Contradiction Map — Synthesis Provenance

C-1 — "AI-native = stricter/more meta" (H1) vs "engineered dialects underperform" (H5)

• 05Side A (H1): stricter, more machine-checkable form helps comprehension/modification. Evidence: type-error dominance, compiler-as-oracle (theory + benchmark).
• Side B (H5): models work best on in-distribution idiomatic code; engineered surface goes OOD and underperforms. Evidence: EsoLang 0–11% on unfamiliar surface (benchmark).
• Resolution: split by location. Surface stays idiomatic (H5 wins for syntax); strictness moves to the envelope — types, contracts, meta, verification (H1 wins for structure). → Central law (Manifesto §3, Guide §0). The reversal was forced by dated reading: the OOD collapse is recovered by tools + current models (R2C-007), so "stricter envelope" is safe given a verification loop.

C-2 — "Specs/context files help" vs "AGENTbench: context files barely help, cost +20%"

• 06Side A: spec-driven development, requirement traceability aid comprehension (our production practice).
• Side B: AGENTbench (benchmark): human context files +4%, generated ones negative, all +20% inference cost.
• Resolution: the authors' own conclusion is "minimal requirements only" — not "no specs." Bloat that triggers unbounded exploration is the harm, not specification. → Minimal-sufficiency delivery (Manifesto §6; card Band-3 extract; lazy-push). Caveat the authors flag: low-parametric-knowledge settings (ours) likely benefit MORE from specs (R2C-009).

C-3 — "Written strategy transfers capability" vs "only executable scaffolds transfer"

• 07Side A (our prior instinct): a good prose explanation from a strong model lifts a weak one.
• Side B: EsoLang follow-up (benchmark): +Text ~0 effect (Sonnet 12→12); +Lib transformative (12→64).
• Resolution: executable beats prose for capability transfer. → Runnable capital (Manifesto §5; the entire scaffold catalog). This reversed our own prior-turn position; recorded as a death so it is not re-nucleated.

C-4 — "Type-constrained decoding cuts compile errors 75.3%/70.2%" vs "no Rust implementation exists"

• 08Side A: type-constrained decoding is highly effective (benchmark, TypeScript).
• Side B: the method is per-language manual work; only TypeScript exists; Rust's traits/lifetimes are far harder (the authors' repo, primary source).
• Resolution: route the oracle to where it exists — post-generation cargo check loop (Class E) for Rust today; constrained decoding is a future tool (vibe-tcg), staged, standing on rust-analyzer rather than reimplementing the type system. The 75.3%/70.2% (synthesis/translation — DR2-012's canonical pair) does not transfer for free. → Guide §12, tcg spec.

C-5 — Rust benchmark conflict: 58% (SWE-bench Multilingual) vs 10–17% (Multi-SWE-bench)

• 09Side A: Rust resolves well (highest of 9 languages at 58%).
• Side B: Rust resolves poorly (10–17%).
• Resolution: difficulty mix, not Rust-unfriendliness. Multi-SWE-bench is harder by construction (77% medium+hard) and Rust PRs are large by nature; within a fixed difficulty tier, the compiler's guidance outweighs the larger-edit burden (R2C-006). Rust failure correlates with EDIT SIZE. → drives the locality/size/ownership rules (Guide §1–2; cards D, I attack edit-size directly).

C-6 — Optimism vs the floor: "current models are fine" vs "weak models stay near floor"

• 10Side A (owner, dated, correct): 2026 models recover OOD via tools; pessimistic readings are stale (3-month-old generations, one-shot prompting). Evidence: R2C-007 (same tasks ~4%→~90–100% in 3 months).
• Side B: even WITH executable scaffolds, Haiku-4.5 stayed near the floor; resources amplify, don't create capability. Evidence: R2C-008 (the three-condition weak-agent test that includes Haiku 4.5 — it publishes per-model figures for Sonnet 4.6, 12→64, and GPT-5.4-mini, 5→53, and none for Haiku, so no Haiku score is on record here).
• Resolution: both true, scoped by reader capability. Optimism holds for Sonnet/GPT-mini class; a floor remains for the weakest tier (and Qwen-32B may sit lower on some axes). → the discipline lowers the floor (consume-only scaffolds for the weakest tier; build/use boundary, scaffold catalog §4) but does not claim to remove it (Manifesto §7). This is the open pilot question (R4).

C-7 — Unresolved / open (honest)

• 11Transfer generation→modification: every scaffold's value is shown for generation; transfer to comprehension/modification of in-distribution Rust is [E-mid], unmeasured on our codebase. No source resolves this. It is the central pilot validation target.
• Build/use boundary: whether weak agents can parameterize scaffolds (Classes A/I) or only consume them (G/H) — first-principles, unmeasured.
• H6 uniformity: partly measured, not settled. The ATLAS files four records under H6, and one of them measures a uniformity effect: DR1-022 (benchmark, med) — matching the syntactic paradigm of prompt and test examples lifts rule extraction 2.3–125%, read there as support that intra-corpus uniformity is an in-context signal. What no record measures under control is a codebase's own internal uniformity; that is the part that stays our hypothesis and the pilot candidate.

12These open items are why the package is BETA and why every card carries a falsifiable prediction in place of a present measurement.