AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents

TL;DR

Empirical results across retrieval-augmented reasoning, tool-augmented agent benchmarks, and embodied task environments show that AutoAgent consistently improves task success, tool-use efficiency, and collaborative robustness over static and memory-augmented baselines.

Abstract

Autonomous agent frameworks still struggle to reconcile long-term experiential learning with real-time, context-sensitive decision-making. In practice, this gap appears as static cognition, rigid workflow dependence, and inefficient context usage, which jointly limit adaptability in open-ended and non-stationary environments. To address these limitations, we present AutoAgent, a self-evolving multi-agent framework built on three tightly coupled components: evolving cognition, on-the-fly contextual decision-making, and elastic memory orchestration. At the core of AutoAgent, each agent maintains structured prompt-level cognition over tools, self-capabilities, peer expertise, and task knowledge. During execution, this cognition is combined with live task context to select actions from a unified space that includes tool calls, LLM-based generation, and inter-agent requests. To support efficient long-horizon reasoning, an Elastic Memory Orchestrator dynamically organizes interaction history by preserving raw records, compressing redundant trajectories, and constructing reusable episodic abstractions, thereby reducing token overhead while retaining decision-critical evidence. These components are integrated through a closed-loop cognitive evolution process that aligns intended actions with observed outcomes to continuously update cognition and expand reusable skills, without external retraining. Empirical results across retrieval-augmented reasoning, tool-augmented agent benchmarks, and embodied task environments show that AutoAgent consistently improves task success, tool-use efficiency, and collaborative robustness over static and memory-augmented baselines. Overall, AutoAgent provides a unified and practical foundation for adaptive autonomous agents that must learn from experience while making reliable context-aware decisions in dynamic environments.

Figures (3)

• Figure 1: The AutoAgent architecture. The Execution Cycle (solid black arrows) handles real-time task progression. The Evolution Cycle (dashed red arrows) drives long-term adaptation. The Elastic Memory Orchestrator couples both cycles by managing experience.
• Figure 2: Cognition as the interface between the LLM and the action space. Tool calls, collaboration, and self-driven generations are treated as actions; their descriptions and beliefs are part of cognition and are updated from practice through execution outcomes and memory updates.
• Figure 3: The framework of the proposed Elastic Memory Orchestration (EMO) module. At each step of history, action information will be preserved in the Memory Pool in two forms: the complete and lossless raw data, and the summarized action abstracts. At the beginning of each current step, the EMO module first concatenates the action abstracts from all historical steps as input to the selector. The selector determines whether the actions in each round should use the raw information (False), the summarized information (True), or discard the corresponding step’s information (None). After this round of judgment, the historical action information is reorganized accordingly. Subsequently, the selector further decides whether to fully summarize multiple historical steps by executing MemFold, or proceed with other actions such as web search, and so on.