Temporal Query-Based Multi-Head Attention

• Temporal Query-Based Multi-Head Attention is a framework that adapts transformer attention to process time-indexed data efficiently by mitigating quadratic complexity.
• Memory and computational optimizations like Multi-Query and Sparse Query Attention reduce overhead by sharing projections and selectively reducing query heads.
• Innovations such as Multi-Head Temporal Latent Attention and collaborative head sharing enable faster decoding and lower GPU memory usage for long-context applications.

Temporal Query-Based Multi-Head Attention refers to the family of architectural and algorithmic techniques that adapt the standard multi-head attention mechanism in transformers to better capture, process, and scale with temporal queries—i.e., tokens or representations indexed by time—especially in tasks with long or structured temporal sequences. Such approaches aim to optimize computational complexity, memory usage, or expressivity by varying how queries, keys, values, heads, and their interactions are modeled, shared, or selected. A central motivation is the quadratic scaling of vanilla multi-head attention with sequence length, which presents barriers to efficient processing of temporal data in LLMs, speech recognition, time series analysis, and sequence modeling in general.

1. Conventional Multi-Head Attention and Temporal Queries

Standard multi-head attention projects input tokens $X$ into $h$ sets of queries, keys, and values via learned matrices:

$$Q^{(h)} = X P_q^{(h)},\quad K^{(h)} = X P_k^{(h)},\quad V^{(h)} = X P_v^{(h)}$$

Each head computes scaled dot-product attention:

$$O^{(h)} = \operatorname{softmax}(Q^{(h)} {K^{(h)}}^\top / \sqrt{d}) V^{(h)}$$

Temporal queries arise naturally when $X$ has a temporal axis (e.g., speech frames, time steps in time series, document positions). In this framework, each head is capable of attending to distinct temporal aspects or dependencies, but the full token-token attention matrix introduces significant $O(N^2)$ complexity (with $N$ the sequence length).

2. Memory and Bandwidth Optimizations: Multi-Query Attention and Sparse Query Attention

Multi-Query Attention (MQA) (Shazeer, 2019) optimizes for incremental inference by sharing key and value projections across heads, reducing the tensor size (and thus memory bandwidth):

$$Q^{(h)} = X P_q^{(h)}, \quad K = X P_k, \quad V = X P_v$$

This compression yields a memory reduction by factor $h$, with empirical results showing per-token decoding speed improvement from $46\,\mu$s to $3.8\,\mu$s and negligible BLEU/perplexity degradation for translation and LLMing.

Sparse Query Attention (SQA) (Filipek, 2 Oct 2025) takes the approach of reducing the number of query heads, rather than key/value heads. If $H_q$ is the number of query heads ($H_q<H$), projections are computed as:

$Q = X W_Q, \quad K = X W_K, \quad V = X W_V$

with $$Q \in \mathbb{R}^{N \times H_q \times d_{\text{head}}}$$

Total FLOPs for scored attention reduce linearly with $H_q$, achieving throughput gains up to $3\times$ on ultra-long sequences (32k–200k tokens) with only minimal quality impact.

Attention Variant	Heads Reduced	Computation Reduction	Quality Impact
MQA	KV	Bandwidth (inference)	Minimal
SQA	Q	FLOPs (train/infer)	Minor at scale

These optimizations are particularly relevant for temporal query-based applications where long sequence processing makes standard attention mechanisms infeasible.

3. Latent and Temporal Compression: Multi-Head Temporal Latent Attention

Multi-Head Temporal Latent Attention (MTLA) (2505.13544) aims to further compress the key-value (KV) cache used for attention along both latent (dimensionality) and temporal axes. Temporally adjacent KV vectors are dynamically merged via a hyper-network, yielding a compressed representation:

$\hat{c}_j = \sum_{i=1}^s w_{ji} c_{ji}$

where $s$ is the stride (compression factor), $c_{ji}$ are original latent vectors, and $w_{ji}$ are learnable weights.

A stride-aware causal mask ensures that training attention patterns match those used in streaming/incremental inference, maintaining autoregressive consistency. MTLA achieves up to $5.3\times$ decoding speedup and $8.3\times$ GPU memory reduction with only minor BLEU drops in speech translation, indicating strong utility for temporal tasks with long contexts.

4. Collaborative and Compositional Head Sharing

Redundancy in independent head projections has motivated collaborative schemes (Cordonnier et al., 2020). Collaborative multi-head attention shares query and key projections across all heads:

$$Q_{\text{shared}},\, K_{\text{shared}} \in \mathbb{R}^{D_{in}\times\tilde{D}_k}$$

Each head has a mixing vector $w_i$:

$$\text{Head}_i = \text{Attention}(\operatorname{diag}(w_i) Q_{\text{shared}}, K_{\text{shared}}, V^{(i)})$$

Tensor decomposition (Tucker/CP) allows post-hoc reparametrization of pre-trained models for efficient collaborative adaptation. The approach can, by design or inference, be extended to temporal queries by embedding temporal structure in the shared projections or learning time-conditioned mixing weights.

In compositional attention (Mittal et al., 2021), the search (query-key) and retrieval (value) computations are decoupled and recombined via dynamic soft competition. For each search, multiple possible retrievals are considered, with context-dependent selection:

$$\text{CAtt}_i = \operatorname{Softmax}\left(\frac{\overline{Q}_i \overline{K}_i^\top}{\sqrt{d_r}}\right)$$

where $\overline{K}_i$ is the concatenation of all candidate retrieval keys.

These paradigms enable more flexible adaptation to queries that may vary temporally or contextually, increasing the capacity for systematic temporal generalization.

5. Specialized Temporal Modules and Augmentations

Several works introduce explicit temporal augmentations:

• Temporal Attention-Augmented Bilinear Network (MTABL) (Shabani et al., 2022): Multiple attention heads focus on independent temporal aspects of the input sequence. Each head applies a specialized mask $\alpha^{(k)}$, with attended features blended by parameter $\lambda$:

$$\tilde{X}_{k} = \lambda(X̄ \odot A_{(k)}) + (1-\lambda)X̄$$

Attended outputs are concatenated and projected to preserve temporal granularity.

• Temporal-Channel Modeling (TCM) (Truong et al., 25 Jun 2024): Introduces head tokens representing channel (frequency) information in addition to temporal tokens. Multi-head attention is applied jointly, enabling cross-temporal and cross-channel interactions. Pooling operations enrich the classification token with both temporal and head token means, yielding significant improvements (e.g., $9.25\%$ EER reduction in synthetic speech detection).
• Guided Query Position (GQPos) (Jiang et al., 2021): In transformer-based object detection, query positions are iteratively updated using prediction output at each layer, ensuring the spatial (and temporal) accuracy of object queries is refined with depth.

6. Scaling and Long Context Processing

Efficient handling of very long context windows (up to 128k tokens) is achieved with frameworks such as LongHeads (Lu et al., 16 Feb 2024). This method assigns each attention head a fixed "chunk" of the context to attend to, matched with the typical training range, and uses query-driven selection of relevant context chunks:

$$P = \{C_1\} \cup \{C_i\,|\,\text{rank}(q_j \cdot c_i) \leq k-2\} \cup \{C_{-1}\}$$

This chunk-based arrangement allows multi-head attention to operate in linear time, distributing long sequences among heads without retraining or inducing out-of-distribution errors. Experiments show $100\%$ passkey retrieval accuracy at $128\text{k}$ tokens and strong long-context document understanding.

7. Practical Applications and Future Directions

Temporal query-based multi-head attention is foundational for:

• Machine translation, LLMing, and speech tasks requiring incremental decoding with low latency and constrained memory bandwidth (Shazeer, 2019).
• Financial time series prediction, where multi-head temporal attention extracts discriminative event patterns (Shabani et al., 2022).
• Large-context retrieval and summarization, where attention must scale efficiently with input size (Lu et al., 16 Feb 2024).
• Speaker verification, where multi-query multi-head pooling aggregates rich temporal/channel features (Zhao et al., 2021).
• Synthetic speech detection and audio event analysis, via cross-temporal/channel modeling (Truong et al., 25 Jun 2024).

Future research is anticipated to integrate dynamic query-based routing, further hybridize query/KV reduction (combining SQA with MQA/GQA), explore compositional scaling across multiple semantic axes, and leverage temporal-chunking in both encoder and decoder settings for flexible, scalable sequence modeling.

• "Fast Transformer Decoding: One Write-Head is All You Need" (Shazeer, 2019)
• "Multi-Head Attention: Collaborate Instead of Concatenate" (Cordonnier et al., 2020)
• "Sparse Query Attention (SQA): A Computationally Efficient Attention Mechanism with Query Heads Reduction" (Filipek, 2 Oct 2025)
• "Multi-head Temporal Latent Attention" (2505.13544)
• "LongHeads: Multi-Head Attention is Secretly a Long Context Processor" (Lu et al., 16 Feb 2024)
• "Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection" (Truong et al., 25 Jun 2024)
• "Guiding Query Position and Performing Similar Attention for Transformer-Based Detection Heads" (Jiang et al., 2021)
• "Multi-query multi-head attention pooling and Inter-topK penalty for speaker verification" (Zhao et al., 2021)
• "Compositional Attention: Disentangling Search and Retrieval" (Mittal et al., 2021)
• "Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction" (Shabani et al., 2022)