Published in arXiv preprint arXiv:2608.28853, 2026
ESNN learns directed, matrix-valued transport between neighbouring vector features while preserving exact Euclidean equivariance. It captures radial and tangential geometric interactions, supports controlled symmetry relaxation, and improves performance across dynamics, mesh simulation, point clouds, and molecular-property prediction.
Published in arXiv preprint arXiv:2605.10414, 2026
GAPE (Gated Adaptive Positional Encoding) addresses core limitations of RoPE in long-context language models. A content-aware bias is injected directly into attention logits while preserving rotary geometry: query-dependent and key-dependent gates suppress irrelevant distant tokens while protecting salient context, improving attention sharpness and long-context performance on retrieval and standard benchmarks.
Published in arXiv preprint arXiv:2512.00242, 2025
ArXiv preprint on Polynomial Neural Sheaf Diffusion: A Spectral Filtering Approach on Cellular Sheaves.
Published in arXiv preprint arXiv:2409.08036, 2024
HetSheaf is a cellular-sheaf framework for heterogeneous graphs that encodes node and edge types through type-aware local feature spaces and learned restriction maps, without specialised architectural components. The companion SheafPool readout is invariant to basis changes and enables graph-level prediction. Gains of up to +2 pp on the Heterogeneous Graph Benchmark with up to 10× fewer parameters.
Published in CVPR (Computer Vision and Pattern Recognition) 2025 Workshops (Nashville, USA 🇺🇸), 2025
Z-SASLM introduces a zero-shot, fine-tuning-free approach to style alignment in diffusion models by blending multiple reference styles directly in latent space using spherical linear interpolation (SLI) with learned, context-aware weights. The method avoids model retraining, preserves content semantics, and yields consistent style transfer across prompts and seeds.