Task behavior
Define success, partial success, refusal, abstention, and unacceptable failure for the intended task.
Research area 01
Trustworthy AI evaluation
We study how to evaluate an AI system against its intended workflow, including behavior, uncertainty, provenance, failure modes, and the point where human review becomes necessary.
Research problem
A benchmark score is not a deployment decision.
AI evaluation becomes useful when it reflects the work a system is expected to perform. A model can score well on a broad benchmark while failing on the data, edge cases, interfaces, or review conditions that matter in a specific workflow.
InfluxWave treats evaluation as a system design problem. The object of study includes the model, prompts, tools, retrieval sources, data transformations, user interface, human reviewer, and operational response to failure.
Evaluation layers
Measure what the system actually does.
A credible evaluation should preserve both aggregate evidence and inspectable examples.
Evidence and uncertainty
Evaluate whether outputs are supported, whether uncertainty is visible, and whether a reviewer can trace important claims.
Operational resilience
Test changes in models, prompts, tools, data sources, and system configuration for regressions and new failure modes.
Research questions
Questions that guide this area.
- Which measurements predict whether a prototype is dependable in its intended workflow?
- How should evaluation datasets represent real operating conditions and costly edge cases?
- How can result provenance help distinguish a model error from a retrieval, tool, or interface error?
- When should a system abstain, escalate, or require human approval?
- How should teams compare system versions without hiding regressions behind one aggregate score?
Evidence boundary
This page defines a research direction. It does not claim a released benchmark, dataset, or validated product.
Connected work
Evaluation depends on infrastructure and system design.
Evaluation results become more useful when the full experiment record can be reconstructed and when deployment choices reflect the failures the evaluation reveals.