Priority Research Agenda
The field's highest-priority open research directions. Open a direction for the full analysis, investigate it in The Lab, or read its living systematic review.
Current AI-assisted research tools are overwhelmingly validated on computer science and closely related domains, leaving the vast majority of scientific disciplines underserved. This domain narrowness fundamentally…
Hallucinated citations, fabricated findings, and factually incorrect statements represent the most critical reliability barrier for deploying LLMs in scientific workflows. Despite being the largest cluster of identified…
The field currently lacks consensus on how to measure whether AI-assisted literature reviews, peer reviews, or research summaries are actually better, worse, or biased compared to human-produced equivalents. Without…
While significant progress has been made automating individual steps such as screening or extraction, fewer than 2% of studies have explored end-to-end automation of the complete literature review cycle. Integrating…
Current benchmarks and systems predominantly evaluate single-turn interactions, but real scientific inquiry requires sustained, multi-step reasoning, iterative hypothesis refinement, and tool-using agents operating over…
Scientific papers communicate critical quantitative information through figures, tables, charts, and diagrams that current text-focused AI systems cannot reliably process. This limitation fundamentally constrains…
AI systems integrated into scientific workflows introduce systematic biases through training data, model architecture, and deployment choices that can distort research outputs, perpetuate existing inequities in citation…
Despite widespread use of prompt engineering and fine-tuning in deploying LLMs for scientific tasks, the field lacks principled, evidence-based guidance on which strategies work best for specific scientific…
Evaluating the novelty of research ideas and generating genuinely new hypotheses are among the highest-value potential applications of AI in science, yet they remain among the least understood and least benchmarked…
The rapid adoption of AI tools in research is outpacing the development of disciplinary norms, institutional policies, and governance frameworks. Researchers across all fields are navigating inconsistent and often…