Any system encoding facts faces a fundamental constraint: when multiple independent locations encode the same fact, truth becomes indeterminate. We prove that DOF = 1 (Single Source of Truth) is the unique representation guaranteeing coherence, the impossibility of disagreement among encodings. The Core Theorem (Oracle Arbitrariness). In any incoherent encoding system (DOF > 1 with divergent values), no resolution is principled: for ANY oracle claiming to identify the "true" value, there exists an equally-present value that disagrees. This is not about inconvenience. It is about the determinacy of truth. Programming languages instantiate this epistemic structure: encoding systems become codebases, facts become structural specifications (class existence, method signatures), coherence becomes consistency across encoding locations, and DOF = 1 becomes the Single Source of Truth (DRY principle). Encoding systems → CodebasesFacts → Structural specifications (class existence, method signatures)Coherence → Consistency across encoding locationsDOF = 1 → Single Source of Truth (DRY principle)We prove that achieving DOF = 1 for structural facts requires specific language features: definition-time hooks AND introspectable derivation. Most mainstream languages (Java, C++, Rust, Go, TypeScript, etc.) lack these features and cannot achieve coherence for structural facts regardless of programmer effort. Four Theorems: Coherence Forcing: DOF = 1 is the unique value guaranteeing coherence. DOF = 0 means the fact is unrepresented; DOF > 1 permits incoherent states.Oracle Arbitrariness: Under incoherence, any resolution is arbitrary, no oracle is justified by the encodings alone.Language Requirements: For structural facts in software, DOF = 1 requires definition-time hooks AND introspection. These are logically forced.Strict Dominance: The coherence restoration complexity gap is unbounded: O(1) vs Ω(n).Theoretical Foundation. The derivation theory (independence, derivability, axis collapse) is established in previous work (https://zenodo.org/records/18123532). This paper proves the coherence consequences and instantiates them to programming languages. All theorems machine-checked in Lean 4 (2,104 lines, 119 theorems). Practical demonstration via OpenHCS PR #44: migration from 47 scattered checks to 1 ABC (DOF 47 → 1).

This paper establishes the separation of complexity classes $\mathbf{P}$ and $\mathbf{NP}$ through a novel homological algebraic approach grounded in category theory. We construct the computational category $\mathbf{Comp}$, embedding computational problems and reductions into a unified categorical framework. By developing computational homology theory, we associate to each problem $L$ a chain complex $C_{\bullet}(L)$ whose homology groups $H_n(L)$ capture topological invariants of computational processes. Our main result demonstrates that problems in $\mathbf{P}$ exhibit trivial computational homology ($H_n(L) = 0$ for all $n > 0$), while $\mathbf{NP}$-complete problems such as SAT possess non-trivial homology ($H_1(\mathrm{SAT}) \neq 0$). This homological distinction provides the first rigorous proof of $\mathbf{P} \neq \mathbf{NP}$ using topological methods. The proof is formally verified in Lean 4, ensuring absolute mathematical rigor. Our work inaugurates computational topology as a new paradigm for complexity analysis, offering finer distinctions than traditional combinatorial approaches and establishing connections between structural complexity theory and homological invariants.

This paper establishes the separation of complexity classes $\mathbf{P}$ and $\mathbf{NP}$ through a novel homological algebraic approach grounded in category theory. We construct the computational category $\mathbf{Comp}$, embedding computational problems and reductions into a unified categorical framework. By developing computational homology theory, we associate to each problem $L$ a chain complex $C_{\bullet}(L)$ whose homology groups $H_n(L)$ capture topological invariants of computational processes. Our main result demonstrates that problems in $\mathbf{P}$ exhibit trivial computational homology ($H_n(L) = 0$ for all $n > 0$), while $\mathbf{NP}$-complete problems such as SAT possess non-trivial homology ($H_1(\mathrm{SAT}) \neq 0$). This homological distinction provides the first rigorous proof of $\mathbf{P} \neq \mathbf{NP}$ using topological methods. The proof is formally verified in Lean 4, ensuring absolute mathematical rigor. Our work inaugurates computational topology as a new paradigm for complexity analysis, offering finer distinctions than traditional combinatorial approaches and establishing connections between structural complexity theory and homological invariants.