Abstract
Deep Research Agents generate analyst-grade reports, yet evaluating them remains challenging due to the absence of a single ground truth and the multidimensional nature of research quality. Recent benchmarks propose distinct methodologies, yet they suffer from the Mirage of Synthesis, where strong surface-level fluency and citation alignment can obscure underlying factual and reasoning defects. We characterize this gap by introducing a taxonomy across four verticals that exposes a critical capability mismatch: static evaluators inherently lack the tool-use capabilities required to assess temporal validity and factual correctness. To address this, we propose DREAM (Deep Research Evaluation with Agentic Metrics), a framework that instantiates the principle of capability parity by making evaluation itself agentic. DREAM structures assessment through an evaluation protocol combining query-agnostic metrics with adaptive metrics generated by a tool-calling agent, enabling temporally aware coverage, grounded verification, and systematic reasoning probes. Controlled evaluations demonstrate DREAM is significantly more sensitive to factual and temporal decay than existing benchmarks, offering a scalable, reference-free evaluation paradigm.
Deep Research Agents generate analyst-grade reports, yet evaluating them remains challenging due to the absence of a single ground truth and the multidimensional nature of research quality. Recent benchmarks propose distinct methodologies, yet they suffer from the Mirage of Synthesis, where strong surface-level fluency and citation alignment can obscure underlying factual and reasoning defects. We characterize this gap by introducing a taxonomy across four verticals that exposes a critical capability mismatch: static evaluators inherently lack the tool-use capabilities required to assess temporal validity and factual correctness. To address this, we propose DREAM (Deep Research Evaluation with Agentic Metrics), a framework that instantiates the principle of capability parity by making evaluation itself agentic. DREAM structures assessment through an evaluation protocol combining query-agnostic metrics with adaptive metrics generated by a tool-calling agent, enabling temporally aware coverage, grounded verification, and systematic reasoning probes. Controlled evaluations demonstrate DREAM is significantly more sensitive to factual and temporal decay than existing benchmarks, offering a scalable, reference-free evaluation paradigm.
Community
Evaluating Deep Research Agents (DRAs) with static LLM judges often leads to the "Mirage of Synthesis," where fluent writing and accurate-looking citations mask underlying factual errors and flawed logic. The authors attribute this to a capability mismatch: static evaluators lack the active retrieval tools and temporal awareness needed to independently verify the claims they assess. To solve this, the paper introduces DREAM (Deep Research Evaluation with Agentic Metrics), a framework that establishes "capability parity" by making the evaluation process itself agentic. DREAM operates in two phases: first, a tool-equipped agent creates a custom evaluation protocol combining static standards with query-specific adaptive metrics (like Key-Information Coverage and Reasoning Quality), and second, it routes these metrics to the appropriate LLM, workflow, or agent evaluators for execution. Controlled experiments demonstrate that by actively cross-referencing external evidence, DREAM is significantly more sensitive to temporal degradation and well-cited falsehoods than existing benchmarks.
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