When implementing content extraction/layout algorithms, treat “heuristics” as *probabilistic classifiers*: constrain them with invariants, gate them with evidence, and lock behavior with targeted regression tests. Practical standard (apply per heuristic): 1) State the invariant explicitly and enforce its shape - Example invariants from the discussions: “marker must be line-start shaped”, “drop-cap target must be on the same page”, “a continuation must share the right geometry but also START a paragraph”. 2) Only compare like with like (units/scopes) - Ensure coordinate/geometry comparisons use comparable quantities (e.g., line-step/leading rather than unrelated em-scaled fractions) and correct scope (don’t let previous-page coordinates influence current-page decisions). 3) Evidence-gate before acting - Prefer multi-signal requirements: floors (min counts), sparsity (blank-on-some-rows), and contextual repetition across pages instead of single-instance acceptance. 4) Make classification value-driven, not structure-driven - Don’t let headers/labels be the classifier; decide from observed values (e.g., marker columns identified via sparse marker values, not header text). 5) Add a failing boundary regression test for each “veto” - For every guard you add (sentence-ending logic, hyphenation continuation exclusion, line-start marker shape, evidence thresholds), add a small test that would have corrupted output before the fix. - Use synthetic fixtures for ordering/producer-format issues so byte order/layout is controllable. Illustrative pattern (Rust-style): ```rust fn maybe_merge_with_evidence(items: &[Item], context: &Ctx) -> Option { // 1) Invariant + shape constraints if !context.invariant_holds(items) { return None; } // 2) Evidence gating (floors + sparsity / repeated-context) let evidence = context.evidence_score(items); if evidence < context.EVIDENCE_FLOOR { return None; } // 3) Finally apply transformation Some(context.apply_merge(items)) } ``` Outcome: fewer false positives (corruption) and fewer over-broad fixes, because heuristics only activate when their evidence is strong *and* their inputs match the invariant assumptions.