Efficient delay-tolerant particle filtering through selective processing of out-of-sequence measurements
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
This paper proposes a novel algorithm for delay-tolerant particle filtering that is computationally efficient and has limited memory requirements. The algorithm estimates the informativeness of delayed (out-of-sequence) measurements (OOSMs) and immediately discards uninformative measurements. More informative measurements are then processed using the storage efficient particle filter proposed by Orguner et al. If the measurement induces a dramatic change in the current filtering distribution, the particle filter is re-run to increase the accuracy. Simulation experiments provide an example tracking scenario where the proposed algorithm processes only 30-40% of all OOSMs using the storage efficient particle filter and 1-3% of OOSMs by re-running the particle filter. By doing so, it requires less computational resources but achieves greater accuracy than the storage efficient particle filter.
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 | 0.001 | 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