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Toward Evaluation Frameworks for Multi-Agent Scientific AI Systems

Marcin Abram

Abstract

We analyze the challenges of benchmarking scientific (multi)-agentic systems, including the difficulty of distinguishing reasoning from retrieval, the risks of data/model contamination, the lack of reliable ground truth for novel research problems, the complications introduced by tool use, and the replication challenges due to the continuously changing/updating knowledge base. We discuss strategies for constructing contamination-resistant problems, generating scalable families of tasks, and the need for evaluating systems through multi-turn interactions that better reflect real scientific practice. As an early feasibility test, we demonstrate how to construct a dataset of novel research ideas to test the out-of-sample performance of our system. We also discuss the results of interviews with several researchers and engineers working in quantum science. Through those interviews, we examine how scientists expect to interact with AI systems and how these expectations should shape evaluation methods.

Toward Evaluation Frameworks for Multi-Agent Scientific AI Systems

Abstract

We analyze the challenges of benchmarking scientific (multi)-agentic systems, including the difficulty of distinguishing reasoning from retrieval, the risks of data/model contamination, the lack of reliable ground truth for novel research problems, the complications introduced by tool use, and the replication challenges due to the continuously changing/updating knowledge base. We discuss strategies for constructing contamination-resistant problems, generating scalable families of tasks, and the need for evaluating systems through multi-turn interactions that better reflect real scientific practice. As an early feasibility test, we demonstrate how to construct a dataset of novel research ideas to test the out-of-sample performance of our system. We also discuss the results of interviews with several researchers and engineers working in quantum science. Through those interviews, we examine how scientists expect to interact with AI systems and how these expectations should shape evaluation methods.

Paper Structure

This paper contains 36 sections, 3 figures.

Table of Contents

  1. Introduction
  2. The Benchmarking Problem
  3. Benchmark Design Principles
  4. Benchmark Taxonomy
  5. Replication Benchmarks
  6. Error Detection Benchmarks
  7. Scientific Reasoning Benchmarks
  8. Future Research Directions
  9. Assumption Enumeration
  10. Minimal Counterexample Construction
  11. Technique Recommendation
  12. Discovery-Style Benchmarks
  13. Fabricated Phenomena
  14. Solving Novel Problems
  15. Benchmark Construction Strategies
  16. ...and 21 more sections

Figures (3)

  • Figure 1: A map showing Rx errors (the nodes; darker is better) and CZ errors (the edges; darker is better) for ibm_torino (133 qubit IBM quantum processor). Figure taken from URL https://quantum.cloud.ibm.com/computers?system=ibm_torino.
  • Figure 2: Sentiment toward two dimensions: critical thinking ability and problem-solving ability. Results based on the conducted interviews.
  • Figure 3: Major themes of future extensions extracted from recent quant-ph ArXiv papers.