arXiv:2512.00986v3 Announce Type: replace Abstract: A surge in academic publications calls for automated deep research (DR) systems, but accurately evaluating them is still an open problem. First, existing benchmarks often focus narrowly on retrieval while neglecting high-level planning and reasoning. Second, existing benchmarks favor general domains over the academic domains that are the core application for DR agents. To address these gaps, we introduce ADRA-Bank, a modular benchmark for Academic DR Agents. Grounded in academic literature, our benchmark is a human-annotated dataset of 200 in
The proliferation of academic publications necessitates more sophisticated automation for deep research, leading to a demand for robust evaluation benchmarks for these systems.
A standardized, academic-specific benchmark allows for accurate measurement and accelerated development of AI agents capable of performing complex research, which is critical for future innovation cycles.
The ability to accurately evaluate and compare academic deep research agents will improve, driving more focused development and clearer understanding of their capabilities and limitations.
- · AI research labs
- · Academic institutions
- · Deep research agent developers
- · Scientific publishers
- · Manual academic research processes
- · Benchmarking tools focused on general domains
The new benchmark accelerates the development of more capable and reliable deep research AI agents.
Improved deep research agents lead to faster scientific discovery and knowledge synthesis across various academic fields.
The enhanced efficiency of academic research could transform scientific funding models and publication processes, potentially challenging traditional peer review systems.
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Read at arXiv cs.CL