Source Verify - AI Citation Checker Model
Citation Checker
by Neurometricš Document Intelligence
Source-Verify is a task-specific model that cross-references footnotes in a paper against a database to ensure they are valid.
Source Verify is designed for Document Intelligence workflows where speed, consistency, and control matter more than generic creativity. Instead of relying on a broad model to guess intent each time, this task-specific SLM is optimized for citation checker and predictable output quality. That makes it easier to adopt in production pipelines where teams need reliable formatting, lower latency, and reduced hallucination risk.
Most teams integrate CRM records, internal docs, and workflow tools to automate repetitive language-heavy tasks with predictable outputs. This makes deployment practical for sales ops, customer success, legal, and operations teams that need answers grounded in their own data. Because the model is small and focused, teams can run it with efficient infrastructure while still meeting quality targets for the use case.
Compared with general-purpose models, focused SLMs like this one typically deliver better precision for narrow tasks, lower cost per request, and easier governance. You can start with the free tier, test the model against your business data, and scale usage through Neurometric APIs or ClawPack workflows when performance meets your benchmark.
Model: Qwen/Qwen3-4B-Instruct-2507
This task-specific SLM uses Qwen/Qwen3-4B-Instruct-2507 with a specialized system prompt. The model weights are identical to the base Qwen model ā the task specialization comes from the system prompt and has been validated through our testing pipeline. Need better accuracy? We can fine-tune the model on your data for your specific use case.
Test Results
Example Input
Policy: Require Doi: true Paper Id: P-2025-221 Footnotes: - Locator: p. 3; Note Id: 1; Citation Key: Ng2016 - Locator: sec. 2.1; Note Id: 2; Citation Key: Khan2020 - Locator: p. 19; Note Id: 3; Citation Key: Ng2016 Reference Db: Ng2016: Doi: 10.1145/0000000.0000001 Year: 2016 Title: Deep Learning: A Practitioner's Guide Khan2020: Year: 2020 Title: Reproducible Research Workflows Zhang2019: Doi: 10.1109/5.771073 Year: 2019 Title: Optimization in High Dimensions
Expected Output
Validation Report:
Summary: Total: 3
Valid: 2
Invalid: 1
All Valid: false
Valid Footnotes: - 1
3
Invalid Footnotes: - Reason: DOI required by policy but missing in reference_db; Note Id: 2; Citation Key: Khan2020
Actual Model Response
We are given a paper with footnotes, a reference database, and a policy that requires DOIs for all citations. Our task is to validate each footnote by checking whether the referenced citation key exists in the database and whether it satisfies the policy (i.e., has a DOI if require_doi is True).
---
Step-by-step validation:
#### Given:
paper_id: P-2025-221 - footnotes:
{ 'note_id': 1, 'citation_key': 'Ng2016', 'locator': 'p. 3' }
{ 'note_id': 2, 'citation_key': 'Khan2020', 'locator': 'sec. 2.1' }
{ 'note_id': 3, 'citation_key': 'Ng2016', 'locator': 'p. 19' }
reference_db:
'Ng2016': title="Deep Learning: A Practitioner's Guide", year=2016, doi='10.1145/0000000.0000001'
'Khan2020': title="Reproducible Research Workflows", year=2020, doi=None
'Zhang2019': title="Optimization in High Dimensions", year=2019, doi='10.1109/5.771073'
policy: {'require_doi': True} ā All citations must have a valid DOI
---
Validation Rules:
Citation key must exist in the reference_db.
If `require_doi` is True, the entry must have a non-None DOI.
---
Check each footnote:
#### Footnote 1: Ng2016, locator: p. 3
Exists in reference_db ā
DOI: '10.1145/0000000.0000001' ā valid (non-None) ā
ā Valid
#### Footnote 2: Khan2020, locator: sec. 2.1
Exists in reference_db ā
DOI: None ā violates require_doi ā
ā Invalid
#### Footnote 3: Ng2016, locator: p. 19
Exists in reference_db ā
DOI: '10.1145/0000000.0000001' ā valid ā
ā Valid
---
Final Result:
ā Valid footnotes: 1 and 3 ā Invalid footnote: 2 (missing DOI)
---
Output (as requested):
```json
{
"paper_id": "P-2025-221",
"footnotes": [
{
"note_id": 1,
"citation_key": "Ng2016",
"locator": "p. 3",
"valid": true
},
{
"note_id": 2,
"citation_key": "Khan2020",
"locator": "sec. 2.1",
"valid": false,
"reason": "Missing DOI (require_doi is True)"
},
{
"note_id": 3,
"citation_key": "Ng2016",
"locator": "p. 19",
"valid": true
}
],
"policy": {
"require_doi": true
},
"summary": "2 out of 3 footnotes are valid. Footnote 2 is invalid due to missing DOI."
}
```
Capabilities
Related models
Frequently asked questions
Does Source Verify work with CRM systems?
Yes. Source Verify is built to work with CRM systems, internal docs, and CSV/JSON pipelines. Most teams connect existing APIs or scheduled exports and map records into prompt-ready JSON.
What data format does this model accept?
You can send plain text or structured JSON. For citation checker, teams usually pass normalized records with field names, context notes, and output constraints.
Why use a task-specific SLM for citation checker?
Task-specific SLMs are tuned for one business outcome, so they are often more consistent and cost-efficient than general models for repetitive production workflows.
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Need better performance?
We can fine-tune this model on your data for higher accuracy on your specific use case.