{"root_cause":"The constant feature has zero batch standard deviation, so batch-local standardization computes 0/0 and creates NaNs that propagate into the loss.","confidence":0.99,"evidence":["Observation: Training remains finite on most shards.","Observation: The first batch of the failing shard immediately produces a NaN loss.","Observation: That batch contains a feature that is constant across every example.","Observation: Preprocessing computes standardization statistics from the current batch.","Inference: The constant feature's batch standard deviation is zero; subtracting its mean produces zero, and dividing by zero yields NaN.","Inference: The shard-specific onset is explained by the feature becoming constant only in that batch."],"competing_hypotheses":["A later operation is independently invalid on this shard, such as log of a non-positive value; unlikely given the exact zero-variance standardization mechanism.","The raw batch already contains NaN or infinity; possible but not supported by the supplied evidence.","Mixed-precision overflow occurs later in the model; highly unlikely because the preprocessing already has a deterministic 0/0 path."],"first_action":"Replay the offending batch and add fail-fast finite assertions after each preprocessing operation: raw input, batch mean, batch standard deviation, centered feature, and standardized feature. This should localize the first invalid value to the division by the zero standard deviation.","prediction_if_true":"The raw feature, mean, standard deviation, and centered feature will be finite; the standard deviation will be exactly zero, and the first non-finite value will appear in the standardized feature immediately after division.","prediction_if_false":"A non-finite value will appear before standardization, the standard deviation will be nonzero, or all standardized features will remain finite, requiring inspection of the next operation in execution order.","immediate_changes":[],"fallback_logic":[]}