arXiv:2606.06090v1 Announce Type: new Abstract: LLM-based agents increasingly tackle long-horizon tasks with interdependent decisions, where each action reshapes future constraints and intermediate errors can cascade. Existing RAG and agent memory systems organize histories by semantic similarity, retrieving content-relevant entries at decision time. We argue that this design mismatches execution-state dependencies: it fragments decision trajectories and mixes valid and erroneous traces, hindering coherent state reconstruction and error isolation. We propose MAGE (Memory as Agent-Guided Explor
The increasing complexity of LLM-based agent tasks necessitates more sophisticated memory management to overcome limitations of current semantic retrieval systems.
Improving memory and execution state management is critical for agents to achieve long-horizon goals and operate autonomously without cascading errors.
This research proposes a new paradigm for agent memory that moves beyond semantic similarity to focus on execution state, allowing for more coherent decision-making and error recovery.
- · AI developers
- · Companies deploying autonomous agents
- · Robotics
- · Developers of tool-use agents
- · Developers relying solely on simple RAG for complex agent memory
Agents will be able to perform more complex, multi-step tasks with greater reliability and less human intervention.
This could accelerate the deployment of autonomous systems in critical applications and industries requiring long-term planning.
More robust agentic capabilities may lead to new economic models and workflow automation beyond current predictions.
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Read at arXiv cs.AI