The authors track exactly when a language model's preference for a final answer becomes stable—before it actually writes the answer out. They project the model's continuation probabilities onto a finite answer set (yes/no in binary tasks) to get a log-odds measure that tells you when the model has "committed" internally. Testing on Qwen3-4B-Instruct in controlled delayed-answer tasks, they find the model's internal answer stabilizes 17–31 tokens before the answer is parseable in the text, and this signal tracks what the model will eventually output (not ground truth).
Main takeaways:
- Models commit to an answer internally before verbalizing it—the finite-answer preference stabilizes 17–31 tokens early on average in the main setup.
- The commitment signal is recoverable from hidden states and tracks the model's eventual output rather than the correct answer.
- The signal is partly separable from simple "where am I in the reasoning?" cues and transfers across prompts without a single fixed coordinate.
- Steering the log-odds locally changes the measure but doesn't reliably control which answer the model generates, separating measurement from causal control.
- The method works without greedy decoding or training probes—it just uses the model's own continuation probabilities projected onto the answer verbalizers.