# How the Cross-Validation Function in AI Berkshire Ensures Data Consistency

> Discover how AI Berkshire's cross_validate function ensures data consistency. It uses Decimal types, median values, and tolerance thresholds to guarantee accuracy. Learn more in the repository.

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: how-to-guide
- Published: 2026-07-05

---

**The `cross_validate` routine in [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) guarantees data consistency by converting all inputs to high-precision Decimal types, calculating median-based reference values, and automatically flagging any source that deviates beyond a configurable tolerance threshold.**

The `xbtlin/ai-berkshire` repository implements a rigorous financial data validation pipeline designed to prevent silent divergence across multiple data sources. At the heart of this system lies the **cross-validation function in AI Berkshire**, which transforms manual spot-checking into an automated, reproducible code-driven process. This routine serves as a standard checkpoint in every research workflow, ensuring that critical metrics like revenue or market cap remain consistent regardless of their origin.

## High-Precision Decimal Standardization

The function eliminates floating-point arithmetic errors by immediately converting all incoming values to Python’s `Decimal` type.

This standardization occurs in [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) before any comparative analysis begins. An optional unit string (e.g., "亿" for hundreds of millions) is appended solely for human-readable output, while the underlying calculations maintain exact precision. This approach prevents the subtle rounding errors that plague binary-float representations, ensuring that deviation calculations remain deterministic across repeated executions.

## Statistical Robustness via Median Reference

Rather than using a simple average that could be skewed by outliers, the function derives a **reference value** by computing the median of all supplied sources.

From the `source_values` dictionary provided to the function, the median calculation creates a statistically robust baseline. This median-based approach ensures that a single anomalous data point from one source cannot distort the reference against which all other sources are measured.

## Automated Deviation Detection

The consistency check follows a strict mathematical protocol:

1. **Calculate deviation**: For each source, the function computes `abs(value - reference) / reference * 100`
2. **Apply tolerance**: A configurable threshold (defaulting to **2%**) determines acceptable variance
3. **Flag inconsistencies**: Any source exceeding the tolerance receives a "❗" marker in the output table

The function returns a Boolean indicating overall success—`True` only when all sources fall within the specified tolerance.

## Implementation in tools/financial_rigor.py

The core logic resides in [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py), which exposes both a Python API and a command-line interface.

```python
from tools.financial_rigor import cross_validate

# Validate revenue figures from multiple sources

field = "revenue"
values = {
    "年报": 7518,
    "Yahoo": 7500,
    "StockAnalysis": 7520
}
unit = "亿"

# Returns True if all sources within 2% tolerance

consistent = cross_validate(field, values, unit)

```

The deviation calculation implemented in the source code ensures that percentage differences are computed against the median reference, not an arbitrary baseline.

## Command-Line Integration and Workflow Automation

The repository integrates cross-validation directly into research workflows through a CLI wrapper that registers the `cross-validate` sub-command.

Research teams invoke validation through markdown files such as [`skills/investment-team.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-team.md) and [`skills/earnings-review.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/earnings-review.md) using standardized commands:

```bash
python3 tools/financial_rigor.py cross-validate \
    --field revenue \
    --values '{"年报": 7518, "Yahoo": 7500, "StockAnalysis": 7520}' \
    --unit 亿

```

For stricter validation requirements, the tolerance parameter accepts floating-point values:

```bash
python3 tools/financial_rigor.py cross-validate \
    --field eps \
    --values '{"Morningstar": 3.45, "Yahoo": 3.41, "GuruFocus": 3.44}' \
    --unit "" \
    --tolerance 1.0

```

Typical output displays a formatted table showing field name, source, raw value, deviation percentage, and status indicators:

```

Field: revenue (单位: 亿)
-------------------------------------------------
Source           Value   Deviation   Status
-------------------------------------------------
年报                7518      0.00%      ✅
Yahoo               7500      0.24%      ✅
StockAnalysis       7520      0.26%      ✅
-------------------------------------------------
All values within 2.0% tolerance.

```

## Audit Trail and Reproducibility

The architecture ensures long-term data integrity through deterministic re-execution capabilities.

Because the routine relies on Decimal arithmetic and fixed tolerance parameters, subsequent re-runs produce identical results, creating a reproducible audit trail. The validation output is routinely captured in research reports within the `reports/*` directory (e.g., `reports/拼多多/拼多多-research-20260702.md`), then later re-checked by [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py) to produce final audit verdicts.

This integration between [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) and [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py) transforms cross-validation from a one-time check into a persistent accountability mechanism.

## Summary

- **High-precision arithmetic**: The `cross_validate` function converts all inputs to `Decimal` types to eliminate floating-point errors
- **Robust statistical baseline**: Uses median calculation from `source_values` to establish outlier-resistant reference points
- **Automated enforcement**: Applies configurable tolerance thresholds (default 2%) to flag inconsistent sources with "❗" markers
- **Workflow integration**: Accessible via CLI at `tools/financial_rigor.py cross-validate` and invoked from skill files like [`skills/investment-team.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-team.md)
- **Deterministic auditing**: Decimal arithmetic and fixed tolerances ensure reproducible results for [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py) verification

## Frequently Asked Questions

### What is the default tolerance threshold in AI Berkshire's cross-validation function?

The default tolerance is **2%**, meaning any data source deviating more than 2% from the median reference value is flagged as inconsistent. This threshold is configurable via the `--tolerance` parameter when using the command-line interface or the `tolerance` argument when calling the Python function directly.

### How does the cross-validation function handle different units or scales?

The function accepts an optional `unit` parameter that appends a string label (such as "亿" for hundreds of millions) to the output for readability. However, all mathematical comparisons occur after converting values to `Decimal` types, ensuring that scale differences are normalized before deviation calculations begin.

### Where is the cross-validation output stored for audit purposes?

Validation results are captured in research reports within the `reports/*` directory structure (e.g., `reports/拼多多/拼多多-research-20260702.md`). These outputs are subsequently consumed by [`tools/report_audit.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/report_audit.py), which re-checks the validation logs to produce final audit verdicts, creating a complete accountability chain from data collection to final report approval.

### Can the cross-validation function be used outside of the standard research workflow?

Yes. While the function is designed to integrate with markdown-based workflows in [`skills/investment-team.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/investment-team.md) and [`skills/earnings-review.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/earnings-review.md), the `cross_validate` routine in [`tools/financial_rigor.py`](https://github.com/xbtlin/ai-berkshire/blob/main/tools/financial_rigor.py) exposes a clean Python API suitable for custom scripts. Import the function directly to validate data in ad-hoc analyses or third-party data pipelines while maintaining the same consistency guarantees.