From Propositional to Perceptual Asymmetry: Extending Frictive Policy Optimization to Asymmetric Partial Information Dialogue
arXiv:2606.30973v1 Announce Type: new Abstract: Frictive Policy Optimization (FPO; Pustejovsky et al., 2025) treats friction in collaborative dialogue -- misalignment, misunderstanding, repair -- as an epistemic signal essential to common-ground construction, rather than noise to be minimized. However, FPO and its implementations assume shared perceptual contexts, where friction arises from differently interpreted propositions over the same scene, which we define as propositional asymmetry. We extend FPO to perceptual asymmetry, where participants hold asymmetric partial information and the sa
The paper builds upon a recent 2025 development in Frictive Policy Optimization (FPO), indicating a natural progression in AI research to address more complex real-world interaction scenarios beyond shared perceptual contexts.
This research is crucial for advancing AI's ability to operate effectively in dynamic, information-asymmetric environments, leading to more robust and human-like AI agents.
AI models will move from assuming shared understanding to actively navigating and leveraging information asymmetry, enabling more sophisticated and adaptive interactions in collaborative and adversarial settings.
- · AI agents developers
- · Robotics
- · Generative AI
- · Collaborative software
- · Simple rule-based AI systems
- · AI limited to shared context
AI systems will be better equipped to handle real-world scenarios where different agents possess unique, partial information.
This improved understanding of asymmetry will enhance the development of AI agents capable of more nuanced negotiation, strategic planning, and conflict resolution.
Sophisticated AI agents could lead to new forms of human-AI collaboration where AI proactively fills information gaps or identifies misalignment, changing work processes across many industries.
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