Search-Time Contamination in Deep Research Agents: Measuring Performance Inflation in Public Benchmark Evaluation
arXiv:2606.05241v1 Announce Type: cross Abstract: Public benchmarks enable fair and reproducible evaluation of LLM reasoning, but they become fragile for deep research agents that actively search the web during inference. Such agents may retrieve public benchmark metadata, question context, or even ground-truth answers via web search. This gives rise to Search-Time Contamination (STC), where external retrieval bypasses intended reasoning and inflates measured performance. We systematically study STC in deep research agent evaluation. We define three contamination types with increasing severity
The proliferation of advanced AI research agents capable of web search necessitates a re-evaluation of benchmark integrity as these models become more sophisticated and integrated into research workflows.
This study exposes a critical vulnerability in the evaluation of advanced AI agents, potentially leading to an overestimation of their true reasoning capabilities and misallocation of research resources.
The standard methodology for evaluating AI agents, particularly those with web search capabilities, must evolve to account for and mitigate Search-Time Contamination.
- · AI ethicists and researchers focused on robust evaluation
- · Developers of more sophisticated, contamination-resistant benchmarks
- · Organizations prioritizing genuine AI reasoning over inflated performance metric
- · AI research agents relying on web search for benchmark performance
- · Benchmarks susceptible to search-time contamination
- · Early-stage AI startups whose 'performance' was based on contaminated benchmarks
New benchmark design principles will emerge, specifically targeting the detection and prevention of Search-Time Contamination.
There will be a push for more transparent and auditable AI agent evaluation methodologies, potentially leading to a re-ranking of 'state-of-the-art' models.
Investment in AI agent development may shift from general web search integration to more constrained, reasoning-focused architectures.
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Read at arXiv cs.AI