Cross-domain benchmarks reveal when coordinated AI agents improve scientific inference from partial evidence

2026-05-21

AI Summary

Scientific evidence is often scattered across heterogeneous, multi-source data, making it difficult for any single source to fully capture complex phenomena and thereby limiting the evaluation of collaborative AI agents. This work introduces the first cross-domain benchmark to systematically assess the ability of collaborative AI systems to reason from partial evidence across four distinct tasks: molecular sonification, detection of scientific paradigm shifts, arboviral outbreak identification, and exoplanet validation. The study proposes three collaborative mechanisms—enhancing performance (e.g., achieving AUROC = 0.944 in disease prediction and AUROC = 0.955 in planet validation), improving interpretability, and refining representations—and explicitly validates their benefits through frozen evaluation panels, predefined protocols, rigorous baselines, and ablation studies. Results indicate that while collaboration does not universally improve performance, it consistently enhances traceability and representational capacity.

Abstract

Scientific evidence often spans instruments, databases, and disciplines, so no single source records the full phenomenon. This makes it difficult to determine when coordinated AI agents add value over simpler scientific workflows. We evaluate this question with a cross-domain benchmark spanning four scientific tasks: mapping molecular structure into musical representations, detecting historical paradigm shifts in science, identifying vector-borne disease emergence, and vetting transiting-exoplanet candidates. Each case uses a frozen evaluation panel, predefined scoring protocols, explicit baselines, ablations or null controls, and stated limitations. The results define three operating regimes. When different disciplines each capture only part of the phenomenon, cross-channel composites improve over single-channel baselines: climate-vector emergence reaches AUROC 0.944 and exoplanet vetting reaches AUROC 0.955. However, the exoplanet workflow is effectively tied with a strong combined-summary baseline, showing that decomposition does not always improve top-line performance. When one signal dominates, as in paradigm-shift detection, coordination mainly improves interpretation and traceability. For molecular sonification, the gain is representational rather than predictive. ScienceClaw x Infinite provides the auditable artifact and provenance layer for this evaluation. The benchmark therefore assigns value to coordination only when the corresponding performance, provenance, or representation claim is supported by explicit comparators.

Problem

• cross-domain
• coordinated AI agents
• scientific inference
• partial evidence
• benchmark

Innovation

• cross-domain benchmark
• coordinated AI agents
• scientific inference
• provenance auditing
• performance decomposition