Referential Regimes：基于变换的中性基底身份刻画研究
Referential Regimes: Transformation-Based Identity Profiles for Neutral Substrates
AI 导读
这篇 arXiv 论文研究记录变换下的身份保持问题，构建 Neutral Substrates（arXiv:2601.14271）之上的引用机制框架。作者将候选身份基底与记录变换族配对，划分为保持、破坏、中性或不适用四类，并用 Lean 4 对作用域分解、规则细化和分叉的六个分类单元进行推导与证明。形式化使身份声明可审计，其结论依赖于所声明的基底、模型和担保。
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