Applying adaptation spaces to support quality of service and survivability
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
Adaptation is a key technique in constructing survivable information systems. Allowing a system to continue running, albeit with reduced functionality or performance, in the face of reduced resources, attacks, or broken components is often preferable to either complete shutdown or continued normal operation in compromised mode. However, unpredictable adaptation can sometimes be worse than the problem it seeks to cope with. In this paper we introduce adaptation spaces, which precisely and predictably specify the adaptation of a software component. We then present two survivable systems that have been specified and implemented using adaptation spaces. The first example uses user preferences regarding quality in an audio application to guide the adaptation when available bandwidth decreases. The second trades off performance overhead with intrusion resistance for "stack-smashing" attacks. We formally define an adaptation space and show briefly how it enables certain kinds of reasoning about adaptive applications. We conclude with related work and future plans.
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 | 0.001 | 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