Agentic AI for Mission Adaptation: A Distributed Cognition Framework for Air Force Operations
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
Modern air combat operations demand rapid, decentralized decision-making under uncertainty and communication constraints. This paper proposes an agentic AI framework for combat triage and mission adaptation, grounded in distributed cognition. The architecture fuses multimodal data from drones, wearables, and battlefield sensors to assess injury severity, threat levels, and mission status in real time. We implement survival regression models and triage consistency checks trained on large-scale EMS data (NEMSIS), alongside reinforcement learning agents that simulate UAV coordination, casualty evacuation timing, and dynamic route adaptation in adversarial conditions. Human-machine interfaces-such as AR displays and triage conflict alerts-provide explainable, missioncritical recommendations to medics, pilots, and commanders. This research advances a scalable, deployable model of AI-human teaming for the Air Force, supporting distributed mission command and medical autonomy in contested environments.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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