§ 6.1
Research Question
Can intelligent document processing be formalised as a composition of typed, independently evaluable modules whose interaction yields auditable, restriction-aware decision support at enterprise scale? The question is intentionally phrased in compositional terms: we are interested not in the absolute capability of any individual extractor, validator or constraint engine, but in the structural properties that emerge when such components are wired together under a typed interface contract and evaluated against an end-to-end fitness function.
§ 6.2
Hypothesis
We hypothesise that a modular pipeline composed of document intelligence (DOCBOT), cross-source validation (SYSTEMBOT) and symbolic restriction evaluation (RESTRICTIONBOT) attains a strictly superior point in the reliability–auditability frontier than monolithic baselines of comparable parameter budget, while preserving throughput within 15% of an equivalently sized end-to-end neural extractor. The hypothesis is operationalised through a pre-registered ablation protocol detailed in §7.
§ 6.3
Objectives
(O₁) Specify the framework formally as a typed dataflow graph with stage-level contracts. (O₂) Implement reference modules under those contracts. (O₃) Evaluate accuracy, latency, throughput and auditability against a synthetic but representative enterprise corpus. (O₄) Characterise failure modes through fault injection at every stage boundary. (O₅) Release reproducible artefacts — corpora, configurations and evaluation harness — under an open scientific licence.
§ 6.4
Workflow
Document acquisition → schema-bound extraction → cross-source consistency analysis → symbolic restriction evaluation → decision-support emission with full provenance. Each arrow is a typed channel; each node a pure function over its declared input envelope. Side-effects (caching, source lookups, telemetry) are isolated in effect-aware wrappers that preserve the purity of the underlying transformation.
§ 6.5
Pipeline
Typed, unidirectional, with explicit boundaries between stages. Each stage is a total function over its declared input type, returning a typed result envelope whose disjoint sum encodes success, partial success and recoverable failure. The dataflow graph is intentionally acyclic; iteration is expressed through provenance-annotated reprocessing rather than back-edges, preserving the algebraic properties required for compositional reasoning.
§ 6.6
Validation Strategy
Three nested evaluation loops are exercised in sequence. (V₁) Per-stage unit evaluation under controlled distributions, isolating extraction error from validation error. (V₂) End-to-end pipeline evaluation on held-out corpora drawn from the operating envelope of the target deployment. (V₃) Module ablation, in which each agent is independently disabled to quantify its marginal contribution to the end-to-end metric of interest. Statistical significance is reported at α = 0.01 with bootstrap-derived confidence intervals.
§ 6.7
Error Handling
Partial failure is modelled as a first-class citizen of the type system rather than as exceptional control flow. Each stage returns a typed result envelope distinguishing success, partial success, and recoverable failure; uncoverable failures are routed to a quarantine bucket whose contents are surfaced through the same observability surface as the canonical decision stream, guaranteeing that no document silently disappears from the audit trail.
§ 6.8
Architecture Description
Three cooperating agents over a typed pipeline orchestrator. Provenance is propagated as a first-class artefact through every stage boundary, materialised as an immutable DAG whose nodes carry cryptographic hashes of the contributing evidence. The architecture is described through five complementary views (orchestrator graph, high-level pipeline, component decomposition, data-flow algebra, validation topology) constituting a 4+1 style description.
§ 6.9
Processing Logic
Stateless transformations are preferred wherever the semantics permit; stateful effects (cache lookups, source resolution, telemetry emission) are encapsulated in observable wrappers exposing explicit retry, time-out and back-pressure controls. This separation of pure logic from effectful boundary allows the entire framework to be re-evaluated deterministically against any persisted evidence snapshot.
§ 6.10
Cross-Source Validation
Validation is formulated as an agreement functional a(R, E) over the extracted record R and an indexed family of independent evidence sources E = {S₁, …, Sₙ} weighted by an empirically calibrated reliability prior. The functional returns both a scalar confidence and a provenance subgraph that records every contributing source, transformation, and arbitration decision, supporting closed-form replay against the original evidence snapshot.
§ 6.11
Restriction Analysis
Operational restrictions are encoded as a declarative constraint set C and evaluated symbolically against the validated record R; violations are surfaced with machine-readable rationale and natural-language justification. The constraint system is monotone in C — adding a restriction never converts a reject into an approve — which guarantees that constraint catalogues can be extended without invalidating prior decisions, a property required for stable longitudinal audit.
§ 6.12
Reproducibility Protocol
Every reported figure is paired with a deterministic seed, an immutable corpus snapshot identifier, a stage-level configuration manifest, and a content-addressed hash of the evaluation harness. Re-execution against the same triple (seed, corpus, manifest) is required to yield bit-identical artefacts; deviations are themselves treated as findings and are investigated as candidate determinism faults rather than discarded as noise. All artefacts are released under an open scientific licence in alignment with ACM and IEEE reproducibility badging criteria.
