MS-Lite: A Lightweight, Complementary Merge-and-Shrink Method
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
Merge-and-shrink is a general framework for creating abstraction heuristics. In this paper we present two new variations of merge-and-shrink: MS-lite and DM-HQ. MS-lite is an extremely fast merge-and-shrink that maintains only the smallest abstractions that preserve local heuristic information. MS-lite has complementary strength over other merge-and-shrink methods due to its efficiency. In addition, we show that MS-lite has little dependence on merging strategies and its eager shrinking strategy can lead to better heuristics for some planning tasks. DM-HQ features a merging criterion that utilizes information about heuristic quality to make the merging decisions. Our experiments show that combining DM-HQ and MS-lite dramatically outperforms the current state-of-the-art merge-and-shrink method by solving 75 more tasks on an International Planning Competition (IPC) benchmark set of 1499 tasks.
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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