ValueSeed: Teleology as a First-Class Object — Refinement, Degradation Monotonicity, and a Complete Claim Linter

ValueSeed is a foundational specification and assessment framework for treating teleology (the object of optimization) as a first-class, explicit, and mechanically verifiable research object. Modern AI alignment debates frequently remain unresolved not because empirical evidence is insufficient, but because teleology is often implicit, informally justified, and semantically incomparable across systems. Rewards, preference models, safety constraints, and “values” are typically embedded as engineering artifacts rather than declared semantic objects. This causes persistent category errors: claims of “alignment” extend beyond declared goals; comparisons across systems assume equivalent objectives without alignment declarations; and Goodhart-style failures appear as empirical surprises rather than formally declared degradation pathways. ValueSeed introduces a formal teleology specification tuple that explicitly declares:(1) teleology identity, (2) goal objects, (3) commitment semantics, (4) cost/stake semantics, (5) adjudication observers, (6) degradation channels (proxy drift / reward hacking / Goodhart modes), and (7) authority / immutability constraints.It defines a refinement preorder over teleologies, a monotone degradation semantics, and a claim language with deterministic small-step evaluation rules. This release includes ValueSeed-Lint, a deterministic claim linter that classifies claims into verdicts such as ADMISSIBLE, BLOCKED, NON-COMPLIANT, and NOT-COMPARABLE, separating empirical performance from semantic admissibility. Foundational meta-theorems are proven: Progress (well-formed teleologies admit deterministic claim evaluation), Preservation (teleology refinement preserves admissibility and truth), and Completeness of Linting (every claim receives a unique verdict under compliance). ValueSeed does not propose new learning algorithms, benchmarks, or moral theories.It provides semantic infrastructure: it makes alignment claims explicit, comparable, and auditable, and clarifies where debates are structurally undecidable under implicit or incompatible teleologies. This is a research objectification framework, not an outcome prescription.