arXiv:2606.31422v1 Announce Type: new Abstract: Long-horizon language agents do not only choose actions; they carry a private model of the world from one decision to the next. When that model drifts, a later failure can be decided before the failing action is ever taken. We study a direct repair mechanism: before committing to the next task action, an agent may ask the environment about one belief field and write the answer back into its world model. This makes environment interaction a scarce calibration resource, not merely a way to advance the task. We introduce \method, a budgeted probing
The increasing complexity and autonomy of AI agents make model reliability and calibration a critical bottleneck, driving research into direct repair mechanisms.
Improving the robustness and accuracy of world models for language agents will accelerate the deployment of reliable AI agents in complex environments.
AI agents will be able to actively query their environment to correct internal model drift, shifting from purely reactive decision-making to more proactive, self-correcting intelligence.
- · AI agent developers
- · Robotics
- · Complex autonomous systems
- · AI systems prone to uncorrected error accumulation
More reliable and less error-prone AI agents will emerge in various applications.
The cost and complexity of deploying AI agents in high-stakes environments may decrease due to enhanced trustworthiness.
This could lead to a faster societal integration of autonomous AI systems, potentially accelerating job displacement in white-collar sectors.
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Read at arXiv cs.AI