Large language models often produce plausible numerical claims from financial filings while using the wrong reporting period, unit, line item, or formula. Verifying such claims requires more than rechecking arithmetic: a verifier must ground the relevant facts, establish the authorized calculation, and determine whethe...
Bethel Hall, Sachi Shome, William Eiers· 1 citation
Lossy compression is widely used in Federated Learning (FL) but is generally treated as an error source, while conventional poisoning defenses inspect update geometry. In this work, we instead treat the compressor's response as a security signal: the input-dependent distortion and payload behavior induced by lossy comp...
RAISE is introduced, which trains policy synthesizers from formal verification in two stages, verified supervised fine-tuning (SFT) followed by a reinforcement learning (RL) stage that learns from verifier signal, and it is found that SFT succeeds largely by letting models express authorization logic they already have.
Ying-Ming Zhou, Adarsh Vatsa, William Eiers· 0 citations
Large Language Models are increasingly used to turn natural-language requirements into code. In access control, that shortcut is dangerous: a generated policy can compile and read correctly while granting access that no one approved. The difficulty is not only writing policy code. It is fixing what the requirements mea...
Adarsh Vatsa, Sachi Shome, Yingming Zhou et al.· 1 citation
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