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This paper presents the first systematic zero-shot evaluation of frontier large language models as goal recognizers on classical PDDL planning benchmarks, finding that some models scale with evidence while others rely on world-knowledge priors regardless of observation accumulation.
This paper introduces Repeated Deceptive Path Planning (RDPP) and a novel framework called DeceptiveMetaPlanning (DeMP) to enable agents to maintain deception against observers that learn and adapt over time.