Onset-Commitment in Large Language Models: First-Token Competing-Routes as a Readout of How Committed a Route Is — Set by Training Depth, Moved by Pressure

A language model commits to a route at the very first generated token, and how committed it is can be read there — as the first-token competing-routes count (CR), the number of candidate continuations still in contention at the commit point. This paper develops that onset-commitment readout into a measurement instrument with two orthogonal levers on one dial. Training depth sets the commitment: the deeper a behaviour is trained into the weights, the further into “committed” the onset sits, and the less any later instruction can move it. Semantic pressure moves it: framing alone — “take your time” versus “answer immediately” — re-opens or quenches the onset race, with no clock or token budget for the words to refer to. The readout is invisible in whole-response mean CR and sharp at the onset: pressure shifts first-token CR from ≈2.4 (low) to 1.0 (high), p < 0.001 — a committor spike that decays within two tokens. On the same substrate an in-context disposition leaves the onset openable (gap +0.95) while the fine-tuned version of the same disposition quenches it to 0.0 — depth read directly as instruction-resistance. Susceptibility to the pressure lever follows a preliminary capacity gradient consistent with an inverted-U. The two levers cross-validate each other (depth × pressure) and dissociate from a second, downstream level of deliberation that survives fine-tuning and carries reasoning-accuracy gains (+13.3 points on a 70B model, difficulty-gated). The instrument unifies chain-of-thought, prompt folklore, and friction-theoretic pressure under one readout, giving the quenched-versus-annealed picture of route-competition both its temperature (pressure) and its disorder (training depth). Scope. CR is treated as an instrument for the language model's own decision physics — temperature, annealing, the commit point — with no claim to measure human cognition. Substrate. The competing-routes substrate is developed in the series' foundational papers. Data and code. The pressure-ladder stimuli, the position-wise CR readouts, and the evaluation scripts are released with the publication. v3 (August 2026) — attribution revision. The temperature reading in section 5 is credited to the account that owns it: reading a control parameter as a temperature governing how readily a system leaves its configuration is simulated annealing (Kirkpatrick, Gelatt & Vecchi, 1983). The borrowing is scoped rather than assumed — the pressure here is a prompt manipulation and not a sampler parameter, with no schedule and no convergence claim — and what the analogy buys is one prediction the paper tests, stated as its own to be wrong about. Section 6's dissociation, where a fine-tune quenches the onset while the body survives, is placed beside catastrophic interference (McCloskey & Cohen, 1989; French, 1999), against whose default expectation of damage the result is informative. A reproducibility note naming the tool used to check references was reworded to state what was checked. Editorial pass. Earlier versions remain in the version history.

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Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-28
DOI
https://doi.org/10.5281/zenodo.20562088
Primary Topic
Topic Modeling
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preprint
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Onset-Commitment in Large Language Models: First-Token Competing-Routes as a Readout of How Committed a Route Is — Set by Training Depth, Moved by Pressure

Tomas Pødenphant Lund
Zenodo (CERN European Organization for Nuclear Research)
Topic Modeling
preprint

Onset-Commitment in Large Language Models: First-Token Competing-Routes as a Readout of How Committed a Route Is — Set by Training Depth, Moved by Pressure

Tomas Pødenphant Lund
preprint en

Abstract

A language model commits to a route at the very first generated token, and how committed it is can be read there — as the first-token competing-routes count (CR), the number of candidate continuations still in contention at the commit point. This paper develops that onset-commitment readout into a measurement instrument with two orthogonal levers on one dial. Training depth sets the commitment: the deeper a behaviour is trained into the weights, the further into “committed” the onset sits, and the less any later instruction can move it. Semantic pressure moves it: framing alone — “take your time” versus “answer immediately” — re-opens or quenches the onset race, with no clock or token budget for the words to refer to. The readout is invisible in whole-response mean CR and sharp at the onset: pressure shifts first-token CR from ≈2.4 (low) to 1.0 (high), p < 0.001 — a committor spike that decays within two tokens. On the same substrate an in-context disposition leaves the onset openable (gap +0.95) while the fine-tuned version of the same disposition quenches it to 0.0 — depth read directly as instruction-resistance. Susceptibility to the pressure lever follows a preliminary capacity gradient consistent with an inverted-U. The two levers cross-validate each other (depth × pressure) and dissociate from a second, downstream level of deliberation that survives fine-tuning and carries reasoning-accuracy gains (+13.3 points on a 70B model, difficulty-gated). The instrument unifies chain-of-thought, prompt folklore, and friction-theoretic pressure under one readout, giving the quenched-versus-annealed picture of route-competition both its temperature (pressure) and its disorder (training depth). Scope. CR is treated as an instrument for the language model's own decision physics — temperature, annealing, the commit point — with no claim to measure human cognition. Substrate. The competing-routes substrate is developed in the series' foundational papers. Data and code. The pressure-ladder stimuli, the position-wise CR readouts, and the evaluation scripts are released with the publication. v3 (August 2026) — attribution revision. The temperature reading in section 5 is credited to the account that owns it: reading a control parameter as a temperature governing how readily a system leaves its configuration is simulated annealing (Kirkpatrick, Gelatt & Vecchi, 1983). The borrowing is scoped rather than assumed — the pressure here is a prompt manipulation and not a sampler parameter, with no schedule and no convergence claim — and what the analogy buys is one prediction the paper tests, stated as its own to be wrong about. Section 6's dissociation, where a fine-tune quenches the onset while the body survives, is placed beside catastrophic interference (McCloskey & Cohen, 1989; French, 1999), against whose default expectation of damage the result is informative. A reproducibility note naming the tool used to check references was reworded to state what was checked. Editorial pass. Earlier versions remain in the version history.

Zenodo (CERN European Organization for Nuclear Research)
Aarhus University (DK)
Peace, Justice and strong institutions
Topic Modeling
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