Learning to Recognize Reachable States from Visual Domains
Bibliographic record
Abstract
While planning models are symbolic and precise, the real world is noisy and unstructured. This work aims to bridge the gap between noise and structure by aligning visualizations of planning states to the underlying state space structure. Further, we do so in the presence of noise and augmentations that simulates a commonly overlooked property of real environments: several variations of semantically equivalent states. First, we create a dataset that visualizes states for several common planning domains; each state is generated in a way that introduces variability or noise. E.g., objects changing in location or appearance in a manner that preserves semantic meaning. First we train a contrastive learning model to predict the underlying states from the images. We then evaluate how we can align the predictions of a given sequence of visualized states with the problem’s reachable state space, taking advantage of the known structure to improve predictions. We compare two methods for doing so: a greedy approach and Viterbi’s algorithm, a well-established algorithm for observation decoding given a hidden Markov model. The results demonstrate that these alignment methods can correct errors in the model and significantly improve predictive accuracy.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".