Finding state similarities for faster planning
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
In many planning applications one can find actions with overlapping effects. If for optimally reaching the goal all that matters is within this overlap, there is no need to consider all these actions – for the task at hand they are equivalent. Using this structure for speed-up has previously been proposed in the context of least commitment planning. Of a similar spirit is the approach for improving best-first search based planning we present here: intuitively, given a set of start states, reachable from the initial state, we plan in parallel for all of them, exploiting the similarities between them to gain computational savings. Since the similarity of two states is problem specific, we explicitly infer it by regressing all relevant entities, goal, heuristic function, action preconditions and costs, over the action sequences considered in planning. If the resulting formulae mention only fluents whose values the two states have in common, it suffices to evaluate the formulae in one of them. This leads to computational savings over conventional best-first search.
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 | 0.000 | 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 | 0.000 | 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