Probabilistic Modelling·7 min read
Probabilistic Capability Inference in High Dimensions
Evaluating large models across dense benchmark suites is computationally wasteful. By modelling capability as a latent Gaussian surface, we query evaluation points that maximize information gain.
The Capability Landscape as a Latent Field
Traditional LLM and agent evaluations treat benchmarks as static discrete scorecards. However, agent task difficulty exhibits strong structural correlations across dimensions like instruction depth, context length, and syntactic ambiguity.
We parameterize an agent's capability bounds θ as a posterior distribution over a continuous task latent space: P(θ | D). When testing a new checkpoint, the acquisition function chooses benchmarks that minimize expected posterior entropy.
- ›Eliminates 65% of redundant benchmark executions
- ›Recovers non-linear failure thresholds with tight confidence bounds
- ›Yields continuous skill decay contours rather than binary pass rates
“Testing models is an active inference problem, not a batch scorecard exercise.”