Search: “unlearning”

2 stories

Searchkeyword smart Sortlatest hot importance relevance

DomainAll Cyber AIWindow24h 3d 7d 30d AllIn the wild Important only Show duplicates

CategoryAll Vulnerability 4 Ransomware 2 AI safety & security 2 Malware 2 Research 2 AI research 2 Exploit / PoC 2 Data breach 2 Threat actor 1 Phishing & fraud 1 Policy & legal 1

SeverityAll critical 6 high 5 medium 3 low 2 info 5

Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs

Researchers introduce FOM-UL, a layer-selective machine unlearning framework that improves forgetting-utility trade-offs and resists knowledge recovery after quantization.

FOM-UL selects transformer layers for unlearning using a forget-to-retain significance score, concentrating parameter updates on layers highly influential for the forget set while leaving most of the model unchanged. It reduces residual memorization versus GA, NPO, KLD, SURE, ReLearn, and LUNAR-based baselines on TOFU, KnowUnDo, and MUSE-style evaluations while preserving retain-set utility. Under 8-bit and 4-bit post-training quantization and adversarial prompts, it maintains stronger suppression of forgotten content, addressing brittleness of diffuse unlearning updates.

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:50 UTC · 7d agoAI research1

Negative Self-Distillation: Learning to Reason by Avoiding Flaws

Researchers propose Negative Self-Distillation (NSD), a label-free LLM self-improvement method that diverges from self-generated flawed reasoning rather than imitating privileged solutions.

The authors show On-Policy Self-Distillation can degrade complex reasoning by forcing imitation of artificially confident traces built on privileged information, suppressing uncertainty and self-correction. NSD instead generates a question-specific negative condition — such as acting as a 'careless reasoner' — and pushes the model's distribution away from it without ground-truth labels. A dynamic gating mechanism isolates reasoning-critical tokens so gradient updates fix behavioral flaws without damaging foundational linguistic capabilities. NSD consistently outperforms OPSD and other label-free, self-bootstrapping reinforcement learning baselines.

Hugging Face daily papersSep 9, 20:00 UTC · 7d agoAI research1