TLinFormer: Linear Transformer Architecture

• TLinFormer is a transformer architecture that reconfigures attention connectivity to achieve strict linear complexity by compressing historical context.
• It employs a windowed, multi-path design that combines focused and causal self-attention for full-context awareness and efficient token generation.
• Experimental results show up to 53× speedup and reduced memory footprint, making it suitable for ultra-long sequence processing tasks.

TLinFormer is a transformer architecture that aims to mitigate the quadratic complexity bottleneck inherent in standard self-attention mechanisms by reconfiguring attention connectivity, enabling strict linear complexity while preserving full context-awareness and computing exact attention scores. TLinFormer addresses the limitations of prior efficient attention mechanisms, which typically rely on data-agnostic kernel approximations or restrictive local context, by rethinking the topological structure of information flow and implementing a windowed, multi-path connectivity pattern. This allows the model to maintain exact attention computation over arbitrarily long sequences with substantially improved resource efficiency and practical performance compared to conventional transformers (Tang, 28 Aug 2025).

1. Reconfigured Connectivity and Architectural Principles

TLinFormer divides the input sequence into two dynamically managed windows: a historical context window and a generation window. The historical window accumulates all prior tokens; within this window, TLinFormer applies a "focused attention" mechanism where a fixed set of query tokens attends to the full sequence of keys and values, effecting a forced compression of historical information into fixed-size context representations. The generation window comprises the current tokens being either predicted or autoregressively generated.

To ensure causal autoregressive properties, generation tokens are processed with masked causal self-attention, which enforces token-to-token dependencies only for preceding or current positions. Crucially, TLinFormer supplements this local processing with a cross-attention operation that injects the compressed global context from the historical window into the generation pathway, guaranteeing that each new token is aware of the entire input history without the cost of revisiting the global context at every step. Architecturally, this represents a shift from the single-path, bottleneck context vector characteristic of standard transformers to a multi-path design with focused and causal branches. The model's schematic stack arranges these multi-window attention blocks as depicted in Figure 1 of the source, illustrating both the historical-compressed and generation-causal pathways.

2. Linear Complexity via Windowed and Cached Computation

TLinFormer's strict linear complexity is achieved through two intertwined strategies:

• Fixed-size observation windows: Only a window of recent tokens (generation window) is subjected to expensive attention computation each inference step; the historical context, once distilled, is reused for subsequent steps, thus amortizing its cost.
• Static caching with local updates: TLinFormer maintains a static cache for historical key-value vectors rather than concatenating new tokens linearly. During token generation ("cache hit"), only the generation window requires recomputation, while the historical representation can be reused until the window boundary is crossed ("cache miss"). As a result, computational cost for inference is linear in sequence length.

Formally, on cache miss, the total cost is expressed as:

$$\text{Total Cost} = D \cdot \left[N(2 + \theta) + H(\alpha^2 + \beta^2 + ...) + \gamma^2 - \delta \right]$$

where $N$ is sequence length, $D$ is feature dimension, $H$ is network depth, and the other terms are fixed window and architecture-specific constants. On cache hit, cost remains linear with a markedly reduced slope.

Exact softmax attention computation is retained within the reduced windows:

$$\text{Attention}(\bm{Q}, \bm{K}, \bm{V}) = \text{softmax}\left(\frac{\bm{Q} \bm{K}^T}{\sqrt{d_k}}\right) \bm{V}$$

As such, TLinFormer does not use kernel approximations, random projections, or restrictive local selection; all attention scores are computed precisely within functionally partitioned windows.

3. Context Compression and Information Flow

By concentrating the historical window into fixed-size context through focused attention, TLinFormer compels the model to "summarize" all past tokens without loss of information. The cross-attention from global context to generation window further ensures that generated tokens can access the full historical state. Unlike previous efficient transformers, TLinFormer neither relies on memory-intensive global computations at every step nor sacrifices context-awareness.

Multiple TLinFormer blocks can be stacked; each block propagates both causal (local) information and compressed (global) context, achieving multi-layer context integration. The explicit separation and interaction of these flows, as shown in architectural figures in the source, enables deep long-sequence modeling while keeping latency and memory cost low.

4. Efficiency Metrics: Inference Latency, KV Cache, and Memory Footprint

Experimental results indicate TLinFormer provides substantial efficiency improvements over a baseline transformer:

• Inference Latency: TLinFormer achieves up to 20× speedup on cache miss and 53× on cache hit for long-sequence tasks, owing to windowed computation and static caching.
• KV Cache Efficiency: TLinFormer's key-value cache is approximately $1/(H+2)$ that of a standard transformer of comparable depth, reflecting the non-growing cache bounded by window size.
• Memory Footprint: Standard transformers' memory grows linearly with sequence length, rapidly hitting GPU limits. TLinFormer processes sequences exceeding 1 million tokens with much flatter memory increase and efficient static cache management.
• Overall Speedup: The combined effects of windowed cost, reduced concatenation overhead, and optimized context compression yield dramatic practical speedups, especially as sequence length increases.

5. Comparison to Prior Efficient Attention Mechanisms

Previous linear and efficient attention models commonly employ kernel-based approximations or "selective context" strategies that inevitably degrade performance or context-awareness (e.g., random feature mappings, local context windows). TLinFormer employs full-context exact attention computation within functionally partitioned windows, obviating the need for such approximations and thereby avoiding their attendant performance gaps. This design aligns TLinFormer’s modeling capacity closely with that of standard transformers while unlocking strict linear complexity.

6. Practical Implications and Deployment

TLinFormer enables the practical deployment of transformer architectures for ultra-long sequence modeling tasks—such as language modeling, document-level synthesis, or scientific data analysis—where quadratic attention cost and ballooning memory footprint previously precluded transformer applicability. Its static caching, linear computation, and full-context awareness make it suitable for applications with stringent latency and hardware constraints.

A plausible implication is that TLinFormer provides a robust template for future transformer scalability, especially in large-model serving or low-resource environments. The architecture decouples global contextualization from token-wise updates, suggesting new directions for efficient information routing and dynamic context management.

7. Concluding Remarks

TLinFormer's architectural innovations—multi-path windowed attention, dynamic compression of historical context, and exact softmax computation—address fundamental bottlenecks in transformer scalability and resource consumption. Systematic evaluations consistently demonstrate superior inference latency, KV cache, and memory utilization metrics when compared to conventional transformers for long-sequence processing (Tang, 28 Aug 2025). This work exemplifies architectural rethinking along topological connectivity lines and opens new avenues for context-rich modeling under strict resource budgets.