Interactive conflict detection and resolution for personalized features
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
In future telecommunications systems, behaviour will be defined by inexperienced users for many different purposes, often by specifying requirements in the form of policies. The call processing language (CPL) was developed by the IETF in order to make it possible to define telephony policies in an Internet telephony environment. However, user-defined policies can hide inconsistencies or feature interactions. In this paper, a method and a tool are proposed to flag inconsistencies in a set of policies and to assist the user in correcting them. These policies can be defined by the user in a user-friendly language or derived automatically from a CPL script. The approach builds on a pre-existing logic programming tool that is able to identify inconsistencies in feature definitions. Our new tool is capable of explaining in user-oriented terminology the inconsistencies flagged, to suggest possible solutions, and to implement the chosen solution. It is sensitive to the types of features and interactions that will be created by naive users. This tool is also capable of assembling a set of individual policies specified in a user-friendly manner into a single CPL script in an appropriate priority order for execution by telecommunication systems.
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