arXiv cs.AI· Denise M. Case·· 14 小时前AI 评分18
Referential Regimes：面向中立基质的基于变换的身份画像
Referential Regimes: Transformation-Based Identity Profiles for Neutral Substrates
AI 导读
论文在 Neutral Substrates 的条件中立性结果基础上，提出将候选身份基与记录变换族配对，并按保持、破坏、中立、不适用四类对每个变换族分类。作者用不变式定义的身份基和显式效应模型，在 Lean 4 中推导并验证了六个分类单元，覆盖范围分解、规则细化与分叉。
正文 · 原文
Abstract:Data systems operating under persistent legal, institutional, and analytic disagreement require shared reference without presupposing agreement about interpretation. This paper investigates identity preservation under transformation, building on the conditional neutrality result of Neutral Substrates (arXiv:2601.14271).
We pair candidate identity bases with families of record transformations and classify each family as preserving, breaking, neutral, or inapplicable for a basis. Using invariant-defined identity bases and explicit effect models, we derive and certify six classification cells in Lean 4 for scope decomposition, rule refinement, and forking. The derivations show how classifications depend on field granularity, admissible transformation steps, and explicit continuity warrants.
We also characterize the identity relations generated by invariant-preserving steps. Two bases generate equal relations exactly when they agree on the preservation of every admissible step in a shared comparison domain, and a single separating step witnesses a difference.
The formalization makes identity claims auditable, provides testable conditions for systems that transform records, and exposes the assumptions behind each classification. The results are conditional on the declared bases, models, and warrants, whose adequacy for particular referents requires independent justification.
| Comments: | 60 pages; revised manuscript with Lean 4-certified transformation classifications and formal results characterizing equality and separation of invariant-preserving step-generated identity relations. Companion Lean 4 formalization available on GitHub |
| Subjects: | Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2601.16152 [cs.LO] |
| (or arXiv:2601.16152v3 [cs.LO] for this version) | |
| https://doi.org/10.48550/arXiv.2601.16152 arXiv-issued DOI via DataCite |
Submission history
From: Denise M. Case [view email]
[v1]
Thu, 22 Jan 2026 17:51:02 UTC (28 KB)
[v2]
Fri, 10 Jul 2026 21:50:23 UTC (31 KB)
[v3]
Tue, 27 Oct 2026 17:52:39 UTC (36 KB)
来源：arXiv cs.AI · arxiv.org