A joint Euclidean mirror embeds LLM response distributions to recover manifold structure with respect to tuning parameters, enabling consistent inference of those parameters from samples.
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Discriminative factorization distinguishes high-quality query sets for black-box model classification, with chance-level error decaying exponentially in query budget and parameters predicting empirical decay rates on auditing tasks.
Adaptive control charts can monitor learning multi-agent systems but are vulnerable to gradual adversarial defection, revealing a fundamental tradeoff between allowing agents to learn and maintaining security against adversaries.
citing papers explorer
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Recovering manifold structure in LLM responses through a joint Euclidean mirror
A joint Euclidean mirror embeds LLM response distributions to recover manifold structure with respect to tuning parameters, enabling consistent inference of those parameters from samples.
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Black-box model classification under the discriminative factorization
Discriminative factorization distinguishes high-quality query sets for black-box model classification, with chance-level error decaying exponentially in query budget and parameters predicting empirical decay rates on auditing tasks.
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Control Charts for Multi-agent Systems
Adaptive control charts can monitor learning multi-agent systems but are vulnerable to gradual adversarial defection, revealing a fundamental tradeoff between allowing agents to learn and maintaining security against adversaries.