§ 6.1
Research Question
Can a society of autonomous agents built on generative optical models — agents that perceive, negotiate, and self-regulate rather than execute a fixed script — simultaneously dominate monolithic vision–language baselines on efficiency under contention, factual error rate, and audit-grade traceability? The question is deliberately tri-objective: any single axis is trivially optimisable in isolation, and the scientific interest lies exclusively in whether a point exists that is non-dominated on all three at once.
§ 6.2
Central Thesis
T. Autonomous multi-agent orchestration over generative OCR attains a jointly non-dominated point on the efficiency × error × auditability frontier that no monolithic end-to-end extractor of comparable compute budget reaches. The thesis decomposes into three falsifiable claims. (T₁ · Efficiency) Agent-level bottleneck detection with dynamic rerouting raises sustained throughput under contention relative to a statically scheduled pipeline of identical parameter budget. (T₂ · Error) Consensus arbitration across independent evidence sources measurably reduces factual error and hallucinated field values relative to single-decoder generative extraction at matched latency. (T₃ · Auditability) Provenance closure is preserved under agent autonomy: every emitted decision admits complete causal replay despite non-deterministic inter-agent negotiation. The composite claim is that T₁, T₂ and T₃ hold jointly, not merely severally.
§ 6.3
Bottleneck Taxonomy
Four bottleneck classes are measured explicitly and treated as the dependent variables of the study. (B₁ · Optical latency) Cost of generative decoding per page, reported at p50 / p95 / p99 with stage-level attribution, since generative OCR shifts the dominant cost from I/O to inference. (B₂ · Source divergence) Rework and arbitration cost incurred when independent sources disagree, measured as excess evidence lookups per contested field. (B₃ · Agent handoff) Queueing delay and message overhead at inter-agent boundaries, modelled as an M/M/c network and bounded against the Amdahl-serial fraction of the topology. (B₄ · Schema and rule drift) Longitudinal degradation as document distributions and restriction catalogues evolve, quantified as accuracy decay per evaluation window against a frozen baseline.
§ 6.4
Agent Communication Protocol
Agents exchange typed messages over an asynchronous channel algebra rather than through direct invocation. Four message classes are defined: CLAIM (an agent asserts a field value with calibrated confidence), CHALLENGE (a peer contests a claim and supplies counter-evidence), CONGESTION (an agent reports saturation, triggering re-routing or back-pressure upstream), and DISCOVERY (an agent surfaces an unmodelled pattern or candidate restriction for catalogue promotion). Negotiation terminates under a proven convergence bound: confidence is monotonically non-increasing across CHALLENGE rounds, so the protocol cannot cycle. This message algebra is what makes autonomy evaluable rather than anecdotal — every negotiation is a recorded, replayable artefact.
§ 6.5
Hypothesis
We hypothesise that a multi-agent pipeline composed of generative optical perception (OPTIC), consensus arbitration (NEXUS) and symbolic restriction evaluation (AEGIS) 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.
§ 6.6
Objectives
(O₁) Specify the framework formally as a typed agent network with per-agent contracts and a convergent message algebra. (O₂) Implement reference agents under those contracts on a generative optical backbone. (O₃) Evaluate accuracy, hallucination rate, latency, throughput and auditability against a synthetic but representative enterprise corpus. (O₄) Characterise the four bottleneck classes B₁–B₄ through fault and load injection at every agent boundary. (O₅) Release reproducible artefacts — corpora, configurations and evaluation harness — under an open scientific licence.
§ 6.7
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.
§ 6.8
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.
§ 6.9
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.
§ 6.10
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.
§ 6.11
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.
§ 6.12
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.
§ 6.13
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.
§ 6.14
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.
§ 6.15
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.
§ 6.16
Threats to Validity
Four threats are pre-registered. (Internal) Agent autonomy introduces non-determinism in negotiation order; we mitigate through seeded scheduling and report variance across ten repetitions rather than single runs. (Construct) Hallucination is not directly observable, so it is operationalised as a field value unsupported by any evidence source in the provenance closure — a conservative proxy that may under-count fluent but source-consistent errors. (External) The corpus is synthetic-but-representative; generalisation to jurisdictions with materially different document conventions is asserted only as a hypothesis for future work. (Statistical) With four bottleneck classes and three claims, multiple comparison inflates false-positive risk; all reported significance is Holm–Bonferroni corrected at α = 0.01.
