LLMs Show Varying Zero-Shot Goal Recognition Skills in New Study
A new study published on arXiv reveals that large language models (LLMs) demonstrate divergent performance in zero-shot goal recognition tasks, with success rates closely tied to their ability to integrate contextual evidence. The research, titled "Zero-Shot Goal Recognition with Large Language Models" (arXiv:2605.15333v1), systematically evaluates frontier LLMs on classical Planning Domain Definition Language (PDDL) benchmarks.
Goal recognition—a task requiring models to infer goals from observed actions—proves to be a "structurally better suited" challenge for LLMs compared to traditional planning tasks, according to the paper. While prior research showed LLMs can match classical planners through world-knowledge exploitation, this study highlights how goal recognition relies on evaluating consistency with existing knowledge rather than generating novel action sequences.
The researchers found performance disparities among models, with stronger results observed in systems demonstrating "evidence integration capabilities." This suggests that effective goal recognition depends not just on raw knowledge retention but on the model’s capacity to synthesize contextual clues.
The work represents the first systematic analysis of LLMs in this domain, offering insights into how these systems leverage their training data for abductive reasoning tasks. The findings could inform future developments in AI applications requiring intent inference, such as autonomous systems and human-computer interaction.
