A Comparison of Knowledge-Based GBFS Enhancements and Knowledge-Free Exploration
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
GBFS-based satisficing planners often augment their search with knowledge-based enhancements such as preferred operators and multiple heuristics. These techniques seek to improve planner performance by making the search more informed. In our work, we will focus on how these enhancements impact coverage and we will use a simple technique called epsilon-greedy node selection to demonstrate that planner coverage can also be improved by introducing knowledge-free random exploration into the search. We then revisit the existing knowledge-based enhancements so as to determine if the knowledge these enhancements employ is offering necessary guidance, or if the impact of this knowledge is to add exploration which can be achieved more simply using randomness. This investigation provides further evidence of the importance of preferred operators and shows that the knowledge added when using an additional heuristic is crucial in certain domains, while not being as effective as random exploration in others. Finally, we demonstrate that random exploration can also improve the coverage of LAMA, a planner which already employs multiple enhancements. This suggests that knowledge-based enhancements need to be compared to appropriate knowledge-free random baselines so as to ensure the importance of the knowledge being used.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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