arXiv:2602.08964v2 Announce Type: replace Abstract: Understanding an agent's goals helps explain and predict its behaviour, yet there is no established methodology for reliably attributing goals to agentic systems. We propose a framework for evaluating goal-directedness that integrates behavioural evaluation with interpretability-based analyses of models' internal representations. As a case study, we examine an LLM agent navigating a 2D grid world towards a goal state. Behaviourally, we evaluate the agent against optimal policies across varying grid sizes, obstacle densities, and goal structur
The proliferation of advanced LLMs necessitates robust methods for understanding and controlling their behavior, especially as they move towards more autonomous applications.
Reliably attributing goals to AI systems is crucial for their safe and effective deployment, particularly in critical applications where transparency and predictability are paramount.
This framework offers a new methodology for evaluating goal-directedness in LLM agents, enhancing development and regulatory oversight of autonomous AI systems.
- · AI developers
- · AI safety researchers
- · Regulatory bodies
- · Companies deploying AI agents
- · Developers of opaque AI systems
- · Purely black-box AI evaluation methods
Improved understanding and control over emergent behaviors in complex AI agents.
Accelerated development of more reliable and trustworthy autonomous AI systems across various industries.
Potential for new legal and ethical frameworks specifically designed for goal-directed AI, impacting liability and accountability.
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Read at arXiv cs.LG