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Cosinegate Dynamic Routing
ASecurityAchieve efficient neural networks via self-supervised dynamic routing using Cosine Incompatibility Ratio (CIR). Ground gating decisions in geometric novelty rather than learned heuristics, enable per-sample/per-block binary routing via Gumbel-softmax, constrain with progressive FLOPs regularization—maintaining accuracy while reducing computation 28.5% on CIFAR-10.
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- Added September 9, 2026
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[](https://www.skillsdirectory.com/skills/adu2021-cosinegate-dynamic-routing)---
name: cosinegate-dynamic-routing
title: "CosineGate: Semantic Dynamic Routing via Cosine Incompatibility"
version: 0.0.2
engine: skillxiv-v0.0.2-claude-opus-4.6
license: MIT
url: https://arxiv.org/abs/2512.22206
keywords: [dynamic-routing, efficient-inference, residual-networks, gating]
description: "Achieve efficient neural networks via self-supervised dynamic routing using Cosine Incompatibility Ratio (CIR). Ground gating decisions in geometric novelty rather than learned heuristics, enable per-sample/per-block binary routing via Gumbel-softmax, constrain with progressive FLOPs regularization—maintaining accuracy while reducing computation 28.5% on CIFAR-10."
---
## Overview
Dynamic routing based on semantic novelty between identity and residual paths.
## Core Technique
**Cosine Incompatibility Ratio:**
```python
# CIR = 1 - cos(x, F(x))
# Low CIR: identity sufficient (skip block)
# High CIR: block adds novelty (execute)
cir_score = 1.0 - cosine_similarity(input_x, residual_output)
should_skip = cir_score < threshold
```
**Differentiable Gating:**
Gumbel-softmax for training, deterministic for inference.
## Performance
- 91.3% accuracy with 28.5% FLOPs saving
- No auxiliary supervision needed
## References
- Cosine incompatibility ratio for routing
- Geometric grounding of gate decisions
- FLOPs-constrained gating
Attribution
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