Connect early n-gram embeddings to learned n-gram memory in LLMs

README.md

@@ -8,6 +8,14 @@
 [![Paper](https://img.shields.io/badge/arXiv-2506.01254-b31b1b.svg)](https://arxiv.org/abs/2506.01254)
 [![Release](https://img.shields.io/github/v/release/initial-d/ngram-store?label=release)](https://github.com/initial-d/ngram-store/releases/latest)
 
+**从早期 n-gram embedding，到大模型中的 learned n-gram 参数化记忆。**
+
+FastText uses learned subword vectors to build word representations;
+Engram-style LLM memory retrieves learned token-n-gram features and fuses them
+with hidden states. The application changes, while the need to index, store and
+query trained vectors continues. NGramStore follows this thread through a
+shared backend. Learned n-gram tables are one form of parametric memory.
+
 Formerly **me_fasttext**. The engine grew from
 [Memory-Efficient FastText: A Comprehensive Approach Using Double-Array Trie Structures and Mark-Compact Memory Management](https://arxiv.org/abs/2506.01254).
 The paper keeps its original title; later Engram integrations are software extensions.

docs/artifact_card.md

@@ -4,6 +4,12 @@
 Formerly me_fasttext; the original Memory-Efficient FastText paper is the method
 origin, while native row storage and LLM integrations are later software work.
 
+The historical thread runs from n-gram embeddings for word representations to
+learned n-gram memory modules in LLMs. FastText and Engram illustrate these
+application generations: both query trained vector tables, with different keys,
+addressing and model computation. This table-based form of parametric memory is
+the scope of the shared engine.
+
 | Layer / component | What it provides | Boundary |
 | --- | --- | --- |
 | 1. Shared native C engine | Lossless compressed blocks, original-ID forwarding, mmap and gather | Frozen single-worker handles; caches do not bound RSS |

docs/ngram_memory_research_plan.md

@@ -1,5 +1,24 @@
 # NGramStore engine roadmap and research plan
 
+## Historical background: n-gram embeddings to LLM memory
+
+Early learned n-gram embeddings represent local patterns as trainable vectors.
+FastText's subword vectors contribute to word representations. Newer Engram-style
+LLM architectures retrieve learned token-n-gram features from tables and fuse
+them with hidden states. This gives the same addressable-vector abstraction a
+role as an explicit learned memory module alongside the LLM backbone.
+
+Here, n-gram parametric memory means learned lookup-table weights. This is one
+branch of model memory, with a common storage/query problem across application
+generations. The continuity is the table abstraction; character versus token
+keys, hash versus exact addressing, training and context fusion remain distinct.
+
+NGramStore's historical development follows that abstraction: the original
+me_fasttext method optimizes n-gram indexing and memory management for word
+embeddings; the shared engine extends storage and queries to frozen learned
+tables used by LLM memory modules. The later software evidence keeps its own
+scope and does not rewrite the original paper's experiments.
+
 ## Product contract and history
 
 NGramStore is a general-purpose backend for **learned n-gram embeddings**: