arXiv:2502.15224v2 Announce Type: replace Abstract: Interactive discovery requires agents to maintain and update structured beliefs over many rounds of feedback. Before evaluating agents in noisy, open-ended scientific environments, it is useful to isolate this prerequisite capability under controlled conditions. We introduce Auto-Discovery-Bench, a deterministic oracle-guided diagnostic benchmark in which agents recover hidden structures through repeated hypothesis--intervention--feedback cycles. The benchmark instantiates three controlled discovery abstractions: directed graph discovery, und
The proliferation of AI systems necessitates robust diagnostic tools to ensure their reliable and predictable operation, particularly in complex, interactive environments.
Improving the diagnostic capabilities for AI agents directly accelerates their development and deployment in real-world, high-stakes applications, affecting their trustworthiness and efficiency.
This benchmark provides a standardized, controlled environment for evaluating and improving the structured state tracking capabilities of AI agents, which was previously harder to isolate and measure.
- · AI researchers
- · AI development platforms
- · Robotics companies
- · Complex systems integrators
- · Companies relying on opaque, non-diagnosable AI systems
- · Approaches lacking structured belief-state management
The new benchmark allows for more rapid and reliable iteration on AI agent architectures.
Improved diagnostics will lead to more robust and less error-prone AI agents deployed in critical applications.
As AI agents become more reliable, new sectors and applications currently deemed too risky for autonomous systems will open up.
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