analiza aiqbench
ShadowMiner v1 represents a significant step in autonomous scientific discovery by using knowledge graphs and LLMs to identify structural blind spots in AI research literature. The system validates generated hypotheses against existing work and factual sources, demonstrating a practical framework for automating the problem-discovery phase of the research cycle. This approach directly addresses the scalability challenges inherent in advancing machine learning fields through systematic gap analysis.
ShadowMiner v1 - An Experience Report on Implementing and Measuring a Problem-and-Hypothesis Discovery Engine
arXiv:2610.04339v1 Announce Type: new Abstract: ShadowMiner v1 is a system that automatically discovers research problems and generates hypotheses from AI papers. It is a nine-stage pipeline. It structures documents into a knowledge graph and finds graph gaps in it - structural blind spots in research. These graph gaps are included in the LLM generation prompt. Each generated hypothesis is then verified by checking whether it is already covered by existing research, scoring its quality, and checking that the facts it relies on are accurately drawn from its sources. This report does not propose a new generation or evaluation technique. It describes our experience of implementing and applying ideas from prior work, and measuring whether each one actually contributed.
Abstract:ShadowMiner v1 is a system that automatically discovers research problems and generates hypotheses from AI papers. It is a nine-stage pipeline. It structures documents into a knowledge graph and finds graph gaps in it - structural blind spots in research. These graph gaps are included in the LLM generation prompt. Each generated hypothesis is then verified by checking whether it is already covered by existing research, scoring its quality, and checking that the facts it relies on are accurately drawn from its sources. This report does not propose a new generation or evaluation technique. It describes our experience of implementing and applying ideas from prior work, and measuring whether each one actually contributed.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2610.04339 [cs.AI]
(or arXiv:2610.04339v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.04339 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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