A New Adaptive Indexing for Real-Time Web Search
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
Adaptive indexing is an alternative to the self-tuning methods. It is especially useful in the scenario of unpredictable workload, and there is no idle time to invest in index creation. The authors present their ongoing work on a new realistic adaptive indexing that transforms the previous data crawling offline approach to a data-driven online approach. The proposed approach consists of three tasks: topic prediction, resource selection, and results combination and ranking. They work simultaneously to retrieve highly relevant results to the user's query in real time. To make the index highly refreshed and up-to-date, they collected data from highly prominent resources (e.g., Facebook, Twitter, Wikipedia, etc.). The empirical results showed that the proposed model is better than the traditional models that work offline and spend hours or days for building the index in different periods. In addition, the experiments showed that the training results are highly relevant for adhoc and diversity tasks.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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