Rational Inverse Reasoning

• Rational Inverse Reasoning (RIR) is a framework for inferring latent rewards, beliefs, and models from observed behavior based on utility-directed planning.
• It leverages inverse-preference and inverse-control techniques to deduce internal decision-making processes, even when agents display bounded or irrational actions.
• RIR has practical applications in AI and behavioral economics, offering insights that enhance understanding of both rational and nearly-rational decision environments.

Searching arXiv for the supplied papers and closely related terms to ground the article in current literature. arXiv_search query: "Inverse Rational Control (Wu et al., 2018) Occam's razor insufficient infer preferences irrational agents (Armstrong et al., 2017) Reasoning-Intensive Retrieval (Wei et al., 30 Apr 2026)" Rational Inverse Reasoning (RIR), in the inverse-preference and inverse-control literature, denotes the problem of inferring latent rewards, beliefs, or internal models from observed behavior by assuming that behavior is generated by some form of utility-directed planning. In its canonical form, the observer sees actions, policies, or trajectories and seeks the hidden “reasons” that would make them intelligible under rational or approximately rational choice. The contemporary literature, however, uses neighboring formulations rather than a single standardized framework: some work studies inverse reward inference under unknown irrationality [1712.058