Probes decode a grid cognitive map that reasoning then reorganizes

Arghal et al. use linear and MLP probes to decode a grid-position and goal-location cognitive map from GPT-OSS-20B's layer-15 pre-reasoning activations in a 2D grid-world navigation task [arghal-etal-2026-a-behavioural-and-representational-evaluation-of-goal-directedness-in-language-model-agents] The agent's chosen action agrees with the decoded map at 82.5% average accuracy across grid sizes [arghal-etal-2026-a-behavioural-and-representational-evaluation-of-goal-directedness-in-language-model-agents] Localization accuracy degrades with grid size, and post-reasoning activations reorganize the cognitive-map signal toward immediate action selection rather than a stable spatial code [arghal-etal-2026-a-behavioural-and-representational-evaluation-of-goal-directedness-in-language-model-agents]