A paper published on arXiv, titled "When Does a Language Model Commit? A Finite-Answer Theory of Pre-Verbalization Commitment," investigates the timing of answer commitment in language models (S1).
The research focuses on understanding when a language model's preference for a specific answer becomes stable, even before the final answer is produced (S1).
The study introduces a method called "finite-answer preference stabilization" to analyze this phenomenon (S1).
This method projects the model's continuation probabilities onto a finite answer set, creating a log-odds code to determine answer onset (S1).
Researchers used this approach in binary tasks, yielding an exact log-odds code, δ(ξ)=Sθ(yes∣ξ)−Sθ(no∣ξ) (S1).
The research team used controlled delayed-verdict tasks with Qwen3-4B-Instruct to test their theory (S1).
The findings indicate that the contextual finite-answer projection stabilizes before the answer is parseable (S1).
The study found a mean lead time of 17-31 tokens in the main templates, and a shorter lead in a parser-clean replication (S1).
The signal tracks the model's eventual output, rather than the truth, and is recoverable from compact hidden summaries (S1).
The research also found that the signal is partly separable from cursor progress and transfers as shared information without a single invariant coordinate (S1).
Diagnostics were used to separate the measurement from online stopping, verbalizer-free belief, and causal answer control (S1).
The study showed local sensitivity of δ, but not reliable generation control (S1).
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