The use of awareness in collision prediction
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
Consideration is given to a world made up of a collection of objects which are all moving with respect to each other. The goal is to design a system capable of reporting predicting all possible object collisions, given that all relevant information is available in due time. Previous approaches are based on the notion of a distance function that reflects the closest distance between objects in the world at any given instant in time. Explicitly including time in the representation makes it possible to obtain an algorithm based on the shortest possible time before the next possible collision. The algorithm deals with all pairwise interactions between objects, sorts the pairs with respect to their predicted collision time, and maintains the most-likely-to-collide pairs at the top of a stack. A novel kind of hierarchy in the representation of the world is thus introduced. To find the shortest possible time before a collision, the trajectory of objects is constrained by imposing bounds on the object's acceleration and velocity. All interacting pairs are classified into buckets that reflect the imminence of the collision. The computing cost is kept constant by reclassifying only one pair from each bucket at each time sample.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.000 |
| 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