Do Neuromodulator-Inspired State Prompts Generalize Across LLM Families? A Publicly Preregistered Audit of Output Length, Semantic Breadth, and Instruction Equivalence

Completed results, manuscript and full reproduction package for the publicly preregistered Route B study (preregistration: https://doi.org/10.5281/zenodo.22655376, published before any formal target output was generated). Using official GLM-5.2 and Qwen3.8-Max-0902 APIs, the study crossed 24 Chinese open-ended tasks, eight component-matched prompt conditions, four repetitions and two target models (1,536 fresh-session responses). The rich fatigue package shortened responses relative to neutral in both models (GLM -0.314, 95% CI [-0.383, -0.247]; Qwen -0.349 [-0.394, -0.302]; one-sided exact p = 5.96e-8 in each), and the rich curiosity package increased condition-blind semantic breadth in both (GLM +1.083 [0.562, 1.583]; Qwen +0.708 [0.458, 0.953]). Rich-direct practical equivalence did not pass the cross-model rule for either construct: fatigue equivalence held only in GLM and curiosity equivalence only in Qwen, so both fixed sequences stopped. Machine ratings were highly repeatable but agreed only moderately with a blind author validation sample. A procedural ordering deviation during sealing of the blind review is disclosed in the manuscript and package. The manuscript PDF (v0.3, dated 23 September 2026) predates this deposit; its data-availability statement that results were not yet public is superseded by this record. The package is split into four archives because of upload limits; extract all four into one folder. Results concern observable outputs only and do not establish emotion, consciousness, biological neuromodulation, persistent state or identical internal computation.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-16
DOI
https://doi.org/10.5281/zenodo.23416523
Primary Topic
Topic Modeling
Type
preprint
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preprint

Do Neuromodulator-Inspired State Prompts Generalize Across LLM Families? A Publicly Preregistered Audit of Output Length, Semantic Breadth, and Instruction Equivalence

Zhifeng TAN
Zenodo (CERN European Organization for Nuclear Research)
Topic Modeling
preprint

Do Neuromodulator-Inspired State Prompts Generalize Across LLM Families? A Publicly Preregistered Audit of Output Length, Semantic Breadth, and Instruction Equivalence

Zhifeng TAN
preprint en

Abstract

Completed results, manuscript and full reproduction package for the publicly preregistered Route B study (preregistration: https://doi.org/10.5281/zenodo.22655376, published before any formal target output was generated). Using official GLM-5.2 and Qwen3.8-Max-0902 APIs, the study crossed 24 Chinese open-ended tasks, eight component-matched prompt conditions, four repetitions and two target models (1,536 fresh-session responses). The rich fatigue package shortened responses relative to neutral in both models (GLM -0.314, 95% CI [-0.383, -0.247]; Qwen -0.349 [-0.394, -0.302]; one-sided exact p = 5.96e-8 in each), and the rich curiosity package increased condition-blind semantic breadth in both (GLM +1.083 [0.562, 1.583]; Qwen +0.708 [0.458, 0.953]). Rich-direct practical equivalence did not pass the cross-model rule for either construct: fatigue equivalence held only in GLM and curiosity equivalence only in Qwen, so both fixed sequences stopped. Machine ratings were highly repeatable but agreed only moderately with a blind author validation sample. A procedural ordering deviation during sealing of the blind review is disclosed in the manuscript and package. The manuscript PDF (v0.3, dated 23 September 2026) predates this deposit; its data-availability statement that results were not yet public is superseded by this record. The package is split into four archives because of upload limits; extract all four into one folder. Results concern observable outputs only and do not establish emotion, consciousness, biological neuromodulation, persistent state or identical internal computation.

Zenodo (CERN European Organization for Nuclear Research)
Hong Kong Metropolitan University (HK)
Topic Modeling
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