Experimental Demonstrations of CATL, the Collaborative Autonomy Tasking Layer
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
This paper describes the continued efforts on the development of and experiments conducted with CATL (Collaborative Autonomy Tasking Layer). CATL aims to enable collaborative autonomy between heterogeneous nodes in communications-limited environments. An example application of CATL would be the collaboration between autonomous underwater vehicles (AUVs). To demonstrate feasibility, CATL was used in multiple simulated and real-life experiments, enabling a large number of multinational parties to share information and cooperate using their autonomous vehicles. The group involved institutes from Australia, Belgium, Canada, France, Germany, Italy, the Netherlands, Poland, Portugal, the United Kingdom, the NATO Centre for Maritime Research and Experimentation (CMRE). These institutions worked together in various experiments, demonstrating CATL in use cases for Naval Mine Counter-Measures (MCM), Anti-Submarine Warfare (ASW) and Submarine Escape and Rescue (SMER)
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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