“"A rudimentary, parameter-free heatmap based on the intrinsic k-nearest neighbor structure...can match or surpass the performance of more sophisticated, learned heatmaps." — Pan, Wang, Ying, Xue & Yu, "Beyond the Heatmap: A Rigorous Evaluation of Component Impact in MCTS-Based T...”
You know that feeling when you want to know whether a research area has actually produced anything usable, and the only maps available are hand-typed markdown lists that stopped being updated two years ago? Combinatorial optimization has that problem badly: papers land at ICLR, NeurIPS, ICML, AAAI, and AAMAS across 36 distinct problem types, and no single venue tracks them. The direct-format competitor omargup/neural_combinatorial_optimization stopped at 14 stars and has not been pushed since 2020-08-14 — the standard failure mode. awesome-ml4co addresses the maintenance half of that problem; it does not yet address the 'is any of this better than the classical solver' half.
Think of it like a recipe book that gets typeset automatically instead of being edited page by page. Contributors add a row to data/papers.csv with the fields category, title, publisher, year, type, link, authors, code, then run src/generator.py, which rewrites the whole 164,470-byte README from that CSV. In June 2026 the team added a second data file, data/benchmark_results.csv — 178 rows, 22 columns — that records reported gap and runtime for TSP methods, and the same generator renders SVG charts into assets/benchmarks/ and embeds them into the problem sections. The clever part is what the data layer makes possible: because every benchmark row carries source_paper_title, source_link, pdf_url, source_table, and extraction_confidence, you can trace any plotted point back to the exact table in the exact paper it came from. The unclever part is that the method column contains only learned solvers, so the chart measures progress inside the neural cohort and nothing outside it.
If you are a researcher or ML engineer tracking neural combinatorial optimization and you are tired of grepping conference proceedings by hand, this is the highest-coverage index that exists and the CSVs alone justify the clone. It also suits anyone building tooling on top of the literature — the machine-readable layer is the real product. It is not for you if you need to decide whether to put a learned solver into production: the benchmark file contains no LKH, Concorde, or Gurobi rows, so it cannot answer whether any charted method beats the solver you already run.
Worth cloning for the data layer, not for the charts. The index itself has run since 2021-03-21 with 41 contributors and a last push on 2026-07-19, and the CSV workflow is demonstrably live — recent commit messages include 'Add recent papers (ICLR/AAAI/AAMAS 2026) and regenerate README'. Treat the June 2026 benchmark charts as what the README calls them, a Beta feature: hardware is not normalized, the only distribution is uniform_random_2d_unit_square, and there is no classical baseline to anchor against.
Deep-dive insight, Easy and Pro modes, plus action playbooks — the full breakdown is one tap away.