New Algorithms and Methods for Collaborative Co-Editing Using HTML DOM Synchronization
Bibliographic record
Abstract
The optimistic consistency control method known as Operational Transformation (OT) has been studied by researchers for nearly three decades, with centralized versions lying at the heart of most real-time web co-editing tools in academia and industry. Concurrent document editing is now a "must-have" for the modern workplace, with proven benefits in team productivity and efficiency. Once limited to primitive insert and delete operations, OT algorithms have evolved to support hierarchical data structures such as XML in order to meet the increasingly complex requirements of present-day collaborative applications. However, previous approaches have not focused on the changes that web applications enact upon the Document Object Model (DOM) of the Hypertext Markup Language (HTML) standard. This paper will present a feedback-based real-time architecture that allows arbitrary DOM-based document replicas to remain consistent by defining a new set of operations that preserve the user's editing intentions. The control loop of the architecture enables simultaneous DOM-based modifications by using novel conflict resolution algorithms and methods that bring "Virtual DOM" concepts together with state-of-the-art OT principles to enable advanced operations such as moving, splitting and merging of hierarchical DOM nodes. Through the implementation and evaluation of a rich-text editor, it will be shown how the architecture facilitates and accelerates the development of multi-user interactive web applications that meet today's demanding latency, scalability and accessibility requirements.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".