A Control Loop-based Algorithm for Operational Transformation
Bibliographic record
Abstract
Operational Transformation (OT) has emerged as a viable theoretical principle for the implementation of real-time collaboration applications. In such systems, the collaboration consists of operations generated by members of a group who are performing concurrent actions on the same document or content. This powerful multi-user co-editing has been researched ever since the seminal works of the late 1980s. As the web evolved into a dominant platform for content consumption and creation, classes of algorithms like OT and Conflict-free Replicated Data Types (CRDT) have enabled flexible content synchronization for applications such as online word processors. Despite their long history in academia, OT and CRDT continue to have unsolved issues due to the centralized approach required for scalable and reliable web-based document editing. This paper proposes a Control Loop-based OT approach based on a serverless architecture and on Finite State Automata (FSA). A control loop principle is used to design a series of algorithms for distributed conflict resolution. The proposed architecture consists of a series of blocks which internally contain a number of multi-level Finite State Machines. The architecture of the new serverless approach for OT is introduced and the basic FSAs that model the co-editing processes are described. Cases encountered in the dynamics of the co-editing processes were modeled to prove that the essential OT properties of causality preservation, convergence, and intention preservation are all satisfied. Simulation results are given at the end of the paper.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".