CFO-HRM Mindweave reconciliation checkpoint

This is the promoted shadow-mode learning checkpoint for CFO-HRM, a native-PyTorch hierarchical recurrent model that scores candidate links between bank transactions and general-ledger cash lines and classifies bank-row exceptions.

It is not a general-purpose language model. It accepts the fixed structured tensors produced by the CFO-HRM data pipeline. It does not read arbitrary natural-language prompts, approve accounting decisions, or post journal entries.

Intended use

This checkpoint is intended for:

• local research and education about hierarchical reasoning on structured accounting tasks;
• reproducing the reported synthetic bank-to-GL reconciliation experiment;
• shadow-mode candidate scoring, followed by deterministic accounting controls and human review; and
• studying confidence thresholds, abstention, exception routing, and robustness to missing evidence.

It is not intended for autonomous reconciliation, journal-entry creation, approval, ERP posting, financial reporting, audit evidence, commercial use, or decisions involving real funds. Do not use a prediction as an accounting conclusion.

Model details

• Framework: native PyTorch
• Trainable parameters: 1,665,352
• Input capacity: up to 24 bank rows and 24 GL rows per case
• Hidden size: 128
• Attention heads: 4
• High-level cycles/layers: 2 / 1
• Low-level cycles/layers: 2 / 1
• Deep-supervision segments at evaluation: 4
• Exception classes: 6
• Continuous features per row: 3
• Categorical fields per row: 6
• Selected fine-tuning step: 50
• Checkpoint file: best.pt
• Checkpoint SHA-256: 65049fa087ff6e4fa2fc28262c237ab7b4987b25cc292104bdf7b78b047662e9

The model embeds categorical and continuous bank/GL features into a shared sequence. Recurrent high-level and low-level reasoning modules repeatedly exchange state. Pair heads score candidate bank-to-GL links, an unmatched head scores the unmatched option, and an exception head classifies each bank row. The model itself does not enforce accounting policy. CFO-HRM applies a one-to-one assignment decoder and then performs entity, currency, amount, date, confidence, uniqueness, and source-lineage checks outside the network.

This implementation is inspired by the Hierarchical Reasoning Model research architecture, but is a small purpose-built reconciliation model rather than an official implementation or checkpoint from that work.

Training data

Fine-tuning used two synthetic Mindweave datasets:

Dataset	Pinned revision	License
mindweave/bank-transactions-us	290e657395a1ce25837642b8309ca9a741f85303	CC BY-NC 4.0
mindweave/accounting-ledger-us	b2f2d0890353438b4473f2c3b2d2b5e15a5132a0	CC BY-NC 4.0

The checkpoint was initialized from a locally synthetic-pretrained CFO-HRM checkpoint at step 900. Robust fine-tuning ran for 150 outer steps with four deep-supervision segments, a batch size of 16, a learning rate of 5e-5, and 25% synthetic retention batches. External training rows used independent categorical dropout of 0.25 for reference, 0.10 for description, and 0.05 for source module. Validation selected step 50.

Labels for positive pairs were derived only from unique (company_id, source_module, source_id) journal-entry provenance and the bank account's mapped cash GL line. Twenty-four narrowly identified source-less monthly fees were labeled unmatched. Ambiguous provenance is retained for review and excluded from match supervision rather than silently treated as unmatched.

Bank and GL rows were independently and deterministically shuffled within each case. This reduced same-position targets from 98.6% to 4.1% and removed the near-diagonal positional shortcut present in the source ordering.

Chronological split

Split	Period	Cases	Bank rows	Matched rows	Bank fees
Train	2024-01 through 2025-06	92	2,021	2,003	18
Validation	2025-07 through 2025-09	23	509	506	3
Test	2025-10 through 2025-12	30	702	699	3

The raw third-party datasets are not included in this model repository. They remain attribution-required, non-commercial CC BY-NC 4.0 data. Download them from the upstream dataset repositories at the pinned revisions and review their terms before use. Do not infer that this checkpoint or its training data is cleared for commercial use.

Evaluation

The following values were measured on the held-out October–December 2025 test split. Selective metrics use a confidence threshold of 0.90.

Raw test split

System	Assignment accuracy	Match F1	Exception accuracy	Bank-fee recall	ECE	Auto-match coverage	False auto-match rate
Synthetic-pretrained HRM	99.43%	99.43%	7.41%	100%	14.76%	52.99%	0%
Deterministic evidence baseline	100%	100%	99.57%	0%	0.83%	99.57%	0%
This checkpoint	100%	100%	100%	100%	7.95%	83.90%	0%

Additional measured values for this checkpoint on the raw split:

• match precision and recall: 100% / 100%;
• exact-case accuracy: 100%;
• dollar-weighted accuracy: 100%;
• selective assignment accuracy: 100%;
• selective coverage: 84.33%;
• selective dollar coverage: 83.40%;
• abstention rate: 15.67%; and
• assignment Brier score: 3.83%.

The deterministic baseline's perfect matching result is a central caveat. This synthetic test set is largely solvable through exact amount, date, and provenance evidence. These results do not establish a matching advantage for HRM. The measured improvement over that baseline is exception classification, especially recognizing the three held-out bank fees.

Missing-evidence stress tests

Test	Assignment accuracy	Match F1	Exception accuracy	Bank-fee recall	ECE	Selective coverage	Auto-match coverage	False auto-match rate
Reference masked	95.4416%	95.4220%	99.5726%	33.3333%	5.2496%	80.0570%	79.6296%	0%
Source module, reference, and description masked	96.2963%	96.2804%	87.7493%	0%	11.2771%	48.2906%	47.8632%	0%

For comparison, the deterministic baseline with references masked achieved 99.1453% assignment accuracy and 99.1416% match F1, with 0% bank-fee recall and 62.9630% selective coverage. With source module, reference, and description all masked, it retained 99.1453% assignment accuracy and 99.1416% match F1 but abstained on 99.5726% of rows: selective coverage was only 0.4274%, auto-match coverage was 0%, and bank-fee recall was 0%.

On the fixed synthetic retention split, this checkpoint achieved 95.71% match F1 and 34.22% exception macro recall. The starting checkpoint achieved 95.41% and 37.86%, respectively.

Local use

The checkpoint requires the CFO-HRM source package and the two pinned upstream datasets. It is not compatible with the Hugging Face pipeline() API.

git clone https://github.com/Aznatkoiny/cfo-hrm.git
cd cfo-hrm
uv sync --extra dev

# Download this model repository separately, then verify the checkpoint.
sha256sum /path/to/model-repository/best.pt

The expected digest is:

65049fa087ff6e4fa2fc28262c237ab7b4987b25cc292104bdf7b78b047662e9

Place the upstream CSV files under the paths shown below, preserving the dataset layouts, and evaluate:

uv run cfo-hrm-eval-mindweave \
  /path/to/model-repository/best.pt \
  --bank-dir data/raw/mindweave/bank-transactions-us \
  --ledger-dir data/raw/mindweave/accounting-ledger-us \
  --split test \
  --device cuda \
  --confidence-threshold 0.9 \
  --output evaluation.json

For CPU-only evaluation, use --device cpu --no-amp.

To produce a versioned review request for the separate control layer:

uv run cfo-hrm-export-review \
  /path/to/model-repository/best.pt \
  --bank-dir data/raw/mindweave/bank-transactions-us \
  --ledger-dir data/raw/mindweave/accounting-ledger-us \
  --split test \
  --case-index 8 \
  --device cuda \
  --output review-request.json

The Go orchestrator must then validate that proposal. In the supplied end-to-end example, it routed 18 control-passing matches and one unmatched fee to exception_review, set requires_human_review: true, and set posting_authorized: false.

Safety boundary

Use this model only as one component in a controlled review workflow:

1. Treat model outputs as proposals, never approvals.
2. Apply deterministic accounting controls independently of the model.
3. Abstain and route low-confidence, unmatched, or policy-sensitive rows to a human reviewer.
4. Preserve source hashes, checkpoint hashes, record IDs, control results, and review decisions in a tamper-evident audit trail.
5. Keep ERP posting and approval credentials outside the model process.

The accompanying CFO-HRM Go control layer deliberately sets posting_authorized: false. The model repository contains no ERP posting client and must not be extended to bypass segregation of duties or human approval.

Limitations

• All external training and test records are synthetic.
• The test split contains only three bank fees and no unresolved rows, so the reported exception percentages have high variance.
• Exact identifiers and clean provenance make the raw matching task unusually easy.
• The model degrades materially when contextual evidence is removed, especially on fee recall.
• It was not evaluated on split payments, aggregate payments, many-to-many reconciliation, substantial reference corruption, GL-only exceptions, real ERP exports, other countries, other accounting standards, or adversarial inputs.
• Maximum case size is fixed at 24 bank rows and 24 GL rows.
• Confidence scores are not guarantees; calibration can shift on any new company, system, period, or data-generating process.
• This checkpoint cannot replace professional judgment, internal controls, accounting policy, audit procedures, or regulatory compliance.

Before any real-data pilot, perform company-specific data mapping, privacy and security review, out-of-period evaluation, calibration, control testing, reviewer acceptance testing, and comparison with deterministic baselines. Keep the system in shadow mode until those gates are independently satisfied.

License and attribution

The CFO-HRM source code is Apache-2.0. This checkpoint was fine-tuned on CC BY-NC 4.0 third-party datasets and is therefore published under CC BY-NC 4.0 as the conservative model-artifact license. This does not broaden the upstream permissions or constitute legal advice. Attribute the two Mindweave dataset repositories, retain their license notices, and do not use the checkpoint or data commercially without establishing the necessary rights.