Zero-Shot Goal Recognition with Large Language Models

2026-05-14 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A new study systematically evaluates frontier Large Language Models (LLMs) for zero-shot goal recognition using classical PDDL benchmarks. While LLMs have shown near-parity with classical planners in planning domains, their goal recognition competence varies significantly. The research reveals that some LLMs effectively integrate accumulating evidence, achieving accuracy comparable to landmark-based methods with full observations. In contrast, other models remain heavily influenced by their world-knowledge priors, showing limited improvement even with increased evidence. Qualitative analysis indicates this divergence stems from fundamental differences in how models integrate evidence, rather than just domain familiarity. These findings establish goal recognition as a crucial benchmark for assessing the foundational planning knowledge within LLMs.

Key takeaway

For AI scientists evaluating LLM capabilities in planning, you should consider goal recognition as a robust and principled benchmark. Focus on models that demonstrate strong evidence integration, as this indicates genuine symbolic reasoning rather than reliance on world-knowledge priors. Your choice of LLM for planning tasks should account for its ability to scale with observational evidence, which is critical for real-world application accuracy.

Key insights

LLM goal recognition competence varies, highlighting fundamental differences in evidence integration.

Principles

• Goal recognition suits LLM strengths.
• Evidence integration varies across LLMs.

Method

The study conducts a systematic zero-shot evaluation of frontier LLMs on classical PDDL benchmarks to assess goal recognition capabilities and analyze reasoning traces.

In practice

• Use goal recognition for LLM planning evaluation.
• Prioritize LLMs with strong evidence integration.

Topics

• Large Language Models
• Goal Recognition
• Zero-Shot Evaluation
• PDDL Benchmarks
• Evidence Integration

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.