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When solving problems, LLMs benefit from a 'chain of thought' approach. By breaking down tasks into smaller, familiar steps, they reduce prediction entropy and increase confidence in the final answer.

Vishal Misra's work on understanding LLMs is profound. He has developed models that reduce the complex, multidimensional space of LLMs into a geometric manifold, allowing us to predict where reasoning can move within that space. This approach reflects how humans simplify the complex universe into manageable forms for reasoning.

The iterative nature of science requires LLMs to engage in simulations, theoretical calculations, and experiments to discover scientific insights.

The debate between LLMs and other reasoning models highlights the limitations of LLMs in understanding real-world context and predicting the future.

LLMs are criticized for lacking a true world model because they predict human responses rather than actual events.

Large Language Models (LLMs) create Bayesian manifolds during training. They confidently generate coherent outputs while traversing these manifolds, but veer into 'confident nonsense' when they stray from them.

Current LLMs do not develop true world models; they build models of what a human would say next, relying on human-derived concepts.

LLMs are criticized for lacking a true world model because they predict human-like responses rather than actual outcomes.

The integration of geometric reasoning with LLMs can enhance the representation of atoms and design geometries, benefiting scientific research.

The use of LLMs and VLMs in robotics provides a way to incorporate common sense into robotic systems, allowing them to make reasonable guesses about potential outcomes without prior experience of mistakes.