Consensus vs Connected Papers

Consensus

Consensus Search revolutionizes academic research by enabling users to ask natural language questions and receive synthesized, evidence-based answers drawn from millions of peer-reviewed papers. The AI-powered search engine goes far beyond traditional keyword matching—it understands research context, interprets scientific methodology, and identifies relevant findings even when papers use different terminology. When a user asks a question like 'Does meditation reduce anxiety?', the system analyzes hundreds of relevant studies, extracts their conclusions, and presents a coherent synthesis showing what the collective research indicates. Each answer includes the Consensus Meter visualization showing agreement levels, plus direct links to source papers for verification. The search also highlights key findings from individual studies, showing sample sizes, methodologies, and statistical significance to help users evaluate evidence quality. Transform hours of manual literature review into seconds of AI-powered research synthesis with fully cited, verifiable answers

✓ Transform hours of manual literature review into seconds of AI-powered research synthesis with fully cited, verifiable answers

Connected Papers

Connected Papers' signature feature transforms complex citation networks into intuitive visual graphs where each paper appears as a node, with connections representing citation relationships and content similarity. The AI engine analyzes thousands of papers to identify meaningful relationships, positioning closely related works near each other in the visualization. Node size indicates the paper's influence within the network (based on citation count and centrality), while color coding represents publication year, enabling researchers to instantly distinguish foundational works from recent developments. Users can zoom, pan, and interact with the graph to explore different areas of the research landscape, clicking on any node to view paper details, abstracts, and direct links to full texts. Researchers can comprehend an entire research field's structure in minutes rather than hours, identifying key papers and relationships that would be missed through traditional linear search results.

✓ Researchers can comprehend an entire research field's structure in minutes rather than hours, identifying key papers and relationships that would be missed through traditional linear search results

Consensus

Study Insights provides AI-generated summaries of research papers that extract and highlight the most critical information without requiring users to read entire publications. The machine learning models are trained to identify paper structure and extract key elements including research questions, methodology, sample characteristics, primary findings, statistical results, and author conclusions. Each summary is structured consistently, making it easy to quickly compare findings across multiple papers. The system also identifies and highlights limitations acknowledged by researchers, potential conflicts of interest, and funding sources—critical context often buried in paper text. For quantitative studies, key metrics and statistical significance values are prominently displayed, helping users quickly assess the strength of findings. Understand the essential findings, methodology, and limitations of any research paper in minutes rather than hours

✓ Understand the essential findings, methodology, and limitations of any research paper in minutes rather than hours

Connected Papers

Beyond the main similarity graph, Connected Papers provides specialized views that separate a paper's intellectual ancestry from its subsequent influence. The 'Prior Works' view identifies foundational papers that the selected work builds upon, tracing the intellectual lineage and theoretical foundations of research. Conversely, the 'Derivative Works' view shows papers that have cited and built upon the selected work, revealing how ideas have been extended, applied, or challenged. This bidirectional analysis helps researchers understand both the historical context of research and its ongoing impact, essential for comprehensive literature reviews and identifying research trajectories. Users can trace the complete intellectual history of any research topic, understanding both where ideas originated and how they've evolved, enabling more thorough and contextualized literature reviews.

✓ Users can trace the complete intellectual history of any research topic, understanding both where ideas originated and how they've evolved, enabling more thorough and contextualized literature reviews

Consensus

The Consensus Meter is Consensus's signature feature that visually represents the level of scientific agreement on any research question. When users search for a topic, the meter displays the percentage of studies that support, oppose, or show mixed results on the question at hand. This visualization transforms the abstract concept of 'scientific consensus' into an immediately understandable graphic. The meter is powered by AI that reads and classifies findings from relevant papers, categorizing each study's conclusions and aggregating them into an overall consensus view. Users can click through to see which specific papers support each position, enabling deeper investigation of why certain studies reached different conclusions. This feature is particularly valuable for contested topics where understanding the weight of evidence—not just individual studies—is crucial. Instantly understand whether scientific research supports, opposes, or is divided on any topic without manually reviewing dozens of papers

✓ Instantly understand whether scientific research supports, opposes, or is divided on any topic without manually reviewing dozens of papers

Connected Papers

Connected Papers allows researchers to build comprehensive graphs by adding multiple seed papers, creating a unified visualization that shows how different papers and research threads interconnect. This feature is particularly valuable for interdisciplinary research or when exploring how different approaches to a problem relate to each other. Users can start with several key papers from their reading list and generate a combined graph that reveals unexpected connections, identifies bridging papers that link different research communities, and provides a holistic view of complex research landscapes spanning multiple sub-fields or methodological approaches. Researchers working on interdisciplinary projects or complex topics can map relationships across multiple research threads, discovering connections and bridging works that would be invisible when examining papers individually.

✓ Researchers working on interdisciplinary projects or complex topics can map relationships across multiple research threads, discovering connections and bridging works that would be invisible when examining papers individually

Consensus

The Citation Network feature maps the intellectual lineage of research by visualizing how papers cite and build upon each other over time. Users can explore interactive network graphs showing foundational papers that established key concepts, subsequent studies that replicated or extended findings, and recent developments pushing the field forward. The visualization identifies highly-cited influential papers, research clusters working on related questions, and the evolution of ideas across decades of scholarship. This feature helps researchers understand not just what is known, but how knowledge developed—which early studies were seminal, which findings have been consistently replicated, and which represent emerging areas of investigation. The network also reveals connections between seemingly disparate research areas, enabling interdisciplinary discovery. Trace the evolution of scientific ideas and identify the most influential foundational research in any field

✓ Trace the evolution of scientific ideas and identify the most influential foundational research in any field

Connected Papers

Each paper in the Connected Papers graph includes comprehensive metadata displayed in an accessible sidebar panel. Users can view full abstracts, author lists, publication venues, citation counts, and publication dates without leaving the platform. The interface provides direct links to the paper on its original source (journal website, arXiv, PubMed, etc.) as well as links to the paper on Google Scholar for additional context. For papers available as open access, Connected Papers often provides direct PDF links, streamlining the research workflow by reducing the need to navigate multiple databases and repositories. Researchers can evaluate paper relevance directly within the platform, accessing abstracts and metadata to make informed decisions about which papers to read in full, significantly accelerating the literature review process.

✓ Researchers can evaluate paper relevance directly within the platform, accessing abstracts and metadata to make informed decisions about which papers to read in full, significantly accelerating the literature review process

Consensus

Research Collections enables users to organize discovered papers into custom folders for ongoing research projects, literature reviews, or collaborative team efforts. Users can create hierarchical folder structures, add personal notes and annotations to saved papers, tag papers with custom labels, and share collections with collaborators. The collaboration features support real-time teamwork on literature reviews, with team members able to add papers, comment on findings, and discuss research within the platform. Collections sync across devices, and users can export entire collections to reference management tools like Zotero, Mendeley, or EndNote in standard citation formats. The system also provides collection-level analytics showing research trends, publication dates, and consensus patterns across saved papers. Organize, annotate, and collaborate on research projects with team members while maintaining seamless integration with existing reference management workflows

✓ Organize, annotate, and collaborate on research projects with team members while maintaining seamless integration with existing reference management workflows

Connected Papers

Connected Papers generates unique, shareable URLs for every graph created on the platform, enabling seamless collaboration and knowledge sharing among research teams. When a researcher discovers a valuable graph visualization, they can share the exact view with colleagues, supervisors, or students via a simple link. Recipients see the identical graph with all papers and connections preserved, facilitating discussions about research directions, collaborative literature reviews, and educational contexts where instructors want to show students the landscape of a research area. This feature transforms individual discovery into collaborative knowledge building. Research teams can collaborate effectively on literature reviews and research planning, sharing discoveries instantly and building collective understanding of research landscapes without requiring recipients to recreate searches.

✓ Research teams can collaborate effectively on literature reviews and research planning, sharing discoveries instantly and building collective understanding of research landscapes without requiring recipients to recreate searches