arXiv:2605.22138v1 Announce Type: cross Abstract: How should an agent decide when and how to plan? A dominant approach builds agents as reactive policies with adaptive computation (e.g., chain-of-thought), trained end-to-end expecting planning to emerge implicitly. Without control over the presence, structure, or horizon of planning, these systems dramatically increase reasoning length, yielding inefficient token use without reliable accuracy gains. We argue efficient agentic reasoning benefits from decomposing decision-making into three systems: simulative reasoning (System II) grounding deli
The proliferation of Large Language Models (LLMs) and the increasing complexity of AI tasks are pushing the need for more efficient and robust agentic reasoning architectures.
This research directly addresses the inefficiency and unreliability of current end-to-end approaches in AI planning, which is crucial for scaling AI applications and reducing computational costs.
The focus shifts from implicit, emergent planning in large models to explicit, structured decomposition of decision-making into distinct reasoning systems within AI agents.
- · AI researchers and developers
- · Companies investing in AI agents and autonomous systems
- · Computational infrastructure providers
- · Organizations relying solely on inefficient, opaque LLM reasoning
- · Current purely reactive policy architectures
More efficient and reliable AI agents become possible, reducing inference costs and improving task success rates.
This efficiency could accelerate the development and deployment of complex AI agents across various industries, including robotics and automation.
The structured approach to agentic reasoning might lead to more interpretable and controllable AI systems, addressing current black-box concerns.
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Read at arXiv cs.LG