A Behavioural and Representational Evaluation of Goal-Directedness in Language Model Agents
Raghu Arghal, Fade Chen, Niall Dalton, Evgenii Kortukov, Calum McNamara, Angelos Nalmpantis, Moksh Nirvaan, Gabriele Sarti, Mario Giulianelli
Why It Matters
What makes this one worth your time
Understanding goal-directedness in AI agents can improve predictability and reliability, which is crucial for developing more autonomous and intelligent systems.
A framework to evaluate goal-directedness in LLM agents through behavior and internal representation analysis.
Summary
The paper proposes a framework for evaluating goal-directedness in language model agents by combining behavioral evaluation with interpretability analyses of internal representations. It examines an LLM agent in a 2D grid world, assessing its performance against optimal policies and decoding its internal representations to understand how it encodes spatial maps and action plans.
Key contributions
- Proposed a framework integrating behavioral and interpretability analyses for goal-directedness evaluation.
- Demonstrated the framework on an LLM agent in a 2D grid world.
- Analyzed how internal representations correlate with agent actions and reasoning processes.
Notable insights
- The use of probing methods to decode internal representations of spatial maps and action plans.
- The finding that reasoning shifts internal representations from spatial cues to immediate action selection.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2602.08964v2 Announce Type: replace-cross 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 structures, finding that performance scales with task difficulty while remaining robust to difficulty-preserving transformations and multi-goal structures. We then use probing methods to decode internal representations of the environment and multi-step action plans. We find that the LLM agent non-linearly encodes a coarse spatial map, preserving approximate task-relevant cues about its position and the goal location; that its actions are broadly consistent with these internal representations; and that reasoning reorganises them, shifting from spatial cues towards immediate action selection. Our findings support the view that introspective examination is required beyond behavioural evaluations to characterise how agents represent and pursue their objectives.