Author: Denis Avetisyan
A new system, MobiMem, empowers AI agents to evolve and improve their performance through sophisticated memory management, bypassing the need for constant retraining.
MobiMem utilizes specialized memory types and system-level services to enable long-term learning, action reuse, and improved efficiency in AI agents performing GUI automation and scheduling tasks.
While large language model (LLM) agents excel at automating tasks, their reliance on continual model retraining presents challenges for post-deployment adaptation and efficiency. This paper introduces MOBIMEM, a memory-centric agent system proposed in ‘Beyond Training: Enabling Self-Evolution of Agents with MOBIMEM’, designed to facilitate iterative self-evolution without model updates. By leveraging specialized memories for user profiles, task experiences, and action sequences, alongside OS-inspired services for scheduling and reuse, MOBIMEM achieves improved personalization, capability, and speed. Could this memory-centric approach unlock truly autonomous and continuously improving agents for complex real-world applications?
The Imperative of Memory-Centric Agents
Contemporary agent systems, frequently built upon the paradigm of continuous model training, exhibit notable difficulties when confronted with the complexities of dynamic environments. The constant need to refine models through repeated exposure to new data creates a significant computational burden and hinders real-time responsiveness. This approach often results in brittle performance, as agents struggle to generalize beyond the specific conditions encountered during training-a phenomenon exacerbated by the ‘catastrophic forgetting’ of previously learned skills. Moreover, the reliance on extensive retraining limits scalability and efficient adaptation to unforeseen circumstances, preventing these agents from exhibiting the fluid intelligence characteristic of biological systems. Consequently, the limitations of continuous training necessitate a shift toward architectures that prioritize efficient knowledge retention and reuse.
Current agent systems often falter not due to a lack of processing power, but from a critical inability to learn from experience in a meaningful way. Without persistent memory, each interaction is largely treated as novel, forcing repeated learning of similar situations and hindering efficient adaptation to changing environments. This reliance on continuous retraining is demonstrably inefficient; agents struggle to generalize beyond immediate contexts and exhibit limited capacity to leverage past successes – or failures – when facing new, yet related, challenges. Consequently, these systems often appear reactive rather than proactive, unable to build a cumulative understanding of the world and apply it strategically, effectively diminishing their long-term performance and adaptability.
The prevailing paradigm of artificial intelligence, focused on ever-larger models and continuous retraining, faces inherent limitations in truly dynamic environments. A shift is required towards architectures that treat memory not as an auxiliary component, but as the foundational element of intelligence itself. This reimagining proposes agents capable of accumulating, organizing, and strategically retrieving past experiences to inform present actions, mirroring the way biological organisms learn and adapt. Such memory-centric systems would move beyond reactive responses, enabling proactive problem-solving, efficient generalization to novel situations, and a capacity for cumulative learning-ultimately fostering a more robust and adaptable form of artificial intelligence. The focus isn’t simply on what an agent knows, but on how it remembers and utilizes that knowledge.
Effective intelligence hinges not simply on processing power, but on the capacity to learn from and apply past experiences to novel situations. Current agent architectures often operate within a limited contextual window, requiring constant retraining to adapt to changing environments. A paradigm shift is occurring, focusing on the development of systems capable of robust, long-term memory storage and retrieval. These emerging architectures aim to move beyond immediate sensory input, enabling agents to access and reason with a vast repository of prior knowledge. This allows for more efficient learning, improved generalization, and the ability to navigate complex, dynamic environments by leveraging previously encountered patterns and solutions, rather than relearning them repeatedly. The ability to effectively store, retrieve, and synthesize information from beyond the immediate context is therefore becoming increasingly recognized as a crucial component of truly intelligent agency.
MobiMem: An Architecture for Persistent Self-Evolution
MobiMem distinguishes itself from traditional continual learning systems by prioritizing knowledge reuse as a mechanism for self-evolution. Rather than solely relying on iterative training with new data, MobiMem agents actively leverage previously stored experiences and extracted knowledge to adapt to novel situations. This is accomplished through a persistent memory system designed to retain and recall relevant information, enabling the agent to apply past solutions to current problems without requiring retraining. The system effectively builds upon its existing knowledge base, allowing for more efficient learning and adaptation over time, and ultimately facilitating a form of autonomous self-improvement.
MobiMem’s architecture centers on three interconnected memory modules that facilitate persistent learning. Action Memory stores executable plans, representing the agent’s behavioral repertoire and enabling rapid response to known situations. Profile Memory maintains abstracted representations of entities and concepts encountered in the environment, allowing the agent to generalize and adapt to novel scenarios; this module utilizes LLMs for profile extraction and template rewriting. Finally, Experience Memory serves as a record of past interactions, including states, actions, and outcomes, enabling the agent to learn from its experiences and refine its action strategies; data in this module informs both Action and Profile Memory updates, creating a closed-loop learning system.
MobiMem utilizes a Large Language Model (LLM) to facilitate interaction between its memory modules and action execution. Specifically, the LLM performs profile extraction from Experience Memory, converting raw experiences into structured, reusable knowledge. This extracted information populates the Profile Memory, enabling the agent to generalize from past interactions. Furthermore, the LLM handles template rewriting within Action Memory, adapting pre-defined action templates to suit specific contexts derived from the current profile. This LLM-driven process effectively translates stored knowledge into actionable strategies, enabling the agent to respond dynamically to novel situations without requiring explicit re-training for each scenario.
MobiMem’s architecture incorporates an Agent Scheduler to facilitate efficient parallel execution of tasks, resulting in a 1.98x speedup compared to serial processing. This is achieved through multi-task scheduling, which allows the system to concurrently address multiple objectives and utilize available computational resources more effectively. The scheduler dynamically assigns tasks to available agents, minimizing idle time and maximizing throughput. This parallelization directly contributes to improved agent responsiveness, enabling faster reaction times and more fluid interactions with the environment.
DisGraph: A Direct-Access Architecture for Profile Management
Profile Memory utilizes a DisGraph data structure to represent user profiles, differing from traditional graph databases which prioritize edge-based relationships. In a DisGraph, semantic information – encompassing user preferences, known facts, and behavioral patterns – is stored directly within nodes. This nodal storage enables significantly faster retrieval of profile data because accessing a node’s internal information is computationally less expensive than traversing multiple edges to aggregate the same information. The design prioritizes direct access to semantic content, reducing the lookup time required to construct a comprehensive user profile for personalization and responsive interactions.
The agent’s profile management system achieves rapid access to user-specific data – preferences, known facts, and behavioral patterns – by structuring this information for direct retrieval rather than relying on relationship-based queries. This direct-access methodology results in a measured profile alignment rate of 83.1%, indicating a high degree of correlation between the agent’s understanding of the user and the user’s actual characteristics. The system prioritizes minimizing latency in accessing these profile elements, directly impacting the agent’s ability to deliver personalized and responsive interactions.
The Agent Record and Replay (AgentRR) mechanism functions by capturing sequences of actions that have previously led to successful outcomes. These recorded action sequences are then stored and subsequently reused when the agent encounters similar situations, effectively accelerating the learning process and improving overall performance. This reuse is not simply rote repetition; AgentRR allows for adaptation and modification of recorded sequences as needed to fit the current context. The system maintains structures, such as ActChain and ActTree within Action Memory, to facilitate efficient prefix-based action reuse, allowing the agent to quickly identify and apply relevant portions of previously successful sequences.
The Agent Record and Replay (AgentRR) mechanism leverages Action Memory structures, specifically ActChain and ActTree, to enable efficient prefix-based action reuse. ActChain organizes actions sequentially, while ActTree provides a hierarchical representation, allowing the agent to identify and apply previously successful action sequences to new tasks. This approach achieves a 77.3% action reuse rate when implemented with human-crafted templates, significantly optimizing task completion by reducing the need to recalculate or re-execute common action prefixes. The system stores and retrieves these action sequences, effectively caching solutions for recurring task components.
Validation and the Path Forward for Adaptive Intelligence
Rigorous testing of MobiMem’s architecture on the AndroidWorld benchmark confirms its proficiency in automating complex interactions within graphical user interfaces. The system successfully navigated and executed tasks requiring precise manipulation of on-screen elements, demonstrating a capacity for reliable, autonomous operation on real-world mobile devices. This validation is crucial, as AndroidWorld presents a challenging suite of tasks designed to assess the practical capabilities of AI agents in everyday scenarios, and MobiMem’s performance highlights its potential for widespread application in mobile automation and assistive technologies. The ability to effectively interpret and respond to GUI elements signifies a key advancement towards creating agents capable of seamlessly integrating with and simplifying human-computer interaction.
MobiMem demonstrably accelerates agent interactions through significant reductions in processing time. Evaluations reveal the system achieves up to a nine-fold decrease in end-to-end latency, enabling markedly faster responses to user requests and environmental changes. Critically, the speed at which user profiles are aligned and retrieved – a core function for personalized behavior – is exceptionally swift, registering a mere 23.83 milliseconds. This rapid profile alignment is achieved through the DisGraph structure and contributes directly to the system’s overall responsiveness, paving the way for real-time, adaptive agent behavior and improved user experiences.
The architecture of MobiMem intentionally leverages large language models (LLMs), a design choice that unlocks significant advantages in terms of adaptability and scalability. This reliance on LLMs isn’t merely a functional component; it establishes a bridge to the expansive ecosystem of natural language processing tools and datasets already in existence. Consequently, MobiMem isn’t confined to a limited scope; it can readily incorporate new linguistic knowledge, refine its understanding of user intent, and benefit from ongoing advancements in the field of NLP. This seamless integration streamlines development, accelerates innovation, and ultimately expands the potential applications of the system beyond its initial design parameters, fostering a versatile platform for increasingly sophisticated agent interactions.
The integration of Experience Memory demonstrably enhances agent performance, yielding a significant 50.3% improvement in task success rates across four distinct agent models. This substantial gain suggests the system’s ability to effectively learn from past interactions and apply that knowledge to future challenges. By retaining and leveraging experiential data, the agents exhibit markedly increased proficiency in completing assigned tasks, indicating that the architecture moves beyond static programming towards a more adaptive and robust form of artificial intelligence. This improvement highlights the potential for creating agents that not only respond to immediate commands but also refine their strategies based on accumulated experience, paving the way for more nuanced and effective interactions in complex environments.
Continued development centers on enhancing the large language model’s capacity to discern and represent intricate user profiles, moving beyond simple attribute recognition to capture nuanced preferences and behavioral patterns. Simultaneously, researchers are dedicated to optimizing the DisGraph structure – the system’s knowledge representation – to minimize computational overhead and maximize retrieval speed. These parallel efforts aim to address current limitations in handling highly detailed information and complex queries, ultimately paving the way for more robust and efficient agents capable of adapting to increasingly sophisticated user needs and operating seamlessly within dynamic environments.
The development of MobiMem signifies a crucial step towards realizing genuinely adaptive artificial agents. By effectively capturing and leveraging user experience as a dynamic memory, these agents move beyond pre-programmed responses and towards nuanced interactions tailored to individual needs and contexts. This capability promises to unlock performance gains in increasingly complex scenarios, from assisting with intricate mobile device operations to navigating multifaceted digital environments. Consequently, the potential extends beyond simple automation, paving the way for intelligent assistants that learn, anticipate, and seamlessly integrate into daily life, offering a level of personalized support previously unattainable.
The pursuit of adaptable, self-improving agents, as demonstrated by MobiMem, resonates with a fundamental tenet of computational thinking. Ada Lovelace observed, “The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform.” MobiMem moves beyond simply executing pre-programmed instructions; its memory-centric approach – leveraging profile, experience, and action templates – enables a form of algorithmic self-instruction. This isn’t origination in the human sense, but rather an expansion of the agent’s operational scope through structured recall and reuse, mirroring Lovelace’s vision of a machine exceeding its initial programming through the careful organization of knowledge. The system’s ability to evolve without continuous retraining exemplifies a move towards more robust and genuinely intelligent automation.
What Remains to Be Proven?
The presented work, while demonstrating a functional instantiation of self-evolving agency, merely shifts the locus of complexity. The elimination of perpetual model retraining is, of course, a pragmatic benefit; however, the invariants governing the long-term behavior of the ‘experience’ and ‘action’ memories remain largely unexplored. Specifically, the asymptotic complexity of retrieval within these structures-as the agent encounters genuinely novel situations-demands rigorous analysis. A system predicated on action reuse will inevitably encounter states for which no effective template exists; the graceful degradation-or, more likely, catastrophic failure-in such instances is not addressed.
Furthermore, the notion of ‘profile’ memory, implicitly encoding preferences and biases, begs the question of ontological consistency. How does the agent resolve conflicting preferences, and under what conditions does its ‘self-image’ diverge from observed behavior? The current architecture treats this as a static repository; a dynamic, self-correcting profile-grounded in formal logic-would represent a significant advance, albeit one fraught with philosophical difficulties. The true measure of an intelligent system, it seems, is not its ability to do, but its capacity to know what it does not know.
Ultimately, the pursuit of self-evolving agents will necessitate a departure from purely empirical validation. Demonstrating that a system ‘works’ on a finite set of GUI automation tasks is insufficient. The field requires formal guarantees-provable correctness-regarding the agent’s behavior in arbitrary environments. Until then, the elegant elimination of retraining remains merely a clever optimization, not a fundamental step towards true machine intelligence.
Original article: https://arxiv.org/pdf/2512.15784.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-21 13:23