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Evaluating the Goal-Directedness of Large Language Models

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arxiv 2504.11844 v1 pith:KQ5LNWYL submitted 2025-04-16 cs.AI cs.CLcs.LG

Tom Everitt , Cristina Garbacea , Alexis Bellot , Jonathan Richens , Henry Papadatos , Siméon Campos , Rohin Shah This is my paper
classification cs.AIcs.CLcs.LG
keywords goal-directednessllmscapabilitiesenableevaluationsmodelstasksacross
verification ladder T0 review T1 audit T2 compute T3 formal
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To what extent do LLMs use their capabilities towards their given goal? We take this as a measure of their goal-directedness. We evaluate goal-directedness on tasks that require information gathering, cognitive effort, and plan execution, where we use subtasks to infer each model's relevant capabilities. Our evaluations of LLMs from Google DeepMind, OpenAI, and Anthropic show that goal-directedness is relatively consistent across tasks, differs from task performance, and is only moderately sensitive to motivational prompts. Notably, most models are not fully goal-directed. We hope our goal-directedness evaluations will enable better monitoring of LLM progress, and enable more deliberate design choices of agentic properties in LLMs.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Behavioural and Representational Evaluation of Goal-Directedness in Language Model Agents

    cs.LG 2026-02 conditional novelty 6.0 of 10

    An LLM navigation agent encodes a coarse spatial map and multi-step plans in its activations, and reasoning shifts these representations from broad environment information to immediate action selection.

  2. Goal-Directedness is in the Eye of the Beholder

    cs.MA 2025-08 reject novelty 5.0 of 10

    Goal-directedness cannot be measured objectively; existing behavioral and mechanistic measures only reveal a fit between the chosen formal model and the agent, so research should shift to multi-agent simulation.

  3. Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language

    cs.AI 2025-07 conditional novelty 5.0 of 10

    Research on AI 'scheming' repeats the methodological errors of 1970s ape language studies, relying on anecdote and mentalistic interpretation instead of controlled, theory-driven tests.

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