DeepMind论文：语言模型能推导广义相对论但无法发明它

Could a language model derive General Relativity yet still be unable to invent it?

This Google DeepMind paper argues yes by separating discovery into induction, deduction, and abduction.

Induction extracts rules from observations; deduction derives consequences once axioms are supplied.

The paper argues that induction and deduction still leave a missing operation in scientific discovery.

The missing operation is the abductive jump from experience to a new explanatory premise.

Einstein is the case study because Newtonian gravity offered almost no empirical error signal: inertial and gravitational mass agreed to 10⁻⁹, while Mercury’s perihelion anomaly was patched with the hypothetical planet Vulcan rather than treated as a reason to rebuild spacetime.

A compression-driven system would therefore have little gradient toward General Relativity.

Einstein developed General Relativity despite the data strongly favouring the old theory. His motivation came largely from conceptual conflicts and thought experiments, not from simply fitting a better model to a large set of observations.

The paper’s proposed direction is action-controllable world models that let agents intervene in physically consistent simulations and translate simulated experience into candidate axioms.

This is still a position paper, not an experiment proving that language models cannot perform abduction.