Code for Revisiting a Cutting Plane Method for Perfect Matchings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This is an example implementation, in Python 3, of the minimum-cost perfect matching algorithm described in Chen, Cheung, Kielstra, and Winn's paper <em>Revisiting a Cutting Plane Method for Perfect Matchings</em>. It currently uses SciPy to solve linear programs, but will work with any black-box LP solver with only minor alterations. This is academic example code, not suitable for use in a production environment. <strong>Requirements</strong> SciPy NumPy igraph <strong>Usage</strong> <pre><code class="language-python">import cpmatching import cpmatching.io as io from igraph import Graph G = Graph() G.add_vertices(16) G.add_edges([[13, 15], [11, 14], [10, 11], [10, 14], [9, 11], [8, 11], \ [7, 12], [5, 15], [5, 13], [4, 11], [4, 13], [3, 7], [2, 6], [2, 13], \ [1, 5], [0, 1], [0, 3], [0, 12], [8, 9], [4, 12]]) G.es["weight"] = 1 x = cpmatching.find_matching(G) io.pretty_print_solution(G, x)</code></pre> Further details are available in README.md.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it