Improved Upper Bounds on Multiflow-Multicut Gaps in Cactus Graphs
Bibliographic record
Abstract
Given a set of source-sink pairs, the maximum multiflow problem asks for the largest total amount of flow that can be feasibly routed between them. The minimum multicut problem, which is dual to multiflow, seeks the lowest-cost set of edges whose removal disconnects all source-sink pairs. It is straightforward to see that the value of a minimum multicut is at least that of the corresponding maximum multiflow. The ratio between the two is known as the multiflow-multicut gap. The classical max-flow min-cut theorem tells us that this gap is exactly one when there is only a single source-sink pair. However, for multiple source-sink pairs, the gap can be arbitrarily large. In this work, we investigate the multiflow-multicut gap in cactus graphs, and establish the following results (i) tight upper bound of 1.5 for cycle (ii) an upper bound of 2 + 2/(ln 2) < 3.45 for general cactus graph (iii) tight upper bound of 2 for unicyclic graphs, where the graph contains exactly one cycle (iv) tight upper bound of 2 for path cactus graphs, where cycles are arranged along a single path. We develop novel generalizations of the classical rounding algorithm to establish our results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".