Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists

Key points

• Problem: Scholarly platforms such as Google Scholar, Semantic Scholar, and OpenAlex treat the paper as the unit and link papers by citations, so whether a citation extends a method, resolves its bottleneck, or is mere background stays buried in text. The authors argue that humans reconstruct this lineage in their heads, while AI research agents cannot reliably reconstruct method evolution from unstructured text.
• Input and output: This is a dataset paper that also proposes three operators running on top of the graph. The input is 1,030,314 AI papers from 1965 to 2025, and the output is a graph with 8,155 method nodes, 9,545 aliases, and 9,410,201 typed edges, where every non-background causal edge carries verbatim quotes as evidence. Given a method name, an idea description, or a research question, the operators return evolution chains, five-dimension scores, or a new research proposal with evidence.
• Method: An LLM classifier reads the text around each citation and labels the edge with one of seven types (extends, improves, replaces, adapts, uses_component, compares, background), and the first four form the strong-causal subgraph used for lineage search. Each causal edge gets bottleneck, mechanism, and trade-off spans quoted verbatim from the citing paper plus a confidence, and a deterministic checker discards edges whose quotes fail substring match or whose endpoints violate publication-year order. Lineage search uses SGT-MCTS (Self-Guided Temporal Monte Carlo tree search), which adds an edge-confidence and year-gap prior to UCT (the MCTS selection rule that balances high-scoring and under-visited branches), idea evaluation computes five dimension scores from graph statistics instead of LLM judgment, and idea generation requires every proposal to cite the stored bottleneck quote of a real edge.
• Implementation: PDFs are parsed with the Nougat v1.0 parser, with the GROBID v0.8.0 parser as the fallback for the 3.7% of documents where Nougat fails, and extraction reads only the Introduction, Method, and Related Work sections of each citing paper. Both extraction phases use Qwen3.6-35B-A3B at temperature 0, with a separate audit model used only in the reliability benchmarks. The authors state that they release the graph and pipeline as open infrastructure.
• Results: On a benchmark built from 30 surveys (2,268 nodes, 1,462 edges, 133 chains), Intern-Atlas reached a node match ratio of 91.0%, an edge reachable ratio of 89.7%, and a path semantic correctness of 92.0%, and SGT-MCTS reached a node recall of 84.8 against 44.9 for the strongest baseline, Beam@10 (beam search with width 10). On 1,200 papers, mean idea scores followed the strata order of 8.48 for top-tier conferences, 7.83 for core conferences, 6.85 for workshops, and 5.84 for rejected submissions, and on 100 ideas rated by 10 experts the overall Spearman correlation was 0.81 against 0.58 for a pure LLM judge. Ideas generated for 100 research questions scored 7.20 overall, 1.02 points above the best of four baselines (Semantic Scholar, 6.18), and won 88.0%, 82.0%, and 81.0% of blind expert pairwise comparisons against No-KB (generation without external knowledge), OpenAlex, and BM25 RAG (generation grounded in papers found by BM25 keyword retrieval).
• Limits: The authors report Phase-1 edge-type accuracy of 70.4% for the production model and 93.0% for the audit model, reflecting how hard fine-grained relations such as extends versus improves are to separate. The 14-axis bottleneck taxonomy is fixed at release, the temporal coherence function (the weight given to publication-year gaps) is calibrated on post-2015 AI literature and needs recalibration for fields with different research cadences, and the generated ideas were scored with the same Intern-Atlas evaluation pipeline.

Paper links

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