Bibliographic record
Abstract
The set agreement power of a shared object O describes O's ability to solve set agreement problems: it is the sequence (n_1, n_2, ..., n_k, ...) such that, for every k >= 1, using O and registers one can solve the k-set agreement problem among at most n_k processes. It has been shown that the ability of an object O to implement other objects is not fully characterized by its consensus number the first component of its set agreement power) [1, 3, 14]. This raises the following natural question: is the ability of an object O to implement other objects fully characterized by its set agreement power? We prove that the answer is no: every level n >= 2 of Herlihy's consensus hierarchy has two objects that have the same set agreement power but are not equivalent, i.e., at least one cannot implement the other. We also show that every level n >= 2 of the consensus hierarchy contains a deterministic object O_n with some set agreement power (n_1, n_2, ..., n_k, ...) such that being able to solve the k-set agreement problems among n_k processes, for all k >= 1, is not enough to implement O_n.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.009 | 0.025 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".