Uniform Hardness Amplification in NP via Monotone Codes
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Bibliographic record
Abstract
We consider the problem of amplifying uniform average-case hardness of languages in NP, where hardness is with respect to BPP algorithms. We introduce the notion of monotone errorcorrecting codes, and show that hardness amplification for NP is essentially equivalent to constructing efficientlylocally encodable and locally list-decodable monotone codes. The previous hardness amplification results for NP [Tre03, Tre05] focused on giving a direct construction of some locally encodable/decodable monotone codes, running into the problem of large amounts of nonuniformity used by the decoding algorithm. In contrast, we propose the indirect approach to constructing locally encodable/decodable monotone codes, combining the uniform Direct Product Lemma of [IJK06] and arbitrary, not necessarily locally encodable, monotone codes. The latter codes have fewer restrictions, and so may be easier to construct. We study what parameters are achievable by monotone codes in general, giving negative and positive results. We present two constructions of monotone codes. Our first code is a uniquely decodable code based on the Majority function, and has an efficient decoding algorithm. Our second code is combinatorially list-decodable, but we do not have an efficient decoding algorithm. In conjunction with an appropriate Direct Product Lemma, our first code yields uniform hardness amplification for NP from inverse polynomial to constant average-case hardness. Our second code, even with a brute-force decoding algorithm, yields further hardness amplification to 1/2 −log (1) n. Together, these give an alternative proof of Trevisan’s result [Tre03, Tre05]. Getting any non-brute-force decoding algorithm for our second code would imply improved parameters for the problem of hardness amplification in NP.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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