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Landau theory explains criticality in linear in-context learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a Landau theory to explain the critical phenomenon observed in linear in-context learning (ICL). This theory frames the double-descent singularity, which occurs when pretraining samples approach the number of learnable parameters, as a critical phenomenon in quenched disordered systems. The study identifies sample-to-sample fluctuations of learned parameters as the root cause of this singularity and establishes a relationship between the order parameter and the empirical relaxation matrix eigenvalues. AI

IMPACT Provides a statistical-physics framework for understanding interpolation criticality in linear in-context learning.

RANK_REASON The cluster contains a research paper detailing a new theoretical framework for understanding a phenomenon in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Landau theory explains criticality in linear in-context learning

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The cluster contains a research paper detailing a new theoretical framework for understanding a phenomenon in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Daesik Kim, Sumin Choi, Hyojae Jeon, Jung Hoon Han ·

    Landau theory of quenched criticality in linear in-context learning

    arXiv:2608.28059v1 Announce Type: cross Abstract: In-context learning (ICL) allows a pretrained model to infer a new task from examples supplied in its prompt without updating its parameters. In linear models of ICL, the prediction error develops a double-descent singularity when…

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