Controlled experiments show language models extract correct answers from contradictory data only when errors are structurally incoherent, supporting the hypothesis that gradient descent selects the most compressible answer cluster.
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Proposes a two-gradient-field model with candidate order parameters alpha_dagger and kappa_c to unify phase transitions across learning theory and non-equilibrium chemistry.
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Truth as a Compression Artifact in Language Model Training
Controlled experiments show language models extract correct answers from contradictory data only when errors are structurally incoherent, supporting the hypothesis that gradient descent selects the most compressible answer cluster.
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Phase Transitions in Driven Informational Systems: A Two-Field Perspective on Learning Theory and Non-Equilibrium Chemistry
Proposes a two-gradient-field model with candidate order parameters alpha_dagger and kappa_c to unify phase transitions across learning theory and non-equilibrium chemistry.