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#artificial intelligence Preprint Sep 2026

Greedy Decoding Is Not Precision-Invariant: Cross-Precision Output Divergence in LLM Inference

An empirical error-propagation analysis is developed and finds that 22 layers of accumulated body error do not distinguish flipping from non-flipping steps; the outcome depends primarily on the top-two logit margin at the LM head relative to the directional perturbation between the top-two candidates.

Gao-Yuan Du, A. Khan, Rex Zhou et al. · 0 citations
#machine learning Preprint Sep 2026

Hearing the Whispers: Black-Box Membership Inference Attacks on Finetuned TTS Models

This work presents the first black-box MIA framework explicitly tailored to TTS models at both the speaker and record levels, and characterize the feasible query space and establish two criteria, scorable extent and memorization elicitation, for evaluating five representative queries.

Kun-Lin Cai, Kai-Yuan Zhang, Zihang Xiang et al. · 0 citations

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