AI paper index

Two Candidate Readouts of a Proposed Common Race: Effort-Value Attribution and Commit-Position as Substrate Signatures of Race-Architecture

2026-08-23 · Zenodo (CERN European Organization for Nuclear Research)

One-line summary

An AI research paper on Two Candidate Readouts of a Proposed Common Race: Effort-Value Attribution and Commit-Position as Substrate Signatures of Race-Architecture.

Engineering notes

Engineering notes will be added by the aipentium editorial team.

Chinese explanation / 中文解读

中文解读待补充:本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

Six effort-value biases — the IKEA effect, the endowment effect, the sunk-cost fallacy, the generation effect, effort justification, and the effort heuristic — and the widening of a language model’s commit-position under instruction-tuning are proposed here as two readouts of one race-architecture, both measurable from token logprobs alone. It is a research direction with explicit falsifiers, not a settled result. A race-architecture under bounded resources — parallel evaluation of candidates, accumulation under resource limits, and an irreversible commit — leaves a substrate state during and after each resolution. The paper proposes two candidate substrate signatures of that state and specifies the measurement each would need to become falsifiable. Trace-dominance in comparative-evaluation races. Effort invested during encoding is proposed to lay down a deeper hysteresis trace that then carries a larger signal-share in later comparative-evaluation races. This is offered as one component of an effort-essential subset of the six biases, not a single-mechanism reduction: endowment under passive-ownership manipulations and sunk-cost under commitment-without-effort are well documented in conditions where the trace component cannot be operative. Commit-position in the response trajectory. Where a race commits in its output, and how the spread of that position is shaped by training, are themselves measurable. A probe on Qwen2.5-7B base versus its instruction-tuned counterpart (identical fine-tuning content) shows the base model with a 3.4× wider cross-condition spread in commit proportion, and a recognition-to-commit coupling (Pearson r = 0.528) that the instruct model loses (r = 0.104). Claims are graded by empirical readiness: established biases re-expressed in the framework’s vocabulary (parsimony, no new content); a candidate derivation with surplus content the native literature does not make (a predicted inverted-U over effort intensity; the commit-position spread×condition signature separating base from instruction-tuned substrates); and speculative extensions flagged as such. Appendix B engages the accumulator-model literature (drift-diffusion, leaky competing accumulator) so the proposal can be assessed on its own terms. Companion work: Behavioural Friction Theory (10.5281/zenodo.19462499); the substrate framework (10.5281/zenodo.20012654); matched friction under hysteresis (10.5281/zenodo.20059863). v2 update (July 2026). The preliminary, programmatic status of the paper is unchanged; this version tightens its theory and positioning. The earlier reading of commit-position as an optimal-stopping problem with an optimum near 1/e is withdrawn on theoretical grounds: on the race account commitment is obligatory at the output deadline rather than a threshold the trajectory crosses, so there is no stopping optimum for the position to sit at. The commit-position readout is repositioned against recent work that measures when a language model commits; the metric used here is described as a normalised, disclosure-adjacent proxy, and the paper's angle is narrowed to how training moves that position. An internal-commit-versus-public-disclosure distinction reconciles the no-optimal-stopping claim with the existence of learned disclosure policies. The effort-value section adds the nearest sunk-cost neighbour in language models at its scope carve-out. Finally, the joint-measurement commitment is sharpened: proposing that two readouts index one underlying process is a claim about selective influence, and because both readouts are currently read from the same source the joint study as first described would be single-method; a new prospective prediction specifies the double dissociation, with at least one readout obtained by a different method, that a genuine test would require. Builds on v1. v3 (August 2026) — self-containment revision. Key derivations are now stated in-paper; the disclosure-position proxy's identification gap is stated as a limitation; the substrate-general reading is advanced as a hypothesis within a resource-rational framing; small-sample results are reported at correspondingly reduced strength; editorial pass. Earlier versions remain in the version history.

5.0Engineering value
7.0Research novelty
4.0Business relevance

Links and sources

Need this topic turned into a technical roadmap?

aipentium can prepare a custom AI literature review, code map, dataset map, and B2B technology assessment.

Request B2B AI research

Comments

No comments yet. Be the first to share your thoughts on this paper.
Login or register to leave a comment