The Bench Transition System and Stochastic Exploration
Bibliographic record
Abstract
Stochastic exploration has been shown to be an effective way to mitigate the negative impact that heuristic local minima and plateaus can have on Greedy Best First Search (GBFS). Previous work has induced exploration using type systems, which typically partition the state-space using simple features like heuristic value and depth. In this work, we introduce new type systems motivated by the Bench Transition System (BTS). The BTS is a structure used to characterize the behaviour of GBFS, that is based on high water-mark benches, which are sets of states that have made the same amount of progress towards the goal. Since the BTS cannot be constructed during search, our type systems approximate the BTS using the notions of Heuristic Improvement and Low Water-Mark. We first identify that these approximations are exact in state-spaces with plateaus but no local minima, and also show that the resulting type systems are probabilistically complete. Our empirical evaluation shows the effectiveness of this approach on a variety of planning domains.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".