Asynchronous Separation of Unconscious Colored Robots
Bibliographic record
Abstract
We consider the recently introduced model of autonomous computational mobile entities called unconscious colored robots.The entities are the traditional oblivious silent mobile robots operating in the Euclidean plane in Look-Compute-Move cycles.However, each robot has a permanent external mark (or color) from a finite set, visible by the other robots, but not by the robot itself.The basic problem for these robots is separation, requiring all the robots with the same color to separate from the other robots, each group forming a recognizable geometric shape (e.g., circle, point, line); this task must be performed in finite time, in spite of the robots being unconscious of their own color, unable to communicate, and oblivious.This problem has been studied and solved in the synchronous setting (SSS 2023).In this paper we show that the problem is solvable also under the more difficult asynchronous adversary, provided the robots agree on the orientation of one axis, and no robot is uniquely colored.The proof is constructive: we present a distributed algorithm that allows unconscious colored robots with one-axis agreement to separate into parallel lines under the asynchronous scheduler.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".