USING SCATTERED MOBILE AGENTS TO LOCATE A BLACK HOLE IN AN UN-ORIENTED RING WITH TOKENS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A black hole in a network is a highly harmful host that disposes of any incoming agents upon their arrival. Determining the location of a black hole in a ring network has been studied when each node is equipped with a whiteboard. Recently, the Black Hole Search problem was solved in a less demanding and less expensive token model with co-located agents. Whether the problem can be solved with scattered agents in a token model remains an open problem. In this paper, we show not only that a black hole can be located in a ring using tokens with scattered agents, but also that the problem is solvable even if the ring is un-oriented. More precisely, first we prove that the black hole search problem can be solved using only three scattered agents. We then show that, with K (K ⩾ 4) scattered agents, the black hole can be located in O(kn + n log n) moves. Moreover, when K (K ⩾ K) is a constant number, the move cost can be reduced to O(n log n), which is optimal. These results hold even if both agents and nodes are anonymous.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 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 it