Filtering Spam Using Kolmogorov Complexity Estimates
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 introduces an adaptive filter which filters spam email based on Kolmogorov complexity estimates. The complexity filter is first trained exactly like a Bayesian filter. Each email is mapped to a string representation in which the tokens or words are represented by either 0 or 1. Tokens associated with spam are represented by 1 whereas those associated with non-spam, or ham, are represented by 0. Common tokens are ignored. The Kolmogorov complexity of this string representation is estimated using run-length compression. If the resulting Kolmogorov complexity is low then the email is classified as spam. Otherwise the email is classified as ham. The complexity filter can filter messages almost twice as fast as a comparable Bayesian filter and achieve accuracy rates of 80% to 96% While a Bayesian filter views an email as a "bag of words", the complexity filter uses token distribution information and is likely less vulnerable to statistical attack.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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