# Mapping a Decade of Machine Learning Research Into Topic Pages A new platform synthesizes 109,000 research papers into structured, benchmark-focused topic pages. - **Source:** [r/MachineLearning](https://www.reddit.com/r/MachineLearning) - **Published:** 2026-11-03 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/9f7afeaf-d32b-48c5-93f2-f3f8b91430f3 ## Consolidating Academic Research The platform aggregates 109,000 accepted papers from major conferences like ICML, NeurIPS, ICLR, CVPR, and AAAI published since 2016. By grouping these into 1,260 distinct topics, the developers aim to provide a structured overview of academic progress. Currently, 79,000 of these papers have had their result tables and ablations extracted for analysis. ## Granular Topic Page Insights Each topic page provides a deep dive into specific research areas, such as compact Gaussian splatting. Users can view historical benchmark records, identifying which specific paper established each performance milestone. For example, the Deep Blending PSNR metric showed minimal movement, shifting from 30.42 to 30.45, illustrating the plateauing of certain performance metrics. ## Tracking Research Velocity The data highlights rapid shifts in research focus, exemplified by the Gaussian splatting topic. This field grew from 29 papers in the 2023-2024 period to 240 papers in 2025-2026. Such metrics allow researchers to visualize the acceleration of specific subfields and identify when a topic transitions from niche interest to mainstream academic focus. ## Comparative Baseline Analysis The tool identifies which models serve as the standard for comparison in recent literature. In the case of 3D Gaussian splatting, 38 out of 113 papers utilized 3DGS as a primary baseline. Other frequently cited benchmarks include 2DGS, Scaffold-GS, and Mip-Splatting, providing a clear map of the current competitive landscape for new research. ## Ablation and Methodology Transparency Beyond performance numbers, the platform extracts ablation studies from recent papers, including those that negatively impacted performance. This feature allows users to understand the specific components that contribute to model success or failure. By linking every extracted number directly to its source paper, the platform ensures transparency and allows for immediate verification. ## Operational Limitations and Scope The current dataset primarily reflects papers published up to 2025, with 2026 benchmark numbers and ablations currently under active development. The developers explicitly state that extraction is not perfect and benchmark comparability varies across studies. Users are advised to treat the platform as a research starting point rather than a definitive leaderboard. ## Community-Driven Data Refinement The platform is currently free to access and requires no user signup for full functionality. The developers are actively soliciting feedback from domain experts to correct potential inaccuracies in the extracted data. This crowdsourced verification process is considered the most critical component for improving the reliability of the platform's automated data extraction. ## Strategic Utility for Researchers By transforming unstructured PDF content into queryable, benchmark-linked pages, this tool reduces the manual overhead of literature reviews. It enables practitioners to quickly assess the state of the art, identify relevant baselines, and understand the historical trajectory of specific ML topics. This synthesis represents a shift toward more data-driven academic navigation. ## Sources covering this story - https://www.reddit.com/r/MachineLearning/comments/1xmk8rx/we_turned_10_years_of_ml_papers_into_one_page_per/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._