Sillage

Your language model forgets everything. Sillage gives it a memory — and a way to keep learning — in a fixed handful of megabytes, with no gradients and no index that grows.

sillage (n., French) — the trace left behind by something that has passed: a ship's wake, a scent in a room. What a model keeps of what it read.

A frozen language model reads your documents, remembers them, and predicts better next time. No gradients, no fine-tuning, no vector database. One Hebbian matrix written as the model reads, a semantic tier routed by confidence, a cold store that consolidates by surprise, and a rank-16 adapter on the readout — four mechanisms, four preprints, one command-line tool. Everything runs on a laptop CPU.

Install

pip install sillage

Use

sillage index notes.md                # instant: no model, already queryable
sillage ask "what did the report say?"

sillage read notes.md                 # memorize it (CPU: ~8 min per 10k tokens)
sillage complete "The report said"    # generate WITH the memory
sillage status                        # what it knows, tier by tier
sillage forget --all

$ sillage read preprint_v1.txt                    # Monday, memory empty
read preprint_v1.txt: 5859 tokens in 4.9 min | PPL 12.14 -> 11.83 (adapter) -> 11.71 (+memory)

$ sillage read preprint_v2.md                     # Tuesday, a new process
read preprint_v2.md: 11041 tokens in 9.5 min | PPL 10.68 -> 9.82 (adapter) -> 5.39 (+memory)

Three numbers per file: what the frozen model alone predicts, what the rank-16 adapter adds, and what the memory of everything read so far adds on top. The state lives in ./.sillage, survives restarts, and never grows: 7.4 MB with GPT-2, 25 MB with Qwen3, the same after one document or ten thousand. It never learns from its own generations — only from what you give it to read.

From Python:

from sillage import Sillage

s = Sillage(model="gpt2")          # any causal LM; omit it and the state
                                   # says which model it belongs to
s.read("notes.md")                 # read, memorize, index -- then save
s.ask("what did the report say?")  # exact passages, nothing generated
print(s.complete("The report said"))

How much better, exactly?

On 36k tokens of technical text the model had never seen (frozen GPT-2 124M, every system tuned identically on a held-out prefix, 95 % bootstrap CIs):

The fixed 4.2 MB memory beats the unbounded datastore it was designed to approximate — paired bootstrap P = 1.000, replicated over 5 random seeds and on a second model (Qwen3-0.6B).

Where it does not win: on long, low-repetition narrative text, kNN-LM still beats it. This memory captures verbatim recurrence; that is its regime, and it is measured and published rather than hidden.

Any causal language model

sillage read notes.md --model qwen                          # shortcut
sillage read notes.md --model HuggingFaceTB/SmolLM2-135M    # any hub id
sillage read notes.md --model ./my-finetuned-llama          # any local folder

Verified across three architectures (GPT-2, Qwen3, GPT-NeoX). For a model nobody has tuned, the readout calibrates itself on a rolling window of what you read, following the papers' protocol; for the two the papers did tune, their published settings are kept. A memory is written in one model's token space, so give each model its own --state directory.

The four preprints

Sources, figures, every number as committed JSON, the reproduction pipeline and the three negative results are on GitHub: https://github.com/riscoss63/sillage

Citation

@article{sghairi2026sillage,
  title  = {Sillage: Surprise-Gated Amplitude Memory
            for Frozen Language Models},
  author = {Sghairi, Abderrahmane},
  year   = {2026},
  doi    = {10.5281/zenodo.22079016},
  url    = {https://doi.org/10.5281/zenodo.22079016}
}

MIT licensed.