Specifying and solving symbolic and numeric temporal constraints
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
Representing and solving combinatorial problems, especially those including temporal constraints, using a constraint programming language remains a challenging task. In this paper, we present a tool to assist users in specifying and solving problems under qualitative and quantitative temporal const raints. The tool is based on the TemPro framework that has the ability to manage both numeric and symbolic temporal constraints within a unique model. Our tool provides a generic template that can be specialized to describe a wide variety of temporal constraint applications. Given a problem under temporal constraints, the proposed tool with its friendly graphical user interface first assists the user in the different steps of the problem specification. The graph representation of the temporal constraint problem and its consistent scenarios are then automatically generated and visualized during the solving phase. The user has also the ability to add or remove some constraints and see the effects of these changes on the consistency of the problem.
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