Consensus

Searches scientific research papers using semantic understanding rather than keyword matching, leveraging embeddings-based retrieval to find papers semantically similar to natural language queries. The system encodes user queries and paper abstracts/full text into a shared vector space, then ranks results by cosine similarity, enabling discovery of relevant research even when terminology differs between query and source material.

Analyzes retrieved scientific papers using large language models to synthesize direct answers to user questions, extracting key findings, consensus positions, and evidence from multiple sources. The system performs multi-document summarization and reasoning across papers to generate coherent, evidence-backed responses rather than returning raw paper lists, with citations linked back to source material.

Analyzes multiple papers on the same topic to identify areas of scientific agreement, disagreement, and uncertainty, using NLP techniques to extract claims and compare them across sources. The system identifies consensus positions (findings supported by multiple independent studies) and highlights minority or conflicting views, providing users with a nuanced understanding of what the research actually supports.

Automatically extracts and indexes structured metadata from scientific papers including authors, publication date, journal, DOI, abstract, methodology, and key findings using OCR and NLP techniques. This enables filtering, sorting, and faceted search across papers by publication year, journal impact, author reputation, and research methodology, supporting advanced discovery workflows.

Interprets natural language scientific questions by identifying key concepts, research domains, and implicit assumptions, then reformulating them into effective search queries across the scientific literature. Uses domain-specific NLP models trained on scientific text to understand terminology, recognize synonyms, and map colloquial language to formal scientific concepts.

Evaluates the quality and strength of evidence in retrieved papers using criteria such as study design (RCT vs observational), sample size, methodology rigor, and peer review status. Assigns confidence scores or evidence grades to findings, helping users distinguish between high-quality evidence and preliminary or low-quality studies.

Maps relationships between papers through citation networks, showing which papers cite which others and identifying influential papers, seminal works, and emerging research directions. Enables users to explore research genealogy, understand how ideas evolved, and identify key papers that shaped a field.

Searches scientific literature across papers published in multiple languages (Chinese, Spanish, German, French, etc.) by translating queries and papers into a shared semantic space using multilingual embeddings. Enables discovery of research published in non-English journals and languages, reducing English-language bias in scientific search.