DisSLib: CC: A Library for Distributed Search with a Central Common Search State
Bibliographic record
Abstract
Distributed search is important for finding solutions to hard problems in artificial intelligence. Building distributed search systems can be difficult because the steps required to solve these problems are interdependent. Fortunately, aspects of search systems exhibit commonalities that allow them to be distributed using several different paradigms. These paradigms can be used as the basis for libraries to make implementing new systems, or distributing existing systems, easier. DisSLib:CC is such a library. DisSLib:CC implements the distributed search paradigm search with a central common search state. In this paradigm, agents collaborate to update a central search state. Systems built with DisSLib:CC require very little extra code to implement compared to the standalone versions. These systems can also show improvements in wall-clock run-times, which can be improved by varying the meta-parameters of the distribution paradigm. One such parameter is the number of steps, transitions, that the agents execute before consulting the common search state and in this paper we show how varying this meta-parameter improves the efficiency of the systems.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.105 | 0.065 |
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".