Paper

Generalised Eigenvalue Geometry of Semantic Adversarial Attacks

Unreadunread

AI summary

arXiv:2606. 19212v1 Announce Type: new Abstract: Recent empirical work shows that semantically equivalent paraphrases can fool financial sentiment classifiers: although a paraphrase remains close to the original under a strong reference embedding, it may shift the target model's representation enough to change the predicted class.

Reading status

Priority

About this paper

Authors: Martin Anthony, Kaveh Salehzadeh Nobari
Released: 2026-06-18 (21d ago)
Topic: Related to current project
Relation to my work: Read next because Generalised Eigenvalue Geometry of Semantic Adversarial Attacks overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, text, phrase, class, phrases, under, soft, token. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.19212

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Generalised Eigenvalue Geometry of Semantic Adversarial Attacks. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.