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Target-Independent Micro-Interventions for Predicting Training Response Across Language-Model Families

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Benchmark scores describe what a checkpoint can do now, but they do not determine how it will respond to the next training episode. We measure this missing state by branching four short, standardized, target-independent micro-interventions from the same checkpoint and recording their effects in a common capability space. Together with current capability, these responses form L-State; its pulse block supports a flexible direct readout and a structure-preserving operator readout. Under smooth local dynamics, the operator construction admits an end-to-end cross-family bound with explicit source-

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