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Lucía M. Cabrera

Publications and source records attributed to Lucía M. Cabrera.

2 recordsLinked to original sources

Stringy Gauge Structures

A field theory action, able to incorporate stringy gauge symmetry enhancement-breaking effects, which occur at particular points of moduli space in a heterotic string toroidal compactification framework, was previously constructed from three-point interactions and duality symmetries. Via doubled periodic coordinates and a non-commutative product, it encodes the non-Abelian structure in a background-independent form. Moduli-space dependence becomes manifest upon performing a generalized mode expansion. Here we further elaborate on this construction. In particular, we propose field transformations for the retained sectors that make up the foundations of a potential symmetry of the action and that, when mode expanded at enhancement points in moduli space, contain the usual non-Abelian gauge transformations. We also examine the covariance of the corresponding scalar derivative structures. Although the proposal must be regarded as a truncated version of a full, presently unknown field-theory description, it already encodes representations of massless and massive states containing up to one oscillator excitation. Finally, we relate the construction to gauged DFT with an extended tangent space by associating generalized Kaluza-Klein modes considered here with additional generalized-frame directions. At enhancement, their brackets reproduce the enhanced gauge algebra; away from enhancement, the same algebraic structure is recovered by rotating the Cartan basis.

hep-th

Too long; didn't solve

Mathematical benchmarks consisting of a range of mathematics problems are widely used to evaluate the reasoning abilities of large language models, yet little is known about how their structural properties influence model behaviour. In this work, we investigate two structural length variables, prompt length and solution length, and analyse how they relate to model performance on a newly constructed adversarial dataset of expert-authored mathematics problems. We find that both prompt and solution lengths correlate positively with increased model failure across models. We also include a secondary, exploratory analysis of cross-model disagreement. Under a difficulty-adjusted normalised analysis, both variables retain weak negative associations with realised model separation, slightly stronger for prompt length. Overall, our main robust finding is that structural length is linked to empirical difficulty in this dataset.

cs.AI