Learning-Reasoning Correspondence (LRC) proposes that neural agents become more sample efficient, robust, and interpretable when explicit inference tuples are generated, serialized into the input, and regularized for consistency. We test that claim in a finite, matched-capacity replication across three task families: Boolean structural-causal intervention…Read more
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Learning-Reasoning Correspondence (LRC) proposes that neural agents become more sample efficient, robust, and interpretable when explicit inference tuples are generated, serialized into the input, and regularized for consistency. We test that claim in a finite, matched-capacity replication across three task families: Boolean structural-causal intervention prediction, SCAN-compatible command recombination, and gridworld planning imitation. The executed model class is a fixed hashed linear neural proxy with identical feature dimension across learned conditions, ten paired seeds, five data fractions, thirteen conditions, tuple-validity checks, ablations, oracle-symbolic upper bounds, confidence calibration, paired tests, Holm correction, and wall-clock runtime gates. The headline LRC claim is not supported in this regime: no primary endpoint passes the full conjunction of effect threshold, Holm rejection, runtime ratio at or below two, valid baseline floor, and architecture-fit gate. Raw gains are visible but conditional: causal OOD improves by 14.5 percentage points with Holm rejection but exceeds the runtime gate and remains outside the requested architecture class; command sample efficiency passes raw and statistical thresholds but is diagnostic because the full-data baseline floor fails; gridworld sample efficiency is large but not Holm-supported, and gridworld OOD remains below the 10 percentage-point target. Tuple evidence is mixed: full LRC produces high parse and semantic-validity rates, corrupted or feedback-removed tuples degrade behavior, and oracle tuples outperform learned tuples, so tuple quality appears relevant but insufficient to rescue the headline performance claim.