Code Lineages as Curricula for Language-Model Reasoning

Can revision history teach reasoning strategies that isolated examples cannot? This review answers by treating lineage-aware learning as a property of a sociotechnical workflow rather than a feature that can be read from average accuracy. The focal setting is software tasks represented as evolving graphs, where speed and fluency can conceal semantic loss, correlated self-evaluation errors, or domain-specific failure. Evidence from the assigned publications is synthesized with foundational studies of calibration, distribution shift, causal structure, and responsible deployment. Four requirements follow: preserve the lineage of source texts, alternative drafts, reward signals, confidence estimates, domain corpora, and human judgments; measure stability across relevant perturbations; connect confidence to a specific action; and maintain a route for human challenge and correction. The framework distinguishes descriptive performance from decision utility and separates uncertainty about the world from uncertainty created by the model and its evaluator. It also shows why faster inference or richer reasoning is valuable only when it improves a defined decision under a transparent resource budget. The article is a literature review and research agenda, not a report of a newly completed trial.

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