---
license: apache-2.0
base_model: Qwen/Qwen3.6-27B
tags:
  - interpretability
  - oracle-lens
  - activation-reading
  - lora
---

# Oracle Lens (AO) — RL final checkpoint `iolens.final.ddp600.s0` (iter_000600)

A LoRA adapter on **Qwen/Qwen3.6-27B** that turns the model into an *oracle lens*: feed it one
of its own residual-stream activation vectors and it verbalizes the content of that activation
as a bullet list of concepts. This is the on-policy GRPO checkpoint (600 steps, DDP-8) warm
started from the `iolens.final` SFT bullet student — the first positive RL result on the AO
line (held-out joint FVE 0.120 → 0.155 against a frozen activation-reconstructor reward).

## Load contract (read this before using — a wrong contract measures nothing)

| knob | value |
|---|---|
| base model | `Qwen/Qwen3.6-27B` (text decoder) |
| injection site | the embedding slot of marker token **㈜** (id **158983**), flanked by `<activation>`/`</activation>` (neighbor ids 29, 510) |
| injection | **replace** the marker's embedding with `16000 · h / ‖h‖` (unit transform, α=16000; scale-free) |
| supported layers | residual stream layers **20–60, step 4** (11 layers) |
| prompt | the checkpoint's own `concepts_raw` template (in `nla_meta.yaml`): *"An activation vector from layer 20 of a language model is enclosed in activation tags: `<activation>㈜</activation>`. Produce distinct concepts that encode this activation, each as a '- ' bullet on its own line."* rendered with the chat template, `add_generation_prompt=True` |
| parse | one concept per `- ` bullet line |
| eval sampling used in our benchmarks | k=1, T=1.0, top-p 0.95, top-k 64, max-new 256 |

## Usage sketch

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.6-27B", torch_dtype=torch.bfloat16, device_map="cuda")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.6-27B")
model = PeftModel.from_pretrained(base, "<this repo>")

TEMPLATE = ("An activation vector from layer 20 of a language model is enclosed in activation "
            "tags: <activation>㈜</activation>. Produce distinct concepts that encode this "
            "activation, each as a '- ' bullet on its own line.")
ids = tok.apply_chat_template([{"role": "user", "content": TEMPLATE}],
                              tokenize=True, add_generation_prompt=True)
mp = ids.index(158983)                       # the marker slot
h = ...                                      # your residual vector, layer in {20,24,...,60}
v = 16000.0 * h / h.norm()

embed = model.get_input_embeddings()
inputs_embeds = embed(torch.tensor([ids], device="cuda"))
inputs_embeds[0, mp] = v.to(inputs_embeds.dtype).to("cuda")   # REPLACE the slot embedding
out = model.generate(inputs_embeds=inputs_embeds, do_sample=True, temperature=1.0,
                     top_p=0.95, top_k=64, max_new_tokens=256)
print(tok.decode(out[0], skip_special_tokens=True))
```

## Benchmarks (workspace-bench, strict-judged, 2026-09)

Judged baseline macro 0.785 over the 12 bank families (association 0.88, multihop 0.89,
multihop-mt 0.87, multilingual 1.00, typo 1.00); hillclimbing: conjunctive 0.60, role-bound
association 0.85, moral-committed 0.81, moral-deliberative 0.62, user-modeling 0.44 (k=1).
Notable qualitative reads: never-written arithmetic intermediates, both poles of moral
dilemmas from the empty assistant prefix, doctrine retrieval (Tarasoff, Double Effect) at
layer 20, and latent user-attribute inference (see the workspace-bench records).

## Provenance

- Training: on-policy GRPO (self-contained trainer), 600 steps, β_KL 0.02, lr 5e-5,
  group size 64, reward = per-bullet joint FVE against a frozen activation reconstructor
  (whitened, unit concept dirs, k≤4); warm start `ckpts/ao/distill/final.s0/step105`.
- Files: `adapter_config.json`, `adapter_model.safetensors` (PEFT LoRA), `meta.json`
  (training summary), `nla_meta.yaml` (the prompt/injection sidecar — the source of truth
  for the load contract), `run_config.yaml` (full trainer config).
- Canonical archive: `agu18dec/local-workspace` → `ckpts/ao/rl/iolens.final.ddp600.s0/`.