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AI Doesn't Think. It Navigates Probability Landscapes.
Stop asking what the model thinks. Ask where it's standing. A working mental model of LLM generation as terrain navigation - and why it predicts failures.
Key Takeaways
- A language model's weights define a fixed probability landscape; context determines where on it the model stands, and generation is stepwise movement from that position.
- Output is a property of the trajectory, not of hidden deliberation - the same model in two contexts is effectively two reasoners.
- Trajectory commitment means self-generated tokens entrench direction, explaining error cascades, acknowledgment-without-action, and why fresh contexts fix stuck agents.
- Attractor basins - heavily reinforced continuations like sycophancy and generic hedging - capture trajectories regardless of instructions, which is why structural interventions beat prompt legislation.
- Context engineering works because it's terraforming: examples carve grooves, retrieval relocates the walker, schemas build canyon walls.