Policies for Feature Interaction Resolution
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
Telephone systems are marked by the provision of many hundreds of features. Conflict between these features is inherent as the actions of one feature can be in direct opposition to the aims of another feature. Most telecommunication service providers resolve the feature interaction problem by providing specific instructions in their management software. This approach suffers from the complexity of the resulting code and the difficulty of adding new features to the system. In this paper, we propose an agent-based architecture in which the actions of each agent are controlled by a set of policies. We also introduced the concept of fuzzy-policies, which are policies whose suitability for handling an event is calculated dynamically, based on the value of some fuzzy-variables. Conflicts are resolved using an arbitrator agent, which recalculates the suitability of the proposed actions of each agent and deduces the best action that satisfies the end user. The end user has the ability to add new policies, or modify the values of the fuzzy-parameters of the user-agent to alter the behavior of the system, thus obtaining a more personalized service.
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