Design of a proposal to improve the quality of information in the RUT of natural persons, registered by self-management on the DIAN platform, during the first quarter of the year 2025, through the application of Artificial Intelligence tools (AI Agents)
Bibliographic record
Abstract
El presente estudio aborda las necesidades de optimización en los procesos de inscripción autogestionada del Registro Único Tributario (RUT) para personas naturales a través de la plataforma MUISCA, administrada por la Dirección de Impuestos y Aduanas Nacionales (DIAN). La problemática central radica en la significativa tasa de errores en los registros, donde aproximadamente entre el 96% y 98% presenta inconsistencias o datos incorrectos, lo que afecta tanto la eficiencia operativa como la experiencia del usuario, esta ineficiencia es la que la "IA agéntica" busca resolver, al permitir una "autonomía operativa" y una "precisión" sin precedentes en procesos complejos. Para enfrentar este desafío, se propone una estrategia donde se implementan herramientas de Inteligencia Artificial (agentes IA.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".