Datasets:
File size: 3,736 Bytes
56d98e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | # FilingsBench Controls v0.1 candidate report
Run date: 2026-07-27. This report covers a narrative-controls candidate slice, not the original
60-filing financial-facts benchmark.
## Result
The deterministic baseline matched all 48 candidate field outcomes across 12 issuer-disjoint SEC
filings. This includes six expected abstentions: the two 10-Qs do not state annual ICFR conclusions
or support material-weakness status, but both do state disclosure-controls effectiveness.
| Field | Records | Present labels | Abstained labels | Exact outcomes | Evidence on present predictions |
| --- | ---: | ---: | ---: | ---: | ---: |
| ICFR effectiveness | 12 | 10 | 2 | 12/12 | 10/10 |
| Disclosure-controls effectiveness | 12 | 12 | 0 | 12/12 | 12/12 |
| Material weakness disclosed | 12 | 10 | 2 | 12/12 | 10/10 |
| Remediation status | 12 | 10 | 2 | 12/12 | 10/10 |
Median parser latency was 2,292.04 ms; mean was 2,766.30 ms; maximum was 4,635.59 ms. Median peak
Python memory was 24.32 MB and maximum was 74.30 MB. Inference cost was $0. Network transfer is not
included in per-document parser latency. The observed end-to-end command took 34.6 seconds on the
audit machine.
## Dataset and method
The slice contains ten 10-Ks and two 10-Qs from 12 issuers, split 8/2/2 with no issuer overlap.
Annual records balance five effective and five ineffective ICFR conclusions. The deterministic
system retrieves Item 9A or Part I, Item 4, distinguishes ICFR from disclosure controls, returns
exact normalized-document excerpts and offsets, and abstains when the section does not support a
value.
The consistency inference from an effective ICFR conclusion to no current material weakness is
explicit in prediction provenance. It follows SEC staff guidance that management may not conclude
ICFR is effective when one or more material weaknesses exist. SEC staff also says an unremediated
material weakness requires an ineffective conclusion and encourages disclosure of its nature,
impact, and remediation plans. Sources: [SEC ICFR FAQ](https://www.sec.gov/oca/controlfaq1004),
[SEC staff statement](https://www.sec.gov/info/accountants/stafficreporting.htm), and
[SEC Financial Reporting Manual](https://www.sec.gov/about/divisions-offices/division-corporation-finance/financial-reporting-manual/frm-topic-4).
## Reproducibility
- dataset SHA-256: `9de0c23ed5e971d9426b7d235b52332fb13c257829d4a09743641eef213717de`
- predictions SHA-256: `caeceffb0e2a2918e6db18dc4ccd4d1c6ecd33bcee1f1dbee811fc5c417a7a7d`
- summary SHA-256: `e6ea9f86da89414fb7a01aada3cee717505829f9f454130030b4c9371db0436f`
- Python 3.11.15; Windows 11 Home 10.0.26200
- Intel Core i5-1135G7, 4 cores / 8 logical processors; 16.95 GB physical RAM
Commands:
```bash
finstruct controls-dataset build
finstruct controls-benchmark run data/filingsbench_controls_v0.1/records.jsonl \
--output results/controls-v0.1.0
```
## Limitations and model gate
Every record was deliberately selected to exercise a known label state, so this is not a prevalence
sample. The labels were manually read from filed primary HTML but have not yet received independent
double annotation or adjudication; they remain `candidate_pending_independent_review`, not gold.
The slice is small, English-only, and excludes 8-Ks, amendments, foreign-issuer forms, OCR/PDF
filings, management/auditor disagreements, and multi-weakness summary generation.
Because the deterministic baseline has no residual errors on this candidate slice, an open-model or
A100 experiment is not justified yet. The gate reopens after independent review and a larger blind
adversarial set reveal a measured error surface that a model could improve.
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