Bibliographic record
Abstract
In this paper we consider the map construction prob-lem in the case of an anonymous, unoriented torus of unknown size. An agent that can move from node to neighbouring node in the torus is initially placed in an arbitrary node and has to construct an edge-labeled map. In other words, it has to draw, in its local mem-ory, an edge-labeled torus isomorphic to the one it is moving on. The agent has enough local memory to represent the torus and one or two tokens that can be dropped on and picked up from nodes. Efficiency is measured in terms of number of moves performed by the agent. When the agent has no token available, the problem is clearly unsolvable. In the paper we show that, when the agent has one token available there exists an opti-mal algorithm for constructing the map of the torus; the agent, in fact, performs Θ(N) moves (where N is the number of nodes of the torus). Before showing the optimal solution with the optimal number of tokens, we describe a simpler solution that works when two to-kens are available, we then modify it to obtain the same bound when the agent has only one token available.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".