The Step Complexity of Multidimensional Approximate Agreement
Bibliographic record
Abstract
Approximate agreement allows a set of n processes to obtain outputs that are within a specified distance ε > 0 of one another and within the convex hull of the inputs. When the inputs are real numbers, there is a wait-free shared-memory approximate agreement algorithm [Moran, 1995] whose step complexity is in O(n log(S/ε)), where S, the spread of the inputs, is the maximal distance between inputs. There is another wait-free algorithm [Schenk, 1995] that avoids the dependence on n and achieves O(log(M/ε)) step complexity where M, the magnitude of the inputs, is the absolute value of the maximal input. This paper considers whether it is possible to obtain an approximate agreement algorithm whose step complexity depends on neither n nor the magnitude of the inputs, which can be much larger than their spread. On the negative side, we prove that Ω(min{(log M)/(log log M), (√log n)/(log log n)}) is a lower bound on the step complexity of approximate agreement, even when the inputs are real numbers. On the positive side, we prove that a polylogarithmic dependence on n and S/ε can be achieved, by presenting an approximate agreement algorithm with O(log n (log n + log(S/ε))) step complexity. Our algorithm works for multidimensional domains. The step complexity can be further restricted to be in O(min{log n (log n + log (S/ε)), log(M/ε)}) when the inputs are real numbers.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".