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cs.LG2026
A Geometric Invariant of Attention Predicts Head Specialization from Weights Alone
Miguel P. Bento
Transformers are built with continuous symmetries in the attention mechanism. We derive , a scalar invariant that measures each head's capacity to couple to rotary positional en…
cs.LG2026
GaugeQuant: Online Learning of Quantization-Optimal Bases from LLM Symmetries
Miguel P. Bento, João F. Seabra
Transformers are known to have internal continuous symmetries that leave outputs invariant while altering how activations are distributed across coordinates, and hence how well the…
cs.LG2026
MUSE: Multi-Tenant Model Serving With Seamless Model Updates
Cláudio Correia, Alberto E. A. Ferreira, Lucas Martins +7
In binary classification systems, decision thresholds translate model scores into actions. Choosing suitable thresholds relies on the specific distribution of the underlying model…