Bibliographic record
Abstract
In a seminal work, Golab et al. showed that a randomized algorithm that works with atomic objects may lose some of its properties if we replace the atomic objects that it uses with linearizable objects. It was not known whether the properties that can be lost include the important property of termination (with probability 1). In this paper, we first show that, for randomized algorithms, termination can indeed be lost. Golab et al. also introduced strong linearizability, and proved that strongly linearizable objects can be used as if they were atomic objects, even for randomized algorithms: they preserve the algorithm's correctness properties, including termination. Unfortunately, there are important cases where strong linearizability is impossible to achieve. In particular, Helmi et al. MWMR registers do not have strongly linearizable implementations from SWMR registers. So we propose a new type of register linearizability, called write strong-linearizability, that is strictly stronger than linearizability but strictly weaker than strong linearizability. We prove that some randomized algorithms that fail to terminate with linearizable registers, work with write strongly-linearizable ones. In other words, there are cases where linearizability is not sufficient but write strong-linearizability is. In contrast to the impossibility result mentioned above, we prove that write strongly-linearizable MWMR registers are implementable from SWMR registers. Achieving write strong-linearizability, however, is harder than achieving just linearizability: we give a simple implementation of MWMR registers from SWMR registers and we prove that this implementation is linearizable but not write strongly-linearizable. Finally, we prove that any linearizable implementation of SWMR registers is necessarily write strongly-linearizable; this holds for shared-memory, message-passing, and hybrid systems.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".