Learning to Recognize Reachable States from Visual Domains
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
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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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 | 0.001 |
| 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 | 0.004 |
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