Only a paper title. For best accuracy, please copy & paste the exact title to ensure the signal reconstruction pipeline works properly. QM will automatically generate abstract-level semantics, citation contexts, and reference contexts.

Most analyses finish within **3 minutes or so**. However, the number and strength of discovered questions vary depending on the paper's citation/reference density. Some papers yield multiple strong questions; others yield fewer but deeper insights.

No. QM reconstructs the citation + reference signal space for you. Manual reading is optional and not required for QM to function.

QM still works. In such cases, reference-context signals become the primary source of analysis. Many high-value research gaps originate from reference-side limitations.

QM uses a high-limit cap and prioritizes High-Influence citation signals to keep the analysis efficient and meaningful. Only the most information-dense citation patterns are processed.

No. And this is scientifically correct. Each paper contains different levels of citation richness, reference diversity, methodological constraints, and theoretical boundaries. Signal variation is a natural part of the literature landscape.

Yes. QM is signal-driven, not domain-specific. It works across physics, materials science, computer science, biology, engineering, social sciences, and interdisciplinary research.

No. QM accelerates problem discovery, not the entire review. It helps you identify scientifically grounded questions before diving deeper.

QM does not "invent" questions. It extracts and organizes signal patterns that already exist in citation and reference contexts. This yields defensible, high-value, literature-aligned questions—not hallucinations.

The Validation tag (Validated or Failed) is the result of our system's internal Quality Self-Check. This process is designed to ensure that all generated research opportunities meet our strict format and structural requirements. Status Meaning & Action Guide: • Validated (Internal: "Passed"): High Confidence. The entry has successfully passed all mandatory format and structural checks (e.g., key fields are under the 150-character limit). Recommended for direct use—these are the most reliable and ready-to-use suggestions. • Failed: Quality Warning. The entry contains valuable content but did not fully meet one or more automated quality standards (e.g., text length exceeded the limit). Requires Manual Review/Edit—we recommend you check and shorten the Source Context or Structured Question before use. Typically, the text length in a key field exceeded the system's limit. Design Note: Every identified signal passes through our strict quality screening process. We retain 'Failed' entries to ensure no potentially valuable signal is lost, even if it does not fully meet an automated quality standard, thus empowering the user for final manual judgment and refinement.

No. Your inputs stay within your workspace. They are used solely for generating the requested analysis and are not recycled to train any models.

Yes. Teams can distribute credits among members.