Overlap gap and computational thresholds in the square wave perceptron
Bibliographic record
Abstract
Abstract Square wave perceptrons (SWPs) form a class of neural network models with oscillating activation function that exhibit intriguing ‘hardness’ properties in the high-dimensional limit at a fixed constraint density <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>α</mml:mi> <mml:mo>=</mml:mo> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> . In this work, we examine two key aspects of these models. The first is related to the so-called overlap-gap property , that is a disconnectivity feature of the geometry of the solution space of combinatorial optimization problems proven to cause the failure of a large family of solvers, and conjectured to be a symptom of algorithmic hardness. We identify, both in the storage and in the teacher–student settings, the emergence of an overlap gap at a threshold <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>α</mml:mi> <mml:mrow> <mml:mrow> <mml:mi>OGP</mml:mi> </mml:mrow> </mml:mrow> </mml:msub> <mml:mo stretchy="false">(</mml:mo> <mml:mi>δ</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> , which can be made arbitrarily small by suitably increasing the frequency of oscillations <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mn>1</mml:mn> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:mi>δ</mml:mi> </mml:mrow> </mml:math> of the activation. This suggests that in this small- δ regime, typical instances of the problem are hard to solve even for small values of α . Second, in the teacher–student setup, we show that the recovery threshold of the planted signal for message-passing algorithms can be made arbitrarily large by reducing δ . These properties make SWPs both a challenging benchmark for algorithms and an interesting candidate for cryptographic applications.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".