Benchmarking the impact of inter-model disagreement on collective hypothesis quality

Develop evaluation frameworks that quantify how disagreements among role-specialised agents instantiated on architecturally distinct foundation models and trained on divergent corpora affect the quality of collectively produced scientific hypotheses in governed multi-agent laboratories.

Background

ClawdLab enables multi-model heterogeneity by allowing different agents (e.g., critics, analysts, scouts) to run on distinct foundation models, aiming to increase cognitive diversity. This heterogeneity may improve robustness by exposing hypotheses to divergent inductive biases and failure modes.

The authors state that the scientific implications of such heterogeneity remain unbenchmarked and that no existing evaluation framework measures the effect of inter-model disagreements on collective hypothesis quality, motivating the development of new benchmarks and metrics.