arXiv:2607.08012v1 Announce Type: new Abstract: This paper studies an online variant of the assistance games framework, where an informed agent and an uninformed agent repeatedly interact over $T$ timesteps to optimize a common reward function. While the informed agent (the human) observes a latent state of the world, the uninformed agent (the assistant) observes only the human's actions. We provide the first provably efficient learning algorithms for repeated assistance games. We introduce the notion of assistance regret: the gap between the cumulative utility of interactions and that of the
The increased focus on AI agents and human-AI collaboration research is driving the need for more robust and provably optimal interaction frameworks.
This research provides foundational algorithms for improving the efficacy and predictability of AI assistance, which is crucial for deploying intelligent agents in complex environments.
The development of provably efficient learning algorithms for assistance games enables more reliable and optimizable human-AI interaction in dynamic settings.
- · AI developers
- · Robotics
- · Human-AI collaboration platforms
- · Logistics and operations
- · Inefficient AI assistance models
- · Manual task completion
Improved performance and reliability of AI agent systems in cooperative tasks.
Accelerated adoption of AI agents in sectors requiring complex human-machine interaction.
New forms of human-AI collaboration emerging from enhanced mutual understanding and optimization capabilities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG