Moksh Nirvaan

This work was conducted as part of Project Telos and supported by the SPAR mentorship program. For the full technical details, see our paper on arXiv. TL;DR: We present a framework for evaluating goal-directedness in LLM agents that combines behavioural evaluation with analysis of internal representations. Studying GPT-OSS-20B navigating 2D grid worlds, we find that the agent's navigation performance scales predictably with task difficulty and is robust to difficulty-preserving transformations, but is also systematically influenced by goal-like distractors. We complement behavioral analyses by decoding "cognitive maps" of the grid from the model's activations, finding that the agent coarsely encodes goal and self locations near their true positions. We find that the agent's actions are broadly consistent with these internal maps, and a substantial fraction of apparent failures can be attributed to imperfect internal beliefs. We also decode multi-step action plans well above chance from model activations, showing that reasoning reorganises representations from modeling broad structures and plans to task-relevant action selection. Our findings highlight the need for introspective examination to fully characterise agents' failures and behaviors. When we observe an AI agent navigating a maze or solving a puzzle, it's tempting to say the agent is pursuing a goal. But can we actually infer this from the agent's behavior alone? The question of whether AI systems genuinely pursue goals – and how we'd know if they did – is one of the most important open problems in AI safety. An agent that appears aligned might be pursuing hidden objectives that simply happen to align with the desired behaviour for a given context. Conversely, failing at a task does not prove a lack of goal-directedness in itself: humans fail all the time, too. Recent work in this area highlighted how purely behavioral assessments fall short of reliably distinguis