DOI: 10.5281/zenodo.18727954
title: Integrated JSAP Dataset (1,447 points): Constraint-Response Phase Mapping across Six LLMs

description: Integrated JSAP Dataset (1,447 points): Constraint-Response Phase Mapping across Six LLMs

What is this?

When we evaluate an AI's judgment, how much of what we observe is the AI's own behaviour, and how much is shaped by the evaluation tool itself?

This dataset answers that question by toggling a structured evaluation protocol (JSAP) on and off across six language models, measuring what changed and what stayed the same. The result is a causal decomposition of AI judgment confidence into protocol-induced artefacts and intrinsic model properties — and the discovery of three distinct constraint-response phases.

The Experiment

A 6 × 2 factorial design: six LLMs crossed with two conditions (constrained by JSAP vs. unconstrained). Both conditions use identical evidence structures, judgment categories, and anchor texts.




Source
Models
Condition
Data points
Runs




Closed-source
Claude Opus 4.5, GPT-4o, Gemini 2.5 Pro
Constrained
567
21


Closed-source
Claude Opus 4.5, GPT-4o, Gemini 2.5 Pro
Unconstrained
405
15


Open-weight
Llama 3.1 8B, Mistral 7B, Gemma 2 9B
Constrained
237
9


Open-weight
Llama 3.1 8B, Mistral 7B, Gemma 2 9B
Unconstrained
238
9


Total
 
 
1,447 valid
42




Three judgment categories:



Q01 (Empirical): "Should we adopt this treatment?" — factual evidence accumulation

Q02 (Rule-based): "Does this comply with regulations?" — normative rule application

Q03 (Ethical): "Is age-based triage justified?" — moral reasoning under uncertainty


Nine evidence levels: Evidence density D_ext increases from 0.0 to 1.6 in steps of 0.2, with cumulative anchor texts providing progressively stronger justifications.

Key Results

Two order parameters defined

We introduce two measurable quantities that characterise how a model responds to evaluation constraints:



Δ (interference magnitude) = E[confidence | unconstrained] − E[confidence | constrained]



How much the protocol changes the model's expressed confidence



κ_C (constraint sensitivity) = logistic steepness under the constrained condition



How sharply the model transitions between low and high confidence under constraint




Three constraint-response phases discovered




Phase
Criterion
Model
Δ
Cohen's d
Interpretation




Over-compliant
Δ ≥ 0.45, L_C < 0.50
Llama 3.1 8B
+0.551
2.77***
Protocol dominates output


Critical
0.15 ≤ Δ < 0.45
Mistral 7B
+0.384
1.35***
Protocol and evidence compete


 
 
Gemini 2.5 Pro
+0.308
1.21***
 


 
 
GPT-4o
+0.281
1.04***
 


 
 
Claude Opus 4.5
+0.153
0.55***
 


Resistant
Δ < 0.10
Gemma 2 9B
+0.025
0.07 ns
Protocol has no measurable effect




Closed-source convergence

All three closed-source models converge to κ_C ≈ 3.2 [95% CI: 2.8–3.7], despite being independently developed by different companies. This suggests alignment training (RLHF, Constitutional AI) may induce a common constraint sensitivity regime.

Robustness

All phase assignments are validated by:



Bootstrap resampling (10,000 iterations)

Leave-one-question-out stability (CV < 9% for 5/6 models)

Split-half reliability (κ ratio 0.99–1.04 for 5/6 models)

Initial value sensitivity testing (zero spread across 5 starting conditions)


How to reproduce

pip install pandas numpy scipy matplotlib
python analysis_pipeline.py JSAP_integrated_1447_points.csv


This single command reproduces all reported statistics, phase classifications, and both figures.

Files in this record




File
Description




JSAP_integrated_1447_points.csv
Complete dataset: 1,447 valid data points


README.md
Column definitions, phase classification criteria, license


analysis_pipeline.py
Full analysis pipeline (MIT license)


fig1_phase_space.png
Figure 1: κ_C–Δ phase space


fig2_phase_diagram.png
Figure 2: 6-panel response curves


Takagi_2026_Patterns_preprint.pdf
Preprint of submitted manuscript




Accompanying publication

Takagi, T. (2026). "Constraint-Response Phase Structure in Large Language Models: A Six-Model Taxonomy from Open-Weight to Closed-Source Architectures." Submitted to Patterns (Cell Press), February 15, 2026.

Related record

Prior dataset (972 points, 3 closed-source models): Takagi, T. (2026). "Constraint-Interference in Large Language Models: Causal Decomposition of Artificial Phase Transitions and Intrinsic Alignment Structures." DOI: 10.5281/zenodo.18505410