Evaluate the Goal-Directedness of LLMs

Analysis

Why This Paper Matters

As LLMs are increasingly deployed as autonomous agents, understanding whether they truly pursue their assigned goals—rather than merely producing plausible outputs—becomes critical. This paper addresses a gap in current evaluation practices, which typically focus on task performance (e.g., accuracy, F1) but not on the efficiency and intentionality with which models use their capabilities. The authors propose goal-directedness as a distinct metric, arguing that a model might have high capability but low goal-directedness if it fails to gather necessary information or execute plans effectively.

The significance lies in its potential to reshape how we assess LLM readiness for agentic tasks. By separating capability from goal-directedness, the framework allows researchers to identify specific weaknesses in models' agentic behavior, such as poor information gathering or lack of sustained effort. This is particularly relevant as LLMs are integrated into decision-making systems where goal misalignment could have serious consequences.

Technical Contributions

• Novel evaluation framework: Introduces a method to measure goal-directedness by decomposing tasks into subtasks that reveal a model's capabilities, then comparing actual performance to a capability-based benchmark.
• Task design: Includes tasks that require information gathering, cognitive effort, and plan execution, covering a broad spectrum of agentic behaviors.
• Cross-model comparison: Evaluates models from major labs (Google DeepMind, OpenAI, Anthropic), providing a comparative view of goal-directedness across different architectures and training approaches.
• Prompt sensitivity analysis: Tests the effect of motivational prompts, showing that goal-directedness is only moderately influenced by such interventions, suggesting it is a stable property.

Results

The abstract reports that goal-directedness is relatively consistent across tasks, indicating that it may be a general trait rather than task-specific. It also differs from task performance, meaning a model can perform well on a task without being highly goal-directed. The moderate sensitivity to motivational prompts suggests that while prompts can influence behavior, they do not drastically alter goal-directedness. Most models are not fully goal-directed, implying there is room for improvement in agentic capabilities. However, the paper does not provide specific numerical results in the abstract, so the magnitude of these effects remains unclear.

Significance

This research introduces a new evaluation dimension that could become standard for assessing LLM agentic capabilities. By distinguishing goal-directedness from performance, it enables more nuanced monitoring of LLM progress, potentially guiding future model development toward more reliable and intentional agents. The findings also have implications for AI safety, as goal-directedness is a prerequisite for aligned behavior. As LLMs are increasingly used in autonomous systems, this framework could inform design choices that enhance their ability to pursue goals effectively, reducing the risk of unintended behaviors.