Phase Diagrams for Human–AI Development: An Observational Theory of Finalization and Responsibility
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2026
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| _version_ | 1866901949894361088 |
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| author | Shibuki, Katsuya |
| author_facet | Shibuki, Katsuya |
| contents | <p>In human–AI collaborative development, the generation of change units (e.g., PRs, diffs, change requests) accelerates and the number of concurrent change processes increases. Meanwhile, exposure to irreversible loss (safety, legal, economic, reputational, etc.) and accountability ultimately collapse at a responsibility-fold point (RP) coupled to observable finalization events. As generation pressure (arrival rate × concurrency) grows, the RP fold point’s effective service rates for finalization and for maintaining the correctness reference system (CRS) and verification recipes (VR) can become bottlenecks. The operational regime may then exhibit phase transitions—sudden shifts from stable to failure modes—regarding (a) controllability (whether unfinalized WIP remains bounded), (b) CP health (whether CRS/VR remain coherent rather than stale), and (c) reproducibility (whether the same verification recipe yields a unique finalized state).</p> <p>This paper separates the development system into a Data Plane (DP; object layer) and a Control Plane (CP; meta layer), and proposes an observational language that describes phase transitions using variables estimable from logs. Core variables include the finalization load ratio $\rho$, CRS staleness pressure $\Pi$, verification-recipe (VR) staleness pressure $\Psi$, a non-commutativity proxy $1-\hat p_\Delta$, a reproducibility-loss proxy $\hat\varepsilon$, proxies for verification-gate detector performance, exploration share $\hat s$, CRS resolution $\hat R$, and phase-diagram coordinates such as concurrency pressure $\kappa$ and the “non-commutativity × strictness” summary $\chi$. Phase transitions are defined observationally as structural changes in distributional shape, with heavy-tailed cycle-time distributions as a canonical example. This paper makes projection relations among phase diagrams explicit, provides two example class propositions (non-monotone boundaries; possible reproducibility loss under concurrent finalization with non-commutative updates), and enumerates falsifiability conditions.</p> <p>The goal is to avoid premature heuristic short-circuits from experience and instead describe diverse futures (centralized review, formalization and automated verification, exploration-heavy regimes, relaxed reproducibility requirements, institutional shifts in responsibility) as movements of phase boundaries.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18265124 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Phase Diagrams for Human–AI Development: An Observational Theory of Finalization and Responsibility Shibuki, Katsuya design theory human–AI collaboration phase diagram finalization responsibility correctness reference system <p>In human–AI collaborative development, the generation of change units (e.g., PRs, diffs, change requests) accelerates and the number of concurrent change processes increases. Meanwhile, exposure to irreversible loss (safety, legal, economic, reputational, etc.) and accountability ultimately collapse at a responsibility-fold point (RP) coupled to observable finalization events. As generation pressure (arrival rate × concurrency) grows, the RP fold point’s effective service rates for finalization and for maintaining the correctness reference system (CRS) and verification recipes (VR) can become bottlenecks. The operational regime may then exhibit phase transitions—sudden shifts from stable to failure modes—regarding (a) controllability (whether unfinalized WIP remains bounded), (b) CP health (whether CRS/VR remain coherent rather than stale), and (c) reproducibility (whether the same verification recipe yields a unique finalized state).</p> <p>This paper separates the development system into a Data Plane (DP; object layer) and a Control Plane (CP; meta layer), and proposes an observational language that describes phase transitions using variables estimable from logs. Core variables include the finalization load ratio $\rho$, CRS staleness pressure $\Pi$, verification-recipe (VR) staleness pressure $\Psi$, a non-commutativity proxy $1-\hat p_\Delta$, a reproducibility-loss proxy $\hat\varepsilon$, proxies for verification-gate detector performance, exploration share $\hat s$, CRS resolution $\hat R$, and phase-diagram coordinates such as concurrency pressure $\kappa$ and the “non-commutativity × strictness” summary $\chi$. Phase transitions are defined observationally as structural changes in distributional shape, with heavy-tailed cycle-time distributions as a canonical example. This paper makes projection relations among phase diagrams explicit, provides two example class propositions (non-monotone boundaries; possible reproducibility loss under concurrent finalization with non-commutative updates), and enumerates falsifiability conditions.</p> <p>The goal is to avoid premature heuristic short-circuits from experience and instead describe diverse futures (centralized review, formalization and automated verification, exploration-heavy regimes, relaxed reproducibility requirements, institutional shifts in responsibility) as movements of phase boundaries.</p> |
| title | Phase Diagrams for Human–AI Development: An Observational Theory of Finalization and Responsibility |
| topic | design theory human–AI collaboration phase diagram finalization responsibility correctness reference system |
| url | https://doi.org/10.5281/zenodo.18265124 |