Compute DLR-style logit margin losses for APGD-DLR adversarial optimization checks.
Scanned 9/9/2026
Install to Claude Code
Skills Directorynpx -y skills add VectorSpaceLab/AREX-Skill --skill dlr_margin_loss --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: dlr_margin_loss
description: Compute DLR-style logit margin losses for APGD-DLR adversarial optimization checks.
---
# DLR Margin Loss
Use this skill when an adversarial attack needs the AutoAttack DLR loss or when validating that a proxy attack preserves APGD-DLR semantics. Do not use it for binary classifiers or tasks without ordered class logits.
## Inputs
- A logit vector with at least three classes.
- The true class index.
## Outputs
- Scalar untargeted DLR loss.
- Ranking diagnostics from the helper script.
## Workflow
1. Sort logits while retaining the true-class position.
2. Identify the strongest non-true competitor.
3. Normalize the true-versus-competitor margin by the spread between top and third logits.
4. Maximize this loss during untargeted APGD.
## Validation
Run `python tests/test_dlr.py` or the generated skill validator.
## Limitations
This pure-Python helper is intended for deterministic small examples and mirrors the untargeted loss needed by the recovery harness.
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