📄

Paper

Dynamic Adversarial Fine-Tuning Reorganizes Refusal Geometry

by Wenhao Lan arxiv/2604.27019

Free2AITools Nexus Index

38.5

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0

P: Popularity 0

R: Recency 92

Q: Quality 60

Tech Context

Vital Performance —

Safety-aligned language models must refuse harmful requests without broad over-refusal, but it remains unclear how dynamic adversarial fine-tuning changes refusal-control carriers: Kullback--Leibler (KL)-constrained directions or small subspaces that causally modulate refusal without large safe-prompt distribution shifts. We study a 7B backbone under supervised fine-tuning (SFT) and Robust Refusal Dynamic Defense (R2D2), aligning HarmBench, StrongREJECT, and XSTest evaluations with five-ancho...

Source →

- Citations

Paper Information Summary
Entity Passport
Registry ID	2604.27019
License	arXiv
Provider	arxiv

📜

Cite this paper

Academic & Research Attribution

BibTeX

@misc{arxiv_2604_27019,
  author = {Wenhao Lan},
  title = {Dynamic Adversarial Fine-Tuning Reorganizes Refusal Geometry Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2604.27019}},
  note = {Accessed via Free2AITools.}
}

APA Style

Wenhao Lan. (2026). Dynamic Adversarial Fine-Tuning Reorganizes Refusal Geometry [Paper]. Free2AITools. https://arxiv.org/abs/2604.27019

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0

Popularity (P) 0

Recency (R) 92

Quality (Q) 60

💬 Index Insight

FNI V2.0 for Dynamic Adversarial Fine-Tuning Reorganizes Refusal Geometry: Authority (A:0), Popularity (P:0), Recency (R:92), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"Safety-aligned language models must refuse harmful requests without broad over-refusal, but it remains unclear how dynamic adversarial fine-tuning changes refusal-control carriers: Kullback--Leibler (KL)-constrained directions or small subspaces that causally modulate refusal without large safe-prompt distribution shifts. We study a 7B backbone under supervised fine-tuning (SFT) and Robust Refusal Dynamic Defense (R2D2), aligning HarmBench, StrongREJECT, and XSTest evaluations with five-ancho..."

❝ Cite Node

@article{Lan2026Dynamic,
  title={Dynamic Adversarial Fine-Tuning Reorganizes Refusal Geometry},
  author={Wenhao Lan},
  journal={arXiv preprint arXiv:2604.27019},
  year={2026}
}

👥 Collaborating Minds

Wenhao Lan

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📅1970Published

⏱️92RecencyFNI pillar

✅60QualityFNI pillar

🗂️cs.LGField

📄 Data Source: arXiv ↗

🔄 Updated daily

Source summary: Based on arXiv metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: 2604.27019
slug: 2604.27019
source: arxiv
author: Wenhao Lan
license: arXiv
tags: arxiv:cs.LG, arxiv:cs.CL, arxiv:cs.CR, gan

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: null
forks: null

Data indexed from public sources. Updated daily.