Formula for Fixed-Size Window Memory

Window-based Cache as an Example of Fixed-Size Memory

Space Complexity of Sliding Window Attention

Window Size (n_c)

A language model is designed to process extremely long sequences of text, and its developers are concerned about computational resources. They are considering two approaches for the attention mechanism: one that considers all previous tokens in the sequence, and another that only considers a fixed-size window of the 100 most recent tokens. What is the fundamental trade-off between these two approaches?

Applying Sliding Window Attention

In an attention mechanism that uses a fixed-size sliding window, the amount of memory required to store the keys and values for the attention calculation increases as the input sequence gets longer.

Your team is documenting the memory subsystem of a...

You are reviewing two candidate memory designs for...

You’re deploying an internal LLM assistant that mu...

You’re designing an internal LLM feature that moni...

Post-Incident Review: Memory Design for Long-Running Customer Support Chats

Diagnosing Long-Range Failures in a Segment-Processed LLM with Dual Memory

Choosing a Memory Architecture for Long-Context Enterprise Summarization

Postmortem: Long-Document QA Failures Under Fixed-Window vs Compressive Memory

Selecting and Justifying a Long-Context Memory Design for a Regulated Audit Assistant

Incident Triage: Long-Running Agent Workflow with Windowed vs Compressive Memory

Formula for Fixed-Size Window Memory

Suppose you have a sequence of key vectors represented as rows in a matrix, where $\mathbf{k}_0 = [1, 2]$, $\mathbf{k}_1 = [3, 4]$, $\mathbf{k}_2 = [5, 6]$, $\mathbf{k}_3 = [7, 8]$, and $\mathbf{k}_4 = [9, 10]$. Given a processing step at index $i=3$ and a context window size of $n_c=3$, which matrix is constructed by selecting the contiguous block of key vectors ending at the current step?

Properties of a Sliding Window Key Matrix

True or False: For a sequence of key vectors being processed at index $i=10$ with a context window size of $n_c=5$, the resulting sub-matrix of key vectors, denoted $\mathbf{K}_{[i-n_c+1,i]}$, will contain the key vectors from index 5 to index 10 (i.e., $\mathbf{k}_5, \mathbf{k}_6, \dots, \mathbf{k}_{10}$).

Formula for Fixed-Size Window Memory

Consider a sequence of 2-dimensional value vectors, where:

• v_1 = [10, 11]
• v_2 = [12, 13]
• v_3 = [14, 15]
• v_4 = [16, 17]
• v_5 = [18, 19]

Given a current processing index of i = 5 and a context window size of n_c = 3, which matrix below correctly represents the structure formed by vertically stacking the value vectors from the corresponding sliding window, from index i - n_c + 1 to i?

Inferring Window Parameters

Determining Matrix Dimensions from a Sliding Window