Reconcile Conflicting Corporate Filings with Mathematical Precision

Extract atomic, machine-verifiable facts with rigorous 6-dimensional context envelopes (temporal scope, Ind-AS accounting standard, standalone vs. consolidated). Adjudicate contradictions across annual reports, presentations, and prospectus filings with verifiable evidence citations.

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Theoretical Foundation & Architecture

Beyond Naive Chunk-and-Retrieve RAG

Standard RAG systems delay reasoning until query time, producing hallucinations when filings contain apparent contradictions. FLAE is built upon Automated Knowledge Base Construction (AKBC) and the Extended FEVER paradigm, resolving conflicts during ingestion.

Standard Chunk & Retrieve RAGProbabilistic

•Blind Text Chunks: Slices documents into arbitrary token windows without recognizing financial table structures or reporting perimeters.
•Binary FEVER Failure: Classic NLI models treat Q1 vs FY or Standalone vs Consolidated numbers as irreconcilable refutations.
•Zero Lineage Ledger: Outputs synthetic generated summaries with no permanent audit trail or immutable evidence verification.

FLAE Epistemic Fact Ledger (AKBC)Deterministic

✓Structured Ingestion Pipeline: Normalizes claims into atomic tuples (Subject, Metric, Value, ContextEnvelope, Evidence) at ingestion time.
✓Extended FEVER Paradigm: 6-D context envelopes prevent false contradictions by accounting for temporal periods, accounting rules, and organizational scope.
✓Data Fusion & LLM-as-a-Judge: The Arbiter agent clusters candidate pairs via bi-encoder blocking and generates formal reasoning proofs.

Engineered for Statutory Rigor & Auditability

FLAE treats financial filings as observer-bound, timeline-bound knowledge claims rather than universal strings.

1. Epistemic Knowledge

Handles the philosophy of how we know what is true. Instead of treating text as absolute facts, every claim is formally scoped to its observer, fiscal period, and reporting boundary.

2. Fact-Ledger

A single, append-only, structured ledger that acts as the global source of truth. Every claim is permanently stamped with absolute provenance: Document ID, Page Number, and verbatim excerpt.

3. The Arbiter Agent

A deterministic LLM-as-a-Judge role acting as judge, jury, and classifier. It performs multi-attribute data fusion, evaluating whether incoming claims corroborate, contradict, or reconcile.

From Raw PDF to Verified Arbitration Graph

Every ingested document passes through four automated stages before entering the immutable audit ledger.

STAGE 01PDF Ingestion

Parser & Coordinate Tracking

Extracts financial disclosures, structured balance sheets, and narrative footnotes with page-level bounding.

STAGE 02Information Extraction

Atomic Fact Schemas

Deconstructs text into canonical tuples with numerical values, units, and 6-D context envelopes.

STAGE 03Record Linkage

Candidate Clustering

Uses bi-encoder vector similarity in ChromaDB to block and cluster related claims, avoiding O(N²) scaling.

STAGE 04LLM-as-a-Judge

Epistemic Arbitration

The Arbiter adjudicates disputes, classifies relationships, and writes persistent step-by-step reasoning proofs.

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The Three Epistemic Resolutions

How the Arbiter resolves pairwise claims across corporate filings.

Corroborated

Claims state identical numbers and assertions under matching context scopes. Verified by dual verbatim citations across separate filings.

Contradicted

Claims describe the exact same entity, period, and accounting standard, but assert irreconcilably conflicting numbers or qualitative states.

Context Reconciled

Apparent discrepancies (e.g. ₹74,540M vs ₹81,415M) are proven mathematically valid once organizational boundary or temporal qualifier divergence is identified.

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