arXiv:2507.23773v3 Announce Type: replace-cross Abstract: What does it mean to plan? Current agentic systems, whether scaffolded workflows or end-to-end policies, rely on reactive decision-making: selecting the next action via a fixed procedure with at most undifferentiated adaptive computation (e.g., chain-of-thought) lacking explicit modeling of future outcomes. This limits generalizability, as each new task demands re-engineering rather than transfer of shared reasoning capacity. Humans, by contrast, plan by mentally simulating consequences of candidate actions within an internal world mode
The paper builds on recent advancements in large language models and world modeling, addressing the urgent industry need for more adaptable and generalizable AI systems beyond reactive decision-making.
This development proposes a pathway to more robust and general-purpose AI agents capable of truly autonomous planning and complex problem-solving, moving beyond task-specific conditioning.
AI systems could transition from 'reactive decision-making' to 'simulative reasoning,' enabling them to plan and adapt to novel situations without constant human re-engineering.
- · AI research labs
- · Generative AI platforms
- · Robotics companies
- · Automation software providers
- · Companies relying on narrow AI applications
- · Workflow automation platforms requiring heavy customization
The ability of AI agents to plan and simulate outcomes will lead to more complex and multifaceted autonomous systems.
Increased agent autonomy will accelerate the collapse of certain white-collar workflows, requiring fewer human interventions for multi-step processes.
The development of highly generalizable agentic AI could lead to new forms of economic value creation and entirely new industries based on AI-driven problem-solving.
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Read at arXiv cs.LG