Module decoder 

Phase-D probabilistic Recognizer — the “decoder”.

This module implements the deep-scan half of the strict/deep-scan recognizer split introduced in Phase 4 PR-2. When the engine is configured for deep-scan (batch reconciliation mode, rule-escalated region, --deep-scan CLI flag), and the strict recognizer returns zero candidates for a marking region, the engine falls back to the decoder to recover mangled markings that are one of a small set of canonical-shape deviations away from a real CAPCO-2016 marking:

• Edit-distance-1/2 token typos (SERCET → SECRET).
• Token reordering within categories (NOFORN//SECRET → SECRET//NOFORN).
• CAPCO-2016-superseded tokens (COMINT → SI).
• Case mistakes (secret//noforn → SECRET//NOFORN).
• Garbled delimiters (S ∕∕ NOFORN → S//NOFORN).

The decoder never fabricates a marking where none exists. When the observed tokens fit no CAPCO grammar template, it returns Parsed::Ambiguous { candidates: vec![] } — the zero-candidate signal per foundational-plan line 609-612.

Why this lives in marque-engine, not marque-capco

Same Constitution VII rationale as StrictRecognizer (PR-2): marque-capco may not depend on marque-core, but the decoder needs core’s fuzzy-vocab matcher and strict parser to materialize candidates. marque-engine is the sole crate where both chains converge. The original tasks.md T059/T061 placement is amended in tasks.md itself.

Scoring approach (foundational-plan §5.2)

For each candidate the decoder computes:

log_posterior(candidate | observed)
  = log_prior(candidate)                      // baked corpus priors (PR-1)
  + Σ log_likelihood(feature | candidate)     // enumerated scored features

The decoder currently scores the candidate-shape features it records from the closed FeatureId enum: EditDistance1, EditDistance2, TokenReorder, SupersededToken, and BaseRateCommonMarking. Each contributes a fixed log-odds delta documented at the feature’s call site.

FeatureId::StrictContextClassification is part of the audit- schema enum but is not currently a scored-feature term: classification-level context is enforced through the separate ParseContext::classification_floor hard filter (FR-011), which rejects below-floor candidates before scoring rather than adding a likelihood term to the posterior. FeatureId::CorpusOverrideInEffect is reserved for PR-5 when corpus-override is wired; the decoder does not emit it today. Turning either into an actual scored contributor requires a coordinated audit-schema bump (MARQUE_AUDIT_SCHEMA) per marque-rules/src/confidence.rs doc.

The top candidate wins when its posterior exceeds the runner-up by a configured ratio; below that threshold the decoder returns Parsed::Ambiguous { candidates } so the engine can surface a diagnostic rather than auto-apply. Candidate::prior_log_odds carries the prior alone (sum of token log-priors); the per-feature log-odds deltas live only in Candidate::evidence[i].log_odds, so a resolver that reconstructs prior_log_odds + Σ evidence.log_odds recovers the decoder’s internal posterior exactly, without double-counting.

What this module is NOT

• Not a full template-matching grammar engine. The MVP materializes candidates by canonicalizing observed tokens and round-tripping through the strict parser — the strict parser is the arbiter of “is this a CAPCO-shape marking.” If the canonicalized bytes strict-parse, we have a candidate; if not, we discard.
• Not a learning system. All priors are compile-time-baked &'static tables from marque_capco::priors (Constitution III: no runtime corpus override on WASM).
• Not a fix applier. The decoder proposes CapcoMarking candidates; the engine applies them through the normal Diagnostic / FixProposal path with FixSource::DecoderPosterior.

Structs

DecoderRecognizer

Phase-D probabilistic marking recognizer.

StrictOrDecoderRecognizer

Recognizer that runs the strict path first and falls back to the decoder when the strict parse yields no meaningful attributes.