CheckMIABench converts LLMs with intermediate checkpoints into clean MIA testbeds by using pre- and post-checkpoint training data from the same distribution and evaluates published attacks on Pythia and OLMo models while releasing an open-source library.
The distribution of the flora in the alpine zone
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Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.
BLEST is a Tensor Core-accelerated BFS framework using BVSS representation and optimized MMA layouts that reports average speedups of 22x over GAP on real-world graphs.
BiRD defends RAG systems against poisoning attacks by using a dual-signal framework that combines forward ranking for semantic relevance with backward ranking for context consistency, reducing attack success rates by up to 54% and improving accuracy by up to 56%.
Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.
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CheckMIABench: Firm Foundations For Membership Inference Attacks on Language Models
CheckMIABench converts LLMs with intermediate checkpoints into clean MIA testbeds by using pre- and post-checkpoint training data from the same distribution and evaluates published attacks on Pythia and OLMo models while releasing an open-source library.