Building Reliable Agents for Financial Documents

# Illustrative sketch (not from the paper)
import torch
import torch.nn as nn
import torch.optim as optim

# Core FinixDoc-VL model (vision-language backbone)
class FinixDocVL(nn.Module):
    def __init__(self):
        super().__init__()
        self.backbone = nn.Linear(1024, 512)  # placeholder

    def forward(self, image, text):
        # combine visual and textual embeddings
        img_feat = self.backbone(image)
        txt_feat = self.backbone(text)
        return img_feat, txt_feat

# Homoglyph-aware contrastive loss (simplified)
def homoglyph_contrastive_loss(img_feat, txt_feat):
    # placeholder: cosine similarity loss
    return 1 - torch.nn.functional.cosine_similarity(img_feat, txt_feat).mean()

# Composite reward for RL stage (placeholder)
def composite_reward(pred, gt):
    # combine domain-specific metrics (e.g., table accuracy, layout consistency)
    return torch.tensor(1.0)  # dummy reward

model = FinixDocVL()
optimizer = optim.Adam(model.parameters(), lr=1e-4)

def train_step(image_batch, text_batch, gt):
    img_feat, txt_feat = model(image_batch, text_batch)
    loss = homoglyph_contrastive_loss(img_feat, txt_feat)
    # RL refinement (mock)
    reward = composite_reward(img_feat, gt)
    loss = loss - 0.01 * reward  # policy gradient style update
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    return loss.item()

# Split‑then‑merge preprocessing for ultra‑large pages
def split_then_merge(document):
    # partition document into regions (placeholder)
    regions = [document[i:i+1000] for i in range(0, len(document), 1000)]
    # parallel inference (mock)
    results = [model(region, region) for region in regions]
    # merge results (placeholder)
    return results

# Human‑in‑the‑loop data factory (simplified)
def data_factory(raw_doc):
    # heterogeneous inference (e.g., OCR + FinixDoc-VL)
    ocr_output = raw_doc  # placeholder
    finix_output = model(ocr_output, ocr_output)
    # model‑based refinement (Kimi‑K2.5 placeholder)
    refined = finix_output
    # deterministic validation (e.g., schema check)
    assert refined is not None
    return refined

// Illustrative sketch (not from the paper)
const torch = require('torch-js'); // placeholder for torch in Node

// Core FinixDoc-VL model (vision-language backbone)
class FinixDocVL {
  constructor() {
    this.backbone = (x) => x; // identity placeholder
  }
  forward(image, text) {
    const imgFeat = this.backbone(image);
    const txtFeat = this.backbone(text);
    return { imgFeat, txtFeat };
  }
}

// Homoglyph-aware contrastive loss (simplified)
function homoglyphContrastiveLoss(imgFeat, txtFeat) {
  // placeholder cosine similarity loss
  return 1 - imgFeat.dot(txtFeat) / (imgFeat.norm() * txtFeat.norm());
}

// Composite reward for RL stage (placeholder)
function compositeReward(pred, gt) {
  // combine domain-specific metrics
  return 1.0; // dummy reward
}

const model = new FinixDocVL();
const optimizer = { step: () => {}, zeroGrad: () => {} }; // mock optimizer

function trainStep(imageBatch, textBatch, gt) {
  const { imgFeat, txtFeat } = model.forward(imageBatch, textBatch);
  let loss = homoglyphContrastiveLoss(imgFeat, txtFeat);
  const reward = compositeReward(imgFeat, gt);
  loss = loss - 0.01 * reward; // policy-gradient style
  optimizer.zeroGrad();
  // loss.backward(); // placeholder
  optimizer.step();
  return loss;
}

// Split‑then‑merge preprocessing for ultra‑large pages
function splitThenMerge(document) {
  const regions = [];
  for (let i = 0; i < document.length; i += 1000) {
    regions.push(document.slice(i, i + 1000));
  }
  const results = regions.map(r => model.forward(r, r));
  // merge results (placeholder)
  return results;
}

// Human‑in‑the‑loop data factory (simplified)
function dataFactory(rawDoc) {
  const ocrOutput = rawDoc; // placeholder for OCR
  const finixOutput = model.forward(ocrOutput, ocrOutput);
  const refined = finixOutput; // Kimi‑K2.5 placeholder
  if (!refined) throw new Error('Validation failed');
  return refined;
}