Hamiltonian Transformer with norm-preserving attention and phase embeddings outperforms baselines in RF fingerprinting on WiSig dataset, reaching 99.12% same-day accuracy and 61.64% at 150 transmitters.
Gomez, Łukasz Kaiser, and Illia Polosukhin
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Proposes a probabilistic framework for latent agentic substructures in DNNs using log-score utilities and log pooling, with proofs on unanimity and an application to persona emergence in LLM alignment.
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Hamiltonian-Inspired Attention Mechanism for Scalable RF Transmitter Fingerprinting
Hamiltonian Transformer with norm-preserving attention and phase embeddings outperforms baselines in RF fingerprinting on WiSig dataset, reaching 99.12% same-day accuracy and 61.64% at 150 transmitters.
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Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks
Proposes a probabilistic framework for latent agentic substructures in DNNs using log-score utilities and log pooling, with proofs on unanimity and an application to persona emergence in LLM alignment.