Keyword-Enhanced Hierarchical Quantization Encoding

• KHQE is a quantization framework that encodes high-dimensional semantic data into compact IDs using hierarchical pipelines and keyword conditioning to retain essential features.
• It combines stochastic quantization, coarse-to-fine clustering, and mixed-precision bit allocation to optimize compression and memory efficiency.
• Empirical evaluations demonstrate significant gains in retrieval accuracy, operational cost reductions, and performance improvements across image, audio, and text modalities.

Keyword-Enhanced Hierarchical Quantization Encoding (KHQE) denotes a class of methods that encode high-dimensional semantic representations into compact, discrete tokens or “IDs,” leveraging hierarchical quantization pipelines augmented by keyword-informed enhancements. The principal aim is to maintain multi-level, collaborative, and semantic information with minimal loss while simultaneously amplifying representative, contextually salient features—particularly by conditioning quantization on core domain-specific keywords. Recent deployments demonstrate KHQE’s utility for extreme compression, semantic retrieval, scalable keyword spotting, and mixed-precision LLM memory efficiency. The following sections detail KHQE’s foundational methodologies, mathematical principles, cross-modal instantiations, and empirical results.

1. Hierarchical Quantization: Structural Overview

KHQE is predicated on hierarchical representation learning via stacked quantization modules. A canonical structure includes multiple levels, where each subsequent (higher) quantization encoder compresses the discrete representations produced by its immediate predecessor. For instance, in HQA (Williams et al., 2020), the process begins with a base VQ-VAE operating directly on input $x$:

• Base Layer: $z_e(x)$ is computed and quantized using a codebook; reconstruction loss targets the original $x$.
• Higher Layers: The encoder for layer $i$ ingests $z_e^{(i-1)}$ and generates new embeddings, which are quantized with smaller codebooks for higher compression. Crucially, higher layers reconstruct the entire continuous embedding of the previous layer under an MSE-style loss, not merely a sampled code.

This yields a Markovian series:

$$x \rightarrow z_e^{(1)} \rightarrow z^{(1)} \rightarrow z_e^{(2)} \rightarrow z^{(2)} \rightarrow \dots \rightarrow z^{(L)}$$

with only the top-layer codes transmitted for inference.

A similar hierarchical pipeline is used in HQ-VAE (Takida et al., 2023), where latent groups $Z_1, ..., Z_L$ are stochastically assigned over codebooks using a two-path encoder (multi-resolution bottom-up + top-down refinement), and quantization occurs via a variational Bayes treatment, enhancing codebook utilization and mitigating collapse.

2. Keyword-Conditioned Semantic Enhancement

Keyword enhancement in KHQE is designed to amplify business- and domain-critical semantic features. As instantiated in OneSearch (Chen et al., 3 Sep 2025), KHQE first aligns collaborative (query-item interaction) and semantic representations through a weighted sum of contrastive and relevance ranking losses:

$$\mathcal{L}_{\text{align}} = \lambda_1 \mathcal{L}_{\text{q2q}} + \lambda_2 \mathcal{L}_{\text{i2i}} + \lambda_3 \mathcal{L}_{\text{q2i}} + \lambda_4 \mathcal{L}_{\text{rank}} + \lambda_5 \mathcal{L}_{\text{rel}}$$

Subsequently, a set of core keywords $\{k_i\}$ is extracted using NER or curated lists. The text embedding $e$ is then augmented:

$$e_{\text{q}}^{o} = \frac{1}{2}(e_{\text{q}} + \frac{1}{m} \sum_{i=1}^{m} e_{k}^{i})$$

$$e_{\text{i}}^{o} = \frac{1}{2}(e_{\text{i}} + \frac{1}{n} \sum_{j=1}^{n} e_{k}^{j})$$

This operation prioritizes high-salience content and filters noise, ensuring the quantization pipeline encodes the most distinctive item or query attributes.

3. Quantization Procedures and Training Objectives

KHQE integrates stochastic quantization and multi-stage, coarse-to-fine clustering to reduce dimensionality and maintain semantic fidelity. Key steps include:

• Stochastic Quantization: For an embedding $z_e(x)$ and codebook $\{e_k\}$, the assignment probability is:

$q(z=k|x) \propto \exp(-\|z_e(x)-e_k\|^2)$

with samples drawn (for differentiability) via Gumbel Softmax and temperature annealed during training.

• Objective Function: The loss per layer (Williams et al., 2020):

$$\mathcal{L} = -\log p(x|z=k) - \mathcal{H}[q(z|x)] + \mathbb{E}_{q(z|x)}[\|z_e(x) - e_z\|^2]$$

where $\mathcal{H}$ is entropy, reducing mode collapse, and the commitment term stabilizes assignments.

• Hierarchical Clustering and Product Quantization: In OneSearch (Chen et al., 3 Sep 2025), hierarchical RQ-Kmeans encodes coarse features over codebooks of decreasing size (e.g., 4096/1024/512), while OPQ further quantizes the residual embedding post-RQ-Kmeans, securing fine-grained item distinction.

4. Bit Allocation Principles: Mixed-Precision and Key Prioritization

KHQE can be extended to memory-constrained settings in LLMs and acoustic models, leveraging mixed-precision quantization strategies informed by spectral norms. The “Key-Value Norm Disparity” theorem (Hariri et al., 20 Feb 2025) demonstrates that key matrices in KV-cache layers systematically exhibit higher norms (spectral and Frobenius) than value matrices:

$$\|A - \hat{A}\|_2 \lesssim \frac{\sqrt{mn}}{2(2^{b-1}-1)}\|A\|_2$$

Thus, under fixed bit-width, keys are more susceptible to quantization error.

To balance error:

$2^{b_K - b_V} \approx \frac{\|V\|}{\|K\|}$

This result underpins key-driven mixed-precision quantization: assign $b_K > b_V$ to achieve memory savings with negligible impact on model accuracy. Empirical studies confirm that 4-bit keys and 2-bit values preserve near-baseline performance, while the converse yields degradation (Hariri et al., 20 Feb 2025).

5. Cross-Modal Applications and Experimental Evaluation

KHQE has been validated across diverse modalities:

• Image and Audio Compression: Hierarchical quantization (HQA, HQ-VAE) shows that low-bit top-level codes suffice for high perceptual quality at extreme compression rates (as low as 9 bits for 98,304-bit images) while retaining semantics (Williams et al., 2020, Takida et al., 2023). rFID and classification error metrics on MNIST and CelebA demonstrate KHQE variants’ superiority over VQ-VAE baselines and related hierarchies. MUSHRA listening tests confirm higher perceptual quality in audio generation (Takida et al., 2023).
• Embedded Keyword Spotting: Two-stage Quantization Aware Training (QAT) (Zeng et al., 2022) enables sub–8-bit quantization of KWS models, applying tanh-based nonlinear quantization to weights and linear quantization to activations, biases, and batchnorm. This yields 3× CPU and >4× memory savings with minor accuracy loss on DET curves.
• E-commerce Generative Retrieval: In OneSearch (Chen et al., 3 Sep 2025), KHQE supplies semantic IDs for items/queries that capture hierarchical structure and domain-relevant keywords. Compared to RQ-Kmeans without keyword enhancement, the full KHQE pipeline achieves marked increases in recall@10, MRR@10, CUR, and ICR. Online A/B tests show statistically significant CTR, buyer, and order gains alongside a 75.4% reduction in operational costs and an MFU increase from 3.26% to 27.32%.

6. Relevance Maintenance and Codebook Utilization

KHQE maintains stringent query-item relevance through joint embedding alignment (contrastive/ranking losses), collaborative-training signals, and hierarchical quantization that retains unique item features even after coarse clustering. Enhanced codebook utilization rates (CUR, ICR) and improved perplexity metrics compared to deterministic models are consistently reported in HQ-VAE (Takida et al., 2023) and OneSearch (Chen et al., 3 Sep 2025). This ensures semantic IDs remain discriminative and support accurate, fine-grained retrieval or classification.

7. Limitations and Prospective Developments

KHQE’s efficacy may depend on the pre-selection of core keywords, quality of collaborative training signals, and the hyperparameterization of quantization depths and codebook sizes. Layer collapse can occur in absence of stochastic assignment or variational objectives; the HQ-VAE framework’s self-annealing quantization mitigates this (Takida et al., 2023). A plausible implication is that future work may augment KHQE with dynamic bit allocation—modulating codebook sizes, precision, or enhancement weights in response to input statistics (e.g., via per-layer norm tracking as in (Hariri et al., 20 Feb 2025)).

Research groups provide open-source implementations facilitating adaptation to LLMs, generative retrieval, and embedded streaming inference. This suggests KHQE’s widespread utility for high-perceptual-fidelity compression, efficient retrieval, and scalable memory-aware deployment under strict resource constraints.