Perception, Layout, and Validation: Calibrated Confidence for Reliable Straight-Through Processing of Financial Documents
A decomposed perception-layout-validation confidence score with conformal risk control lets VLMs auto-approve 49-72% of financial document fields at target error.
The paper proposes a decomposed confidence layer over VLM-extracted financial document fields using perception, layout, and validation channels plus conformal risk control for reliable straight-through processing. Tested on invoices and ad-buy forms with Qwen3.6-27B and Gemini-3.1-Flash-Lite, AUROC improves from 0.54-0.74 to 0.90-0.99. The method auto-approves 49-72% of fields at sub-10% empirical error versus only 0.1-7.0% for native VLM verbalized confidence.
- Decomposed score spans perception, layout, and validation channels with final conformal risk control
- Validated on three public datasets using Qwen3.6-27B and Gemini-3.1-Flash-Lite VLM families
- AUROC improves from 0.54-0.74 (verbalized confidence) to 0.90-0.99
- Auto-approves 49-72% of fields at target error under 10%, versus 0.1-7.0% natively
- Tags
- confidence-calibrationconformal-risk-controldocument-processingfinancestraight-through-processingvlm
- AI models
- Qwen3.6-27BGemini-3.1-Flash-Lite
From the original article
1 min read at arxiv.orgStraight-through processing (STP) on extracted key-value fields from financial documents without human review requires a calibrated probability together with a bounded guarantee on the residual error of the auto-approved tier.
By Yichao Jin · arxiv.org
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