README.md

---
license: apache-2.0
base_model: Qwen/Qwen3-4B
tags:
  - kv-cache
  - context-compression
  - long-context
  - research
  - negative-results
  - reproducibility
library_name: safetensors
---

# context-cache-lab — Stage 2b memory extractor (research artifact)

> ## ⚠️ This is a negative-result research artifact, not a useful model.
>
> It is published so that a **failed** experiment is reproducible. Its own
> preregistered gates returned **INCONCLUSIVE** (Stage 2b) and **FAIL** (Stage 3).
> Do not use it for anything in production. Do not read a capability claim into it.

A 566M-parameter C²KV-style sidecar that compiles chunks of text into compressed,
pre-RoPE KV "pages" for a **frozen** `Qwen/Qwen3-4B`. It does not generate text and
cannot be used standalone — it only produces key/value state that the frozen target
consumes.

**Code, protocol, preregistration and all results:**
https://github.com/johnathonkillaly/context-cache-lab

## What was measured

| Stage | Verdict | Result |
|---|---|---|
| 2b — does learned compressed state beat equal-budget raw text? | 🟡 **INCONCLUSIVE** | Beat `BUDGET` at every ratio, but missed 2 of 4 frozen criteria by ~1% |
| 3 — do independently compiled pages support two-page reasoning? | ❌ **FAIL** | **0/13** valid items vs NATIVE **13/13**; all 5 gates missed |

**Stage 2b (single-document QA, 4096 tokens, `clean_hit`):**

| ratio | this extractor | equal-budget raw text | NATIVE |
|---|---|---|---|
| 2× | 0.569 | 0.472 | 0.958 |
| 4× | 0.389 | 0.222 | 0.958 |
| 8× | 0.347 | 0.125 | 0.958 |
| 16× | 0.278 | 0.069 | 0.958 |

Semantic facts survived compression (0.625 at 4×) while exact strings did not —
hashes collapsed to **0.000** and identifiers to 0.250, where raw text at the same
budget scored 0.750. Warm reuse was 23–32× faster than native prefill with breakeven
at 2 queries, but compilation costs more than a single prefill, so the first query is
slower.

**Stage 3** then evaluated this exact frozen checkpoint on a preregistered cross-page
compositional task. It scored **zero** clean accuracy on every native-valid item, with a
mean rank margin (−2.47) *worse* than no context at all (−1.10). Compiling the document
jointly instead of per-page also scored zero, so this is not an independence tax.

**What that failure does not establish:** Stage 3 used a different corpus (~94–100 token
pages against ~256-token training chunks), so it measures insufficient transfer of *this*
carrier to *that* task — not the impossibility of independently compiled memory in
general. It also cannot fully separate loss of within-page fidelity from failure to
combine intact relations. See the repository's Stage 3 report for the full limitations.

## Files

| File | Purpose |
|---|---|
| `stage2b_extractor.safetensors` | **Use this.** 109 fp32 tensors, safe to load. Verified to round-trip exactly against the original. |
| `stage2b_extractor.pt` | Provenance artifact only. Its SHA-256 is pinned in the repo's `results/stage3/freeze.json`, so the Stage 3 integrity audit reproduces exactly. **It is a pickle** — the repo's scripts load it with `weights_only=False`. Only use it if you need the hash to verify. |
| `config.json` | `layer_share`, `n_sink`, training step, target model revision, and the original `.pt` SHA-256. |

```
original .pt sha256: b8384dd2eeaa87890a6b65e22f791183399764b93dd9a7f4e8a6e4ce13fca425
target model revision: 1cfa9a7208912126459214e8b04321603b3df60c
training step: 2200
```

## Usage

The extractor is only meaningful alongside the repository's code:

```bash
git clone https://github.com/johnathonkillaly/context-cache-lab
cd context-cache-lab
uv venv --python 3.12 .venv && uv pip install --python .venv/bin/python -e ".[dev]"
```

```python
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from ccl.compressor import MemoryExtractor
from ccl.target import TargetModel

target = TargetModel("Qwen/Qwen3-4B")          # frozen
extractor = MemoryExtractor(target, layer_share=1, n_sink=8)
extractor.load_state_dict(load_file(hf_hub_download(
    "jkillay/context-cache-lab-stage2b-extractor",
    "stage2b_extractor.safetensors")))
extractor.eval()

pages = extractor.compile_chunks([target.encode(c) for c in chunks], ratio=4.0)
```

Requires `transformers>=5.0` (the legacy tuple KV-cache format is gone) and roughly
10 GB of memory for the frozen target plus the sidecar. Developed on Apple Silicon
(MPS); no CUDA is assumed.

## Training

2,200 steps, ~108 minutes on an Apple M4 Max, compression ratio sampled per example
(C²KV's `-Dyn` variant). The target model was frozen throughout — only the shared
memory-token embedding and per-layer Q/K/V projection heads received gradients.
Supervision was on answer tokens only, applied **after** page concatenation, so the
extractor is pushed toward states that compose rather than states that are merely
individually informative.

Training data was a **synthetic** corpus generated deterministically from integer
seeds, with value pools provably disjoint from the held-out evaluation draw. No
scraped text and no personal data.

## License and attribution

Apache-2.0. This is a derivative of [`Qwen/Qwen3-4B`](https://huggingface.co/Qwen/Qwen3-4B)
(Apache-2.0) in the sense that its projections were initialized from that model's
weights; **no Qwen weights are redistributed here** — only the trained sidecar.

The mechanism is a reimplementation of the published design of
[C²KV (Du et al., KDD 2026)](https://arxiv.org/abs/2607.17715). No C²KV source code was
copied. Please cite that paper alongside this artifact.

## Citation

See `CITATION.cff` in the
[GitHub repository](https://github.com/johnathonkillaly/context-cache-lab).