SciAtlas: A Computable Atlas of Science for Knowledge-Grounded AI Research

Key points

• Problem: Most AI scientists gather scientific knowledge through retrieval procedures designed around individual workflows, and even when agents iterate, these procedures usually leave a transient collection of documents used for one task. The authors see this as a lack of breadth, depth and standardization, and note that existing scientific knowledge graphs are often bounded by particular disciplines, information objects or schemas, which limits their shared use across research workflows.
• Input and output: This is a system paper that builds a scholarly knowledge graph, a shared retrieval mechanism and three research workflows. It takes a research object such as a topic, research question, seed paper or idea, grounds it in seed nodes of the graph, and returns ranked paper and author pools. Research Development Trajectory (RDT) and Research Opportunity Discovery (ROD) mainly use the paper pool and Innovation Assessment (IA) also uses the author pool, producing respectively a review outline organized by period and research thread with section-level evidence packs, a motivation, mechanism, falsifiable hypothesis and phased experimental plan, and reviewer-specific assessments, a meta-review and revision advice.
• Method: Under one schema, the graph centres on papers and holds five layers: citation and publication relations, normalized keywords with co-occurrence relations, a topic-to-domain disciplinary hierarchy, authorship, affiliation and co-authorship relations, and peer-review and evaluation signals attached where available. For retrieval, an LLM parser splits the input into typed query units such as concepts, papers, authors and topics, symbolic indexes over names and identifiers and approximate nearest-neighbour indexes over dense embeddings pick seed nodes, and relevance propagates over a query-centred local graph expanded for a fixed number of hops by mixing a restart term back to the seeds with diffusion from neighbours until scores converge or reach the iteration limit. On top of this, RDT diagnoses missing periods and research threads to repeat retrieval rounds, ROD condenses field bottlenecks into a Research Structure Signature to find same-field and cross-domain mechanisms, and IA splits an idea into motivation, method, experiment and impact claims to build claim-level evidence, reviewer profiles and rubrics.
• Implementation: The primary source is OpenAlex, and the normative layer was filled by an automated search agent that located public peer-review pages and attached them to papers. The resulting graph spans 26 disciplines and links more than 157M entities through 3B relational triples, including over 43M papers, and configurable LLM components in the three workflows and the foundation evaluations used GPT-5.5. Per the README, the GitHub repository holds a pip-installable client and CLI for the hosted SciAtlas API, workflow code for literature review, idea evaluation and idea generation, and an Agent Skill pack for coding agents such as Codex and Claude Code, while graph retrieval runs through the hosted API with a personal API token.
• Results: On SurveyLens, made of 1,000 human-written surveys in 10 disciplines, SciAtlas-assisted RDT reached a macro-average Rubric Score of 4.82 against 4.67 for the strongest baseline, SurveyForge, and on LiveIdeaBench, made of 1,180 keywords across 22 domains, ROD scored a mean of 7.38 against 7.12 for the strongest baseline, AI-Scientist. On SciAtlasEval-Idea with 349 ideas, where three LLM judges compared reports pairwise, IA had mean win rates of 99.6% against CoT (chain-of-thought prompting), 96.6% against RAG (retrieval-augmented generation), 74.5% against DeepReviewer, 78.5% against NoveltyAgent and 68.2% against ScholarEval. On 200 SciAtlasEval-HQR (Heterogeneous Query-Object Retrieval) cases with direct query-target edges masked, nDCG@20 (ranking quality of the top 20 results) was 0.51, nearly three times the 0.18 of the strongest baseline, and TaxoBench paper-retrieval F1 (the harmonic mean of precision and recall) was 32.8 against 21.8 for the strongest baseline.
• Limits: The authors note that the graph is built by an automated pipeline but updated periodically, so emerging work may enter late, coverage depends on the underlying scholarly records, and the availability of expertise and evaluation signals can vary across disciplines and venues. The study focuses on knowledge-intensive stages and leaves closed-loop experimental validation out of scope, and representing procedures, conditions, measurements and outcomes as explicit graph objects remains future work, so the claims concern scientific-context construction and knowledge-grounded decision support. The graph's scale demands substantial storage, indexing and retrieval infrastructure, so propagation is restricted to query-centred local neighbourhoods; SciAtlasEval was curated by the authors, and correctness gains on the single-paper PubMedQA and SciFact tasks of ScholarQABench were comparatively small.

Paper links

External research summaries. These are not HDATF publications or measured product results.