{"root_cause":"The tokenized dataset cache is stale: the cached map result is being reused despite the chat-template and BOS changes, so training still receives the previous token IDs.","confidence":0.96,"evidence":["Observation: Raw source conversations changed as expected.","Observation: The printed tokenized sample is byte-for-byte identical to the previous run.","Observation: Fine-tuning behavior is also identical to the previous run.","Observation: Tokenization is performed through a cached dataset map.","Inference: The change is being lost between raw-data loading and model input, with cache reuse as the leading explanation."],"competing_hypotheses":["The updated chat template or BOS configuration is not actually passed into the tokenization function.","The template changes render identically for this particular sample, though this is unlikely given identical training behavior."],"first_action":"Force recomputation of the cached map for one known changed conversation, then compare its rendered text and token IDs with the cached result. This is the cheapest test that directly distinguishes stale cache reuse from an ineffective template change.","prediction_if_true":"The freshly recomputed rendered text or token IDs will differ from the cached sample, and a full uncached tokenization will change fine-tuning inputs.","prediction_if_false":"Fresh recomputation will remain byte-for-byte identical, shifting suspicion to the tokenizer path using the old template/BOS configuration or to changes that do not affect rendered input.","immediate_changes":[],"fallback_logic":[]}