UMID infers membership in contrastive pre-training data using only text queries by performing latent inversion and comparing similarity and variability signals to synthetic gibberish references via unsupervised anomaly detection.
Reinforcement learning from multi-role debates as feedback for bias mitigation in llms
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
HunterAgent combines LLM hypothesis generation with symbolic verification and cost-bounded graph search to reconstruct attack paths under anti-forensics, reporting 86.1% mean F1 on benchmarks with reduced hallucinations.
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Membership Inference for Contrastive Pre-training Models with Text-only PII Queries
UMID infers membership in contrastive pre-training data using only text queries by performing latent inversion and comparing similarity and variability signals to synthetic gibberish references via unsupervised anomaly detection.
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HunterAgent: Neuro-Symbolic Attack Trace Reconstruction under Anti-Forensics
HunterAgent combines LLM hypothesis generation with symbolic verification and cost-bounded graph search to reconstruct attack paths under anti-forensics, reporting 86.1% mean F1 on benchmarks with reduced hallucinations.