Beso Mikaberidze | Searcharxiv

arXiv2026

GradNet: A Gradient-Based Framework for Optimal Network Science

Network science has traditionally examined how structure determines dynamics. Here we invert this paradigm: we ask how functional dynamics and resource constraints shape network architecture. We introduce GradNet, an AI-enabled optimization framework that treats network topology as a continuously differentiable object. This allows designing networks that optimize arbitrary dynamical objectives, from synchronization to communication capacity, under realistic constraints. Applying this framework across diverse systems reveals that canonical network features emerge spontaneously from constrained optimization rather than requiring explicit imposition. Optimizing Kuramoto oscillator synchronization under fixed coupling budgets produces sparse, bipartite, frequency-disassortative architectures that eliminate classical synchronization thresholds. Minimizing social tension in opinion dynamics reproduces the empirically observed factional split in Zachary's karate club network. Maximizing entanglement distribution in spatial quantum networks under distance-dependent costs recovers minimum spanning tree architectures. These results demonstrate that optimization acts as both an engineering tool for network design, scalable to networks exceeding $10^5$ nodes, and a scientific probe revealing fundamental structure-function relationships. By recasting network architecture as the solution to constrained optimization problems, this variational perspective offers a unified framework connecting network analysis, design, and inference across physical, biological, and technological systems.

physics.soc-ph↗

arXiv2026

Cross-Prompt Encoder for Low-Performing Languages

Soft prompts have emerged as a powerful alternative to adapters in parameter-efficient fine-tuning (PEFT), enabling large language models (LLMs) to adapt to downstream tasks without architectural changes or parameter updates. While prior work has focused on stabilizing training via parameter interaction in small neural prompt encoders, their broader potential for transfer across languages remains unexplored. In this paper, we demonstrate that a prompt encoder can play a central role in improving performance on low-performing languages - those that achieve poor accuracy even under full-model fine-tuning. We investigate a lightweight encoder paired with multi-source training on typologically diverse languages. We call this architecture-training combination the Cross-Prompt Encoder (XPE), and show that it advances the capture of abstract, transferable patterns across languages. To complement XPE, we propose a Dual Soft Prompt mechanism that combines an encoder-based prompt with a directly trained standard soft prompt. This hybrid design proves especially effective for target languages that benefit from both broadly shared structure and language-specific alignment. Text classification experiments with a transformer encoder (XLM-R) on the SIB-200 benchmark reveal a consistent trade-off: XPE is most effective for low-performing languages, while hybrid variants offer broader adaptability across multilingual settings.

cs.CL↗