AVA

A tool for structured validation and analysis of research and AI-generated outputs.

What it does

AVA supports the evaluation of structured outputs by applying predefined checks and decomposed assessment steps. It produces summaries that highlight inconsistencies, gaps, or points requiring further interpretation. The emphasis is on making evaluation explicit and inspectable rather than implicit or holistic.

Inputs and outputs

Input
Structured or semi-structured data, such as:

• publication metadata
• query results
• model-generated outputs

Output
Structured validation summaries, which may include:

• flagged inconsistencies
• dimension-based assessments
• aggregated observations across inputs

How it is used

Use follows a simple sequence:

1. Provide input data or outputs to be assessed
2. Select or define the validation context
3. Review the structured output produced by the tool

The internal evaluation process is not exposed.

Example

Input
A set of publications with associated metadata and derived indicators.

Output
A structured summary identifying:

• discrepancies between metadata fields
• uneven distribution across dimensions
• outputs requiring closer inspection

This example is illustrative and does not reflect full implementation.

Scope and limits

AVA supports validation and structured assessment but does not replace interpretation. Results depend on the quality and structure of the input data. The tool highlights patterns and inconsistencies but does not resolve them.

Access and status

AVA is under active development.
Access is limited.

Related work

AVA operates alongside frameworks such as GRID+, where evaluation is decomposed into explicit dimensions, and connects to broader work on structured assessment and validation in research systems.

Depth

• overview: available
• methodological detail: partial
• full protocol and implementation: not public