Learning where you are going and from whence you came: h- and g-cost learning in real-time heuristic search
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
Real-time agent-centric algorithms have been used for learning and solving problems since the in-troduction of the LRTA * algorithm in 1990. In this time period, numerous variants have been pro-duced, however, they have generally followed the same approach in varying parameters to learn a heuristic which estimates the remaining cost to arrive at a goal state. Recently, a different ap-proach, RIBS, was suggested which, instead of learning costs to the goal, learns costs from the start state. RIBS can solve some problems faster, but in other problems has poor performance. We present a new algorithm, f-cost Learning Real-Time A * (f-LRTA*), which combines both ap-proaches, simultaneously learning distances from the start and heuristics to the goal. An empiri-cal evaluation demonstrates that f-LRTA * outper-forms both RIBS and LRTA*-style approaches in a range of scenarios. 1
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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