Beyond Training: Enabling Self-Evolution of Agents with MOBIMEM

• URL: http://arxiv.org/abs/2512.15784v1
• Date: Mon, 15 Dec 2025 12:38:43 GMT
• Title: Beyond Training: Enabling Self-Evolution of Agents with MOBIMEM
• Abstract summary: Large Language Model (LLM) agents are increasingly deployed to automate complex tasks in mobile and desktop environments.<n>Current model-centric agent architectures struggle to self-evolve post-deployment.<n>MEMMEM is a memory-aware agent system that enables iterative self-evolution without model retraining.
• Score: 10.20348197254329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Model (LLM) agents are increasingly deployed to automate complex workflows in mobile and desktop environments. However, current model-centric agent architectures struggle to self-evolve post-deployment: improving personalization, capability, and efficiency typically requires continuous model retraining/fine-tuning, which incurs prohibitive computational overheads and suffers from an inherent trade-off between model accuracy and inference efficiency. To enable iterative self-evolution without model retraining, we propose MOBIMEM, a memory-centric agent system. MOBIMEM first introduces three specialized memory primitives to decouple agent evolution from model weights: (1) Profile Memory uses a lightweight distance-graph (DisGraph) structure to align with user preferences, resolving the accuracy-latency trade-off in user profile retrieval; (2) Experience Memory employs multi-level templates to instantiate execution logic for new tasks, ensuring capability generalization; and (3) Action Memory records fine-grained interaction sequences, reducing the reliance on expensive model inference. Building upon this memory architecture, MOBIMEM further integrates a suite of OS-inspired services to orchestrate execution: a scheduler that coordinates parallel sub-task execution and memory operations; an agent record-and-replay (AgentRR) mechanism that enables safe and efficient action reuse; and a context-aware exception handling that ensures graceful recovery from user interruptions and runtime errors. Evaluation on AndroidWorld and top-50 apps shows that MOBIMEM achieves 83.1% profile alignment with 23.83 ms retrieval time (280x faster than GraphRAG baselines), improves task success rates by up to 50.3%, and reduces end-to-end latency by up to 9x on mobile devices.

Related papers

• HyMobileAgent: Data-Environment Co-Scaling for Efficient GUI Agents [57.53434680014667]
  HyMobileAgent is a vision-native foundation model built on Hy3.0-VL-A3B.<n>We develop a joint data and environment centric scaling framework to address the key bottlenecks of mobile interaction.
  arXiv  Detail & Related papers  (2026-07-16T04:12:42Z)
• AutoMem: Automated Learning of Memory as a Cognitive Skill [63.07128029793901]
  We show that memory expertise is a learned skill.<n>We introduce AutoMem, a framework that automates the two axes of memory management.<n>Our results show that memory management is an independently learnable skill.
  arXiv  Detail & Related papers  (2026-07-01T17:57:03Z)
• MEMCoder: Multi-dimensional Evolving Memory for Private-Library-Oriented Code Generation [7.313211974278645]
  MEMCoder is a novel framework that enables Large Language Models to autonomously accumulate and evolve Usage Guidelines.<n>During inference, MEMCoder employs a dual-source retrieval mechanism to inject both static documentation and relevant historical guidelines into the context.<n>It exhibits vastly superior domain-specific adaptation compared to existing memory-based continual learning methods.
  arXiv  Detail & Related papers  (2026-04-27T09:27:59Z)
• VeriAgent: A Tool-Integrated Multi-Agent System with Evolving Memory for PPA-Aware RTL Code Generation [52.43796056267479]
  We propose a PPA-aware, tool-integrated multi-agent framework for high-quality verilog code generation.<n>Our framework explicitly incorporates EDA tools into a closed-loop workflow composed of a textitProgrammer Agent, a textitCorrectness Agent, and a textitPPA Agent<n>To support continuous improvement without model retraining, we introduce an textitEvolved Memory Mechanism that externalizes optimization experience into structured memory nodes.
  arXiv  Detail & Related papers  (2026-03-18T11:25:40Z)
• ActionEngine: From Reactive to Programmatic GUI Agents via State Machine Memory [3.279665979821265]
  ActionEngine is a training-free framework that transitions from reactive execution to programmatic planning.<n>Our agent achieves 95% task success with on average a single LLM call, compared to 66% for the strongest vision-only baseline.
  arXiv  Detail & Related papers  (2026-02-24T03:03:18Z)
• MiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context Modeling [80.48332380100915]
  MiniCPM-SALA is a hybrid model that integrates the high-fidelity long-context modeling of sparse attention with the global efficiency of linear attention.<n>On a single NVIDIA A6000D GPU, the model achieves up to 3.5x the inference speed of the full-attention model at the sequence length of 256K tokens.
  arXiv  Detail & Related papers  (2026-02-12T09:37:05Z)
• Jenius Agent: Towards Experience-Driven Accuracy Optimization in Real-World Scenarios [0.9069311779417014]
  This paper introduces an agent framework grounded in real-world practical experience.<n>An end-to-end framework named Jenius-Agent has been integrated with three key optimizations.<n>Experiments show a 20 percent improvement in task accuracy, along with a reduced token cost, response latency, and invocation failures.
  arXiv  Detail & Related papers  (2026-01-05T07:35:12Z)
• MOBIUS: Big-to-Mobile Universal Instance Segmentation via Multi-modal Bottleneck Fusion and Calibrated Decoder Pruning [91.90342432541138]
  Scaling up model size and training data has advanced foundation models for instance-level perception.<n>High computational cost limits adoption on resource-constrained platforms.<n>We introduce a new benchmark for efficient segmentation on both high-performance computing platforms and mobile devices.
  arXiv  Detail & Related papers  (2025-10-16T18:00:00Z)
• MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents [84.62985963113245]
  We introduce MEM1, an end-to-end reinforcement learning framework that enables agents to operate with constant memory across long multi-turn tasks.<n>At each turn, MEM1 updates a compact shared internal state that jointly supports memory consolidation and reasoning.<n>We show that MEM1-7B improves performance by 3.5x while reducing memory usage by 3.7x compared to Qwen2.5-14B-Instruct on a 16-objective multi-hop QA task.
  arXiv  Detail & Related papers  (2025-06-18T19:44:46Z)
• PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time [87.99027488664282]
  PersonaAgent is a framework designed to address versatile personalization tasks.<n>It integrates a personalized memory module and a personalized action module.<n>Test-time user-preference alignment strategy ensures real-time user preference alignment.
  arXiv  Detail & Related papers  (2025-06-06T17:29:49Z)
• MAPLE: A Mobile Agent with Persistent Finite State Machines for Structured Task Reasoning [46.18718721121415]
  We present MAPLE, a state-aware multi-agent framework that abstracts app interactions as a Finite State Machine (FSM)<n>We computationally model each UI screen as a discrete state and user actions as transitions, allowing the FSM to provide a structured representation of the app execution.<n> MAPLE consists of specialized agents responsible for four phases of task execution: planning, execution, verification, error recovery, and knowledge retention.
  arXiv  Detail & Related papers  (2025-05-29T16:08:51Z)
• Task Memory Engine: Spatial Memory for Robust Multi-Step LLM Agents [0.0]
  Large Language Models (LLMs) falter in multi-step interactions due to reliance on linear, unstructured context.<n>We introduce the Task Memory Engine (TME), a modular memory controller that transforms existing LLMs into robust, revision-aware agents.<n>TME implements a spatial memory framework that replaces flat context with graph-based structures to support consistent, multi-turn reasoning.
  arXiv  Detail & Related papers  (2025-05-26T02:53:22Z)
• Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks [85.48034185086169]
  Mobile-Agent-E is a hierarchical multi-agent framework capable of self-evolution through past experience.<n>Mobile-Agent-E achieves a 22% absolute improvement over previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-01-20T20:35:46Z)
• MOFHEI: Model Optimizing Framework for Fast and Efficient Homomorphically Encrypted Neural Network Inference [0.8388591755871735]
  Homomorphic Encryption (HE) enables us to perform machine learning tasks over encrypted data.<n>We propose MOFHEI, a framework that optimize the model to make HE-based neural network inference, fast and efficient.<n>Our framework achieves up to 98% pruning ratio on LeNet, eliminating up to 93% of the required HE operations for performing PI.
  arXiv  Detail & Related papers  (2024-12-10T22:44:54Z)
• Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration [52.25473993987409]
  We propose Mobile-Agent-v2, a multi-agent architecture for mobile device operation assistance. The architecture comprises three agents: planning agent, decision agent, and reflection agent. We show that Mobile-Agent-v2 achieves over a 30% improvement in task completion compared to the single-agent architecture.
  arXiv  Detail & Related papers  (2024-06-03T05:50:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.