Intelligent Mobile Agents in the Military Domain
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
Lockheed Martin Advanced Technology Laboratories has been designing and implementing intelligent, mobile agent prototypes for various military applications since 1995. Through our experience working in the military domain, we have identified a number of agent capabilities that are common themes in many of our applications, including information push, information pull, and sentinel information monitoring. We have implemented reusable agent components to enable rapid development of agent-based applications, where information push, information pull, and sentinel information monitoring are desired behaviors. We have gained a number of valuable insights about mobile agent development, deployment, and agent capability requirements in military applications. These lessons learned can be applied to other domains that have characteristics similar to the military applications on which we have worked. These characteristics include constraints on network reliability and bandwidth; domain-dependent information processing; and complex, autonomous information processing involving large heterogeneous data resources.
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) | 0.078 | 0.001 |
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