebbian Plasticity and Validation-Gated Graph Topologies

To govern the gradual consolidation and structural decay of semantic relationships within a knowledge graph without temporal metrics, the architecture can emulate biological synaptic plasticity. In cognitive neuroscience, Hebbian learning dictates that the connection between two neurons is strengthened when they are co-activated—frequently summarized as "cells that fire together, wire together"23. By mapping this principle onto an agentic memory graph, the system continuously adapts the topology of its knowledge base based entirely on computational access patterns and operational utility rather than chronological age26.

Implementing Long-Term Potentiation (LTP)

In bio-inspired memory systems like Kairos and Shodh-Memory, the connections between entities in the semantic memory tier carry dynamic weights that are updated via Hebbian mechanisms26. When an agent successfully utilizes a specific multi-hop reasoning path or associates two distinct concepts to solve a task, the edges connecting those memory nodes undergo Long-Term Potentiation (LTP)25. The mathematical strengthening of these edges bypasses wall-clock time by utilizing discrete reasoning cycles. The synaptic weight update follows a bounded asymptotic formula to prevent single-trial over-consolidation. For instance, the new weight may be calculated by adding a learning rate modifier to the current weight, scaling inversely with the current strength to ensure diminishing returns as the connection approaches maximum fidelity26. Advanced implementations utilize multi-scale LTP to protect frequently used associations, granting tiered decay protection (e.g., Burst LTP, Weekly LTP, Full LTP) based entirely on sustained co-activation patterns measured in system cycles rather than absolute time28. By tracking the cyclic activation count of specific topological pathways, the system organically highlights highly reliable knowledge, ensuring that the most useful semantic facts become structurally permanent within the memory graph26.

Validation-Gated Consolidation and Logical Anchoring

A significant operational risk in autonomous memory systems is the continuous, automated reinforcement of hallucinations. If an agent repeatedly traverses a flawed logical path due to confirmation bias or systemic noise, a purely unsupervised Hebbian network would strengthen that erroneous connection, eventually locking the hallucination into long-term semantic memory26. To prevent this catastrophic feedback loop, advanced architectures deploy Validation-Gated Learning26. Before any Hebbian strengthening is applied to the knowledge graph, the proposed memory update must pass through a strict, multi-dimensional assessment conducted by specialized validator agents. In the Kairos architecture, these validators independently score the proposed reasoning path based on four specific dimensions: logical consistency, factual grounding, novelty, and alignment26. The Hebbian consolidation is strictly conditional; edge strengthening only triggers if the validation matrix returns an absolute consensus of validity26. Erroneous reasoning paths are denied consolidation, causing them to remain structurally weak and highly susceptible to rapid, cycle-based decay26.

Cycle-Based Long-Term Depression (LTD)

Memory cleaning—the process of forgetting obsolete, deprecated, or irrelevant information—is equally critical to maintaining a highly performant, low-latency agent ecosystem. Without timestamps to trigger scheduled garbage collection, the system implements Long-Term Depression (LTD) through activity-based decay26. Rather than measuring the chronological age of a memory, the architecture tracks the number of system-wide computational cycles that have elapsed since the memory edge was last traversed. Each relationship triple in the graph contains metadata representing inactive cycles26. As the multi-agent system processes new tasks, edges that are bypassed experience a calculated reduction in strength. The decay function operates exponentially relative to the inactive cycle count: [Figure omitted from source export] where [Figure omitted from source export] represents the decay rate and [Figure omitted from source export] represents the half-life measured in cycles26. The system steadily lowers the weight of unused nodes until they fall below a critical minimum threshold, at which point they are pruned26. This mechanism ensures that the agent's memory naturally forgets deprecated coding practices, obsolete user preferences, and irrelevant facts directly in proportion to the system's evolving workload, maintaining optimal sparsity without chronometry.

Spreading Activation and Cognitive Retrieval Mechanics

Beyond the structural adjustments of Hebbian plasticity, the retrieval mechanisms governing how agents recall information must also be untethered from chronological constraints. Traditional Retrieval-Augmented Generation (RAG) models rely on nearest-neighbor similarity matching, fundamentally lacking the capacity to recognize staleness, contradiction, or multi-hop conceptual relationships3. The ACT-R (Adaptive Control of Thought-Rational) cognitive architecture provides a robust mathematical foundation for modeling human-like working memory and associative retrieval using discrete event steps29.

The Base-Level Activation Equation

In a non-temporal ACT-R model, the availability of a memory chunk is determined by its Base-Level Activation, which is computed dynamically based on the frequency of its historical access and the competitive interference of other nodes within the working memory buffer32. Activation represents the inherent availability of an item; a higher activation ensures faster, more precise retrieval by the LLM agent, forming dependencies with minimal latency29. The activation equation models functional decay not as a product of passing seconds, but as a product of successive operational trials. The decay parameter is calculated using the total number of discrete retrievals and the lifespan of the item measured exclusively in event cycles33. Furthermore, the model incorporates an adaptive interference penalty. The decay rate of a memory chunk increases proportionally to the total number of elements currently held in the agent's active context34. This provides a soft-cap on working memory, mimicking the bounded capacity of human attention and forcing older, less relevant data to naturally fade as the agent focuses on complex, high-density tasks34.

Hop-Decayed Spreading Activation

To facilitate complex reasoning without relying on static vector embeddings, the memory graph employs Spreading Activation35. When a consumer agent initiates a query, the concepts within the query act as source nodes in the semantic network, receiving an initial injection of activation energy35. This energy propagates outward, simulating inferential reasoning by traversing the Hebbian-weighted edges to connected nodes35. The extent of this propagation is controlled by a hop-decay function, heavily attenuating the activation based on the structural distance (number of hops) from the source28. To prevent highly connected, generic "hub" nodes from absorbing all activation and drowning out nuanced context, the outgoing energy is mathematically normalized by the topological degree of the node28. This dynamic allows the agent to execute highly contextual, associative memory retrieval. A faded, deeply buried episodic memory can be temporarily revived and brought back into the active context if sufficient activation converges on it from multiple structurally related source nodes, producing a human-like, well-contrasted mechanism for forgetting and remembering37.

Semantic-Event-Triggered Consolidation and Curation

The pipeline responsible for moving data from ephemeral working memory into permanent long-term storage is known as consolidation1. If consolidation is executed eagerly—running an LLM-driven fact extraction loop on every conversational turn—the agent incurs massive computational overhead, rapidly bloats the episodic store, and inadvertently records transient, incomplete thoughts as permanent semantic facts7. Traditional architectures solve this by scheduling batch consolidations based on chronometry (e.g., executing a job every 24 hours). A non-temporal system must rely on topological and semantic phase shifts to govern this lifecycle.

Decoupling Encoding from Consolidation

The Hierarchical Graph-based Agentic Memory (GAM) framework explicitly addresses this by employing a Semantic-Event-Triggered mechanism39. The architecture strictly decouples the rapid encoding of immediate experiences from the slow, deliberate process of long-term semantic integration39. During active execution, the agent's interactions are recorded in an isolated Episodic Buffering Phase, constructing a localized Event Progression Graph that captures immediate dependencies and transient context while maintaining strict isolation39. The system continuously monitors the semantic trajectory of the agent's behavior. Instead of utilizing arbitrary temporal triggers, the architecture employs semantic divergence detection. When the agent shifts focus to a completely new task or conversation topic, the semantic divergence crosses a predefined threshold, signaling a structurally complete boundary39. Only upon detecting this semantic shift does the system transition into the Semantic Consolidation State. The localized Event Progression Graph is subjected to Hebbian Distillation27. A specialized reflective agent analyzes the densely connected hubs within the episodic buffer, extracting reusable procedural knowledge and generalized facts, which are then integrated into the global Topic Associative Network27. This strategy replaces arbitrary chronometric triggers with structural boundaries, minimizing contamination and ensuring the global memory is updated solely with semantically complete units40.

The Memory Curator Agent and Admission Control

To oversee this semantic distillation in a multi-agent environment, the architecture utilizes a dedicated Memory Curator Agent13. This specialist entity acts as the absolute gatekeeper for durable memory writes, running rigorous preflight checks on all candidate memories generated by the worker agents13. The Curator Agent operates strictly on algorithmic predicates rather than timestamps. It evaluates candidate entries across multiple constraints: ensuring safety predicates are met, verifying scope correctness to determine if a procedure is agent-specific or universally applicable, demanding evidence sufficiency for high-stakes decisions, and enforcing non-redundancy by deduplicating overlapping memories via structural comparison13. Complementing the Curator Agent, architectures may employ Adaptive Memory Admission Control (A-MAC), which decomposes the value of a prospective memory into complementary, interpretable factors41. Instead of relying on a black-box LLM decision, A-MAC evaluates future utility, factual confidence, semantic novelty, and content type prior to govern admission41. By treating memory admission as a structured decision problem optimized through cross-validation, the system achieves a superior precision-recall tradeoff, ensuring that only verified, highly useful facts populate the long-term semantic graph41.

Utility-Based Optimization and Piecewise Forgetting

To ensure the long-term memory system does not expand infinitely and degrade retrieval latency, the ecosystem must implement aggressive eviction rules7. Moving beyond pure structural decay, the architecture incorporates Utility-Based evaluation paradigms. Unlike simple reflex agents that act on fixed rules, or goal-based agents that seek binary success conditions, utility-based agents evaluate the state of the world by assigning real-valued scores to the anticipated outcomes of their actions, prioritizing efficiency, accuracy, and cost-effectiveness42.

Thompson Sampling for Memory Exploration

A significant flaw in uncurated memory systems is "Cold-Start Inequity" or exploration bias. When a new procedural memory is generated and written to the graph without a timestamp, it lacks the historical activation counts of older, established memories. Under deterministic greedy retrieval algorithms, the system will continuously exploit the older, highly-weighted memories, leaving the newly generated knowledge starved of activation, unretrieved, and vulnerable to premature decay45. To counter this, advanced non-temporal architectures like U-Mem apply semantic-aware Thompson sampling45. The retrieval mechanism balances exploitation (using proven, highly-weighted knowledge) with exploration (testing newly consolidated, unproven memories). By treating memory retrieval as a multi-armed bandit problem, the system probabilistically selects newer episodic records based on their semantic relevance and posterior utility variance45. As the agent utilizes these new memories, their utility scores are updated based on the success or failure of the downstream task. If the new memory proves highly effective, its utility weight spikes, rapidly securing its position in the long-term graph. If it fails, the utility score plummets, accelerating its algorithmic eviction and preventing the contamination of the shared transactive memory45.

Piecewise Hybrid Decay Mechanics

To model the retention of this utility-scored data, the system utilizes advanced mathematical forgetting curves, such as the piecewise exponential-to-power-law decay model implemented in Shodh-Memory28. Pure exponential decay predicts faster long-term forgetting than observed in biological systems, while power-law decay underestimates short-term forgetting28. The piecewise model captures both regimes perfectly. During the initial phase following consolidation, the memory is subjected to an aggressive exponential decay function, acting as a severe structural filter that rapidly clears out transient noise28. However, if a memory survives this initial exponential pruning through repeated cyclic access and positive utility reinforcement, it crosses a predefined consolidation threshold. [Figure omitted from source export] The decay function dynamically shifts to a heavy-tailed power-law curve, where [Figure omitted from source export] represents the power-law exponent28. In this secondary phase, the degradation of the memory slows exponentially, preserving high-utility semantic facts permanently without ever requiring a timestamp to lock them in place28.

Epistemic Agency and Trust Dynamics in Multi-Agent Networks

The deployment of a Multi-Agent Transactive Memory system devoid of chronological markers necessitates a rigorous approach to epistemology—the philosophical study of belief, truth, and justification47. Epistemologists utilize deontic norms (prescribing what should be believed) and axiological norms (identifying the goals of beliefs) to assess cognitive quality47. When consumer agents retrieve facts and procedural instructions from a global graph, they cannot rely on the recency of the data to gauge its accuracy. Consequently, the architecture must formalize the assessment of epistemic legitimacy—evaluating whether the producer agent that generated the memory is a credible source of truth48. Epistemic agency refers to the capacity to exercise meaningful, autonomous control over the formation, questioning, and revision of beliefs50. In a decentralized AI ecosystem, agents must possess the capacity to critically evaluate the data they consume to prevent the propagation of systemic errors, epistemic bubbles, and algorithmic manipulation50. The risk of "epistemic disengagement," where systems uncritically accept fabricated content or model-embedded biases due to a lack of source monitoring, directly threatens the validity of the collective memory48.

Attribution of Epistemic Authority and LTRT Reranking

To maintain epistemological hygiene, the MATM retrieval pipeline incorporates sophisticated trust modeling via a Learning to Rank Trajectories (LTRT) reranking protocol11. During the secondary phase of retrieval, the system evaluates the query-document pairs against a multi-dimensional feature map designed to mathematically quantify trust12.

The system continuously tracks the historical performance, accuracy rates, and logical consistency of every agent in the network. If an agent consistently produces high-utility trajectories that pass validation and survive piecewise exponential decay, its epistemic authority rating increases12. When a consumer agent queries the transactive memory, the LTRT reranker actively prioritizes artifacts generated by high-authority producers, tailoring the retrieval to the consumer's specific operational parameters11. This effectively substitutes the traditional reliance on temporal validity with a dynamic, peer-reviewed web of source credibility. The truth value of a stored memory is no longer a function of when it was written, but rather a mathematically verifiable reflection of the originating agent's historical reliability, the structural integrity of the logical dependency trace, and the memory's ongoing functional utility within the broader ecosystem12.

Conclusions

The transition from stateless language models to fully autonomous, persistently active agents requires a fundamental redesign of external memory infrastructures. The traditional reliance on absolute chronological timestamps and wall-clock temporal decay mechanisms is inherently brittle. It leads to the erratic retention of novel hallucinations, the premature eviction of foundational logic, and the inability to resolve semantic contradictions systematically. By synthesizing Multi-Agent Transactive Memory (MATM) with non-temporal cognitive architectures, it is entirely feasible to maintain a clean, highly curated, and perpetually updated knowledge base. This is achieved by substituting wall-clock time with discrete computational cycles, semantic event triggers, and topological metadata. Distributed Assumption-based Truth Maintenance Systems (DATMS) provide the logical scaffolding required to resolve contradictions through dependency tracing and assumption retraction, guaranteeing local and shared coherence without chronological precedence. Concurrently, the application of Hebbian plasticity, validation-gated strengthening, and hop-decayed spreading activation ensures that the memory graph organically adapts to the operational realities of the agent ecosystem. Frequent, high-utility pathways are permanently potentiated, while irrelevant noise is subjected to piecewise exponential decay driven by computational inactivity and adaptive interference. Overseen by rigorous Memory Curator Agents and regulated through utility-based Thompson sampling to overcome exploration bias, this architecture transforms static databases into dynamic, living memory graphs. Ultimately, by grounding the retention of knowledge in structural causality, semantic coherence, and verifiable epistemic authority, AI systems can achieve human-like transactive memory capabilities, fostering collaborative ecosystems that learn, adapt, and evolve with unprecedented stability and autonomy.

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