ROAR: Unifying Runs across Heterogeneous AI-Driven Research Systems

2026-10-02

AI Summary

This study addresses the fragmentation of operational data in AI research systems and the absence of unified analytical infrastructure across heterogeneous platforms by proposing ROAR. The system employs a relational schema to standardize heterogeneous outputs while preserving data provenance, integrating parsing layers and multi-objective score normalization techniques to ensure compatibility with new systems without architectural modifications. Experiments consolidating over 900 runs reveal search structures, early-gain patterns, and strategy differences imperceptible within individual systems, validating the practical utility of pooled data for configuration optimization. To our knowledge, this work presents the first unified analytical framework spanning heterogeneous AI research systems.

Abstract

Each run of an AI-driven research system (ADRS) is an expensive search over a vast solution space, and dependable evaluation requires many runs, making run data both costly to produce and valuable to retain for large-scale analysis. Yet this data remains fragmented: teams operate in isolation, ADRS frameworks emit results in different formats, and no shared infrastructure exists to aggregate or compare runs across problems and systems. We present ROAR, a solution for systematically unifying and analyzing heterogeneous ADRS outputs. ROAR addresses two challenges: reconciling heterogeneous ADRS outputs and enabling analytics across runs with different objectives and scoring functions. We achieve this through a relational schema and parsing layer that normalize heterogeneous ADRS outputs while preserving data lineage and temporal structure, and accommodating new systems without requiring schema modifications. From building a corpus of more than 900 runs from multiple ADRS, we show how pooled data can reveal properties of problem landscapes that are difficult to observe. Consistent with prior work, runs with identical configurations may converge to different scores. We find that many runs realize most gains early, and that the effectiveness of different strategies for incorporating prior solutions into the search process varies across problems. We further show that the pooled corpus is actionable and not merely analytical by using ROAR to configure ADRS runs. Together, these results illustrate how pooled ADRS data can expose problem-dependent structure in search behavior that is difficult to detect from any single system, team, or benchmark. Such cross-cutting insights are difficult to obtain while runs remain siloed; ROAR is the first infrastructure designed to unify them.

Problem

• AI-driven research systems
• data fragmentation
• heterogeneous outputs
• run aggregation
• cross-system analysis

Innovation

• Heterogeneous ADRS
• Data Unification
• Relational Schema
• Cross-system Analytics
• Search Behavior