FASTER AGREEMENT VIA A SPECTRAL METHOD FOR DETECTING MALICIOUS BEHAVIOR
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. We address the problem of Byzantine agreement, to bring processors to agreement on a bit in the presence of a strong adversary. This adversary has full information of the state of all processors, the ability to control message scheduling in an asynchronous model, and the ability to control the behavior of a constant fraction of processors which it may choose to corrupt adaptively. In 1983, Ben-Or proposed an algorithm for solving this problem with expected exponential amount of communication. In 2013, the algorithm was improved to expected polynomial communication time, but still an exponential amount of computation per individual processor was required. In this paper, we improve that result to require both expected polynomial computation and communication time. We use a novel technique for detecting malicious behavior via spectral analysis. In particular, our algorithm uses coin flips from individual processors to repeatedly try to generate a fair global coin. The corrupted processors can bias this global coin by generating biased individual coin flips. However, we can detect which processors generate biased coin flips by analyzing the top right singular vector of a matrix containing the sums of coin flips generated by each processor. Entries in this singular vector with high absolute value correspond to processors that are trying to bias the global coin, and this information can be used to blacklist malicious processors.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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