Wenxuan Xiao

Wenxuan Xiao

Binary quantization (BQ) compresses high-dimensional embeddings into one or two bits per coordinate, enabling nearest neighbor search at extreme speed. Yet a striking puzzle persists: BQ achieves competitive recall on contrastive embeddings but fails on others -- and two leading systems adopt diametrically opposite strategies (random rotation vs. preserving coordinate axes) without a common theory explaining when each is appropriate. We resolve this puzzle by connecting the Gaussian structure recently established for InfoNCE-trained representations to a complete analytical framework for BQ quality. The key insight is that coordinate heterogeneity -- the non-uniformity of per-coordinate variances -- governs the key aspects of BQ performance. We derive closed-form expressions for ranking fidelity, prove that the magnitude bit carries information proportional to heterogeneity, and show that random rotation destroys precisely the signal that one paradigm exploits while creating the isotropy that the other requires. A two-parameter scaling law predicts fidelity across models and dimensions. Experiments on 13 datasets and 6 embedding families validate all predictions and provide the first principled design guide for binary quantization systems.

Wenxuan Xiao, Zhiyou Wang, Chengcheng Li

Approximate nearest neighbor (ANN) graph indices such as HNSW and Vamana construct their edge topology in full-precision or high-fidelity quantized metric spaces, relegating binary quantization (BQ) to a post-hoc distance estimator during search. We challenge this paradigm by asking: Can binary quantization build the graph, instead of merely accelerating graph search? We present QuIVer (Quantized Index for Vector Retrieval), a training-free ANN graph index that performs edge selection, pruning, and graph navigation entirely within a 2-bit Sign-Magnitude BQ metric space. QuIVer combines three mutually reinforcing mechanisms: (i) a 2-bit Sign-Magnitude encoding that preserves both sign and magnitude strength at 1/12 the memory of float32 vectors; (ii) Vamana alpha-diversity pruning executed directly on BQ distances, producing long-range navigational edges robust to quantization noise; and (iii) symmetric BQ beam search using only XOR/AND/Popcount, with a final float32 reranking step confined to a small candidate set. On MiniLM-1M (384-d), Cohere-1M (768-d), and DBpedia-OpenAI-1M (1536-d), QuIVer achieves >=91% Recall@10 at 16-39K QPS with 70-140-second construction and <0.9 GB hot memory -- outperforming hnswlib by ~16x and USearch HNSW by ~5x in throughput at comparable recall. Controlled experiments on six additional datasets -- including multimodal CLIP embeddings (RedCaps-512), word vectors (GloVe-100), CV features (SIFT-128, GIST-960), uniform random vectors, and a low-rank synthetic dataset -- precisely delineate QuIVer's applicability boundary: high recall requires cosine-native distributions with low effective dimensionality, while Vamana's graph reachability holds universally. Notably, multimodal CLIP embeddings achieve 78% recall at ef=64, revealing a continuous gradient between single-modality SOTA and non-contrastive usability.

Teng Liu, Yuyou Yang, Wenxuan Xiao et al.

Deep reinforcement learning (DRL) has emerged as a pervasive and potent methodology for addressing artificial intelligence challenges. Due to its substantial potential for autonomous self-learning and self-improvement, DRL finds broad applications across various research domains. This article undertakes a comprehensive comparison of several DRL approaches con-cerning the decision-making challenges encountered by autono-mous vehicles on freeways. These techniques encompass common deep Q-learning (DQL), double deep Q-learning (DDQL), dueling deep Q-learning, and prioritized replay deep Q-learning. Initially, the reinforcement learning (RL) framework is introduced, fol-lowed by a mathematical establishment of the implementations of the aforementioned DRL methods. Subsequently, a freeway driving scenario for automated vehicles is constructed, wherein the decision-making problem is reformulated as a control opti-mization challenge. Finally, a series of simulation experiments are conducted to assess the control performance of these DRL-enabled decision-making strategies. This culminates in a comparative analysis, which seeks to elucidate the connection between autonomous driving outcomes and the learning char-acteristics inherent to these DRL techniques.