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Built something I wished existed earlier: universal_uq is a model-agnostic uncertainty quantification wrapper focused on advanced model families, especially generative systems.
You can wrap your model, choose compatible uncertainty methods, run inference, and get theory-backed:
epistemic uncertainty
aleatoric uncertainty
OOD / novelty uncertainty
calibration
a deployment-facing aggregate score
diagnosis of why uncertainty is high
counterfactual suggestions for how to reduce it
The main idea I focused around is most UQ tools are fragmented by task family, but in reality, we work across model types. So I wanted one wrapper and one dashboard that could work across:
LLMs
foundation / multimodal models
generative models
forecasting models
graph models
RL policies
tabular classifiers/regressors
One contribution I’m especially excited about is Foundation Uncertainty Score (FUS), a wrapper-level unified uncertainty metric.
Such metric is essential because uncertainty quantification methods are scattered across: entropy-based, disagreement-based, novelty-based, calibration-like. FUS gives a common operating score across model families.
Important caveat:
FUS is:
wrapper-level
model type-dependent
normalization-sensitive
useful as a unified operating score, not yet a benchmark-calibrated standard
also added:
1. recent UQ methods like semantic_density, uot_plan, and conformal forecasting variants
2. an uncertainty investigator that asks:why is uncertainty high here?
3. what information would reduce it?
4. what data should we collect?
5. counterfactual reruns to rank uncertainty-reduction suggestions by measured effects
6. latent uncertainty cartography for generative models
a research dashboard with method-based plots and deployment decisions
My big goal was more than computing uncertainty:
“help someone understand whether to trust this model, why not, and what to do next”
Repo:
github.com/apooja1/univer…
If you work on LLM evals, foundation models, safety, calibration, or deployment, I’d love to hear what uncertainty views you wish existed.
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