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
Humans and animals have learned or evolved to use magnetic fields for navigation. Knowing how to model and estimate these fields can be used for motion planning. However, computing the propagation of electromagnetic fields in a given environment requires solving complex differential equations with advanced numerical methods, and therefore it is not suitable for real-time decision making. In this latter, we present a real-time approximator for Maxwell's equations based on deep neural networks that predicts the distribution of a virtual magnetic field. We show how our approximator can be used to perform autonomous 2D navigation tasks, outperforming state-of-the-art navigation algorithms, ensuring completeness, and providing a near-optimal path up to 200 times per second without any post processing stage. We demonstrate the effectiveness of our method with physics-based simulations of an unmanned aerial vehicle, an autonomous car, as well as real-world experiments using a small off-road autonomous racing vehicle. Furthermore, we show how the approach can be applied to multi-robot systems, video game technology, and can be extended to 3D problems.
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