arXiv:2511.14763v3 Announce Type: replace-cross Abstract: Membership Inference Attack (MIA) aims to determine whether a specific data sample was included in the training dataset
arXiv:2607.16207v1 Announce Type: new Abstract: Membership inference attacks (MIAs) test whether a candidate example appeared in a model's training data. We study MIAs for
arXiv:2604.13206v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly integrated into agentic workflows, their unpredictability stemming from numerical
arXiv:2605.15208v1 Announce Type: cross Abstract: Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for
arXiv:2603.22303v1 Announce Type: cross Abstract: Hallucinations in large language models (LLMs) remain a central obstacle to trustworthy deployment, motivating detectors that are
arXiv:2511.08905v3 Announce Type: replace-cross Abstract: Given the high cost of large language model (LLM) training from scratch, safeguarding LLM intellectual property (IP) has
arXiv:2604.19301v1 Announce Type: new Abstract: The conformity bias exhibited by large language models (LLMs) can pose a significant challenge to decision-making in LLM-based
arXiv:2609.04665v1 Announce Type: new Abstract: Self-evolving language models improve by proposing candidate updates and keeping whatever raises a visible score. When that score
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