Hyperdimensional (HD) computing is attractive for low-power platforms, but common encoders flatten inputs and treat neighboring features as independent, discarding spatial structure and inflating the cost of a dense F×D apply. We present MESHHD, a spatially aware, relative and multi-scale base that maps 2D coordinates with random Fourier features to approximate a distance kernel; nearby locations receive similar hypervectors regardless of absolute position. We further introduce a compact Kronecker-structured apply that realizes the bundled base with three small GEMMs, reducing arithmetic and weight movement from O(FD) toward a near-linear form while preserving encoder semantics. Our experimental results show that MESHHD consistently improves accuracy over the state-of-the-art nonlinear HD encoders, especially at smaller D, and reduces per-batch encoding time by ~ 3×, with up to 10× savings in encoder MACs/state at D=10,000.

Scalable video retrieval is increasingly challenging as datasets reach tens of millions of videos. Current text-to-video retrieval (T2VR) methods either compress videos into single dense vectors, losing segment-level detail, or expand them into multi-frame representations, incurring prohibitive storage and search costs. We propose a binary hyperdimensional representation that encodes each video into a compact 3,072-dimension hypervector, preserving semantic fidelity while reducing memory via bit-packing. To leverage the properties of hypervectors for sublinear search, we introduce Hypervector Retrieval (HVR), a frequency-aware inverted index that prioritizes rare informative positions and refines candidates using GPU-accelerated Hamming search. Experiments show that our approach matches or exceeds dense baselines for T2VR and surpasses state-of-the-art partially relevant video retrieval (PRVR) by over 5% Recall@ 10 on ActivityNet. At scale, HVR processes over 2,000 queries per second on 10M videos, maintains recall within 1% of exact search, and achieves 5.3× greater storage capacity than CLIP4Clip and over 2,116× over MS-SL.

Retrieval-Augmented Generation (RAG) systems typically rely on dense floating-point embeddings to retrieve relevant documents, but this approach incurs significant memory and compute costs at scale. We propose a Hyperdimensional Computing (HDC) framework that projects transformer token embeddings into high-dimensional binary hypervectors, which are aggregated into compact document representations. To support sublinear search, we introduce HD-NSW, a graph-based index inspired by navigable small-world networks. HD-NSW clusters similar hypervectors into bundled centroids and connects them with sparse Hammingdistance edges, enabling efficient, beam-guided traversal entirely in the binary domain. Across 15 BEIR benchmarks and synthetic Gaussian mixture corpora, HD-NSW achieves over 99% of dense retrieval quality, reduces memory usage by $8 \times$, and supports over 860 queries per second at 10 million documents while maintaining over 80% throughput at 40 million documents. At five million documents, HD-NSW achieves $7.68 \times$ higher throughput compared to state of the art approximate nearest neighbor methods. Beyond this point, competing baselines encounter memory exhaustion, while HD-NSW continues scaling and maintains high throughput at larger corpus sizes.

We propose Matryoshka, a novel framework for transformer model pruning, enabling dynamic runtime controls while maintaining competitive accuracy to modern large language models (LLMs). Matryoshka incrementally constructs submodels with varying complexities, allowing runtime adaptation without maintaining separate models. Our evaluations on LLaMA-7B demonstrate that Matryoshka achieves up to 34% speedup and outperforms the quality of state-of-the-art pruning methods, providing a flexible solution for deploying LLMs.

Traditional federated learning frameworks, often reliant on deep neural networks, face challenges related to computational demands and privacy risks. In this paper, we present a novel Hyperdimensional (HD) Computing-based federated learning framework designed for resource-constrained mobile robots. Unlike other HD-based learning, our approach introduces dynamic encoding, which improves both model accuracy and privacy by continuously updating hypervector representations. To further address the issue of imbalanced data, especially prevalent in robotics tasks, we propose a hypervector oversampling technique, enhancing model robustness. Extensive evaluations on LiDAR-equipped mobile robots demonstrate that our oversampling method outperforms state-of-the-art HD computing frameworks, achieving up to a 22.9% increase in accuracy while maintaining computational efficiency.