modulation lens: space-ablation cell B_J_nomean (RL step 50)

README.md

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+---
+base_model: Qwen/Qwen3.6-27B
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:Qwen/Qwen3.6-27B
+- lora
+- transformers
+---
+
+# Model Card for Model ID
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+<!-- Provide a quick summary of what the model is/does. -->
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+
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+## Model Details
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+### Model Description
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+### Recommendations
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+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
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+Use the code below to get started with the model.
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+[More Information Needed]
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+## Training Details
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+### Training Data
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+[More Information Needed]
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+### Training Procedure
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+#### Preprocessing [optional]
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+
+#### Training Hyperparameters
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+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+## Evaluation
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+#### Metrics
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+### Results
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+#### Summary
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+## Environmental Impact
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+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+## Technical Specifications [optional]
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+### Model Architecture and Objective
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+## Glossary [optional]
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+### Framework versions
+
+- PEFT 0.20.0

USAGE.md

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+# Modulation lens — space-ablation cell `B_J_nomean`
+
+LoRA on Qwen3.6-27B. Reads ONE activation from layer 42 and emits 4 `* ` bullets naming the
+things that state is holding in mind. Trained end-to-end for this cell:
+dictionary decomposition (NNOMP over a 2.9M-atom modulation dictionary, 500k activations)
+→ SFT (3000 steps) → RL (50 steps).
+
+## This cell's reconstruction space
+
+| | |
+|---|---|
+| Jacobian (J-lens, L42→L62) | **yes** |
+| activation-pool mean subtracted | **no** |
+| fitted affine | none (identity) — in every cell |
+
+Sibling cells: `modulation-lens-grid-A-jspace-meansub`, `-B-jspace-nomeansub`,
+`-C-raw-meansub`, `-D-raw-nomeansub`.
+
+## Results
+
+| | this cell |
+|---|---|
+| WorkspaceBench, SFT warm start | 0.120 |
+| WorkspaceBench, after RL | 0.402 |
+
+Scored with the workspace-bench repo's **deterministic** `word_matcher` over 10 mechanical banks,
+NOT the usual LLM judge (`bank_judge`), which was unavailable. A string matcher cannot see a
+translation or a paraphrase, so these read strictly lower than LLM-judged numbers and are **not
+comparable** to any `bank_judge` figure — including the 0.196 j-lens baseline quoted elsewhere in
+this project. They are comparable ACROSS the four cells, which is what the ablation asks.
+
+Headline across the grid: mean subtraction decides the **warm start** (centered cells beat
+un-centered ones, paired-by-family t=2.35 and t=2.83), but after 50 RL steps cells A, B and D are
+statistically indistinguishable (t=-0.16, t=+0.16). The Jacobian is second-order throughout:
+A vs C, which differ only in J, tie at the SFT stage (t=0.95).
+
+## Diagnostics measured BEFORE training
+
+| metric | value | meaning |
+|---|---|---|
+| NNOMP mean FVE | 0.3766 | 4-atom reconstruction quality |
+| target-blind floor (best constant cosine) | 0.643 | score obtainable WITHOUT reading the activation |
+| atom diversity (unique atoms per bullet slot) | 0.330 | 1.0 = every bullet a distinct atom; low = reciting |
+
+**Read the floor before the FVE.** In cell D a single fixed vector that never looks at the
+activation scores 0.817, so its reconstruction number reflects a shared mean component rather
+than anything about the specific activation. D's SFT emitted one dictionary atom as its first
+bullet on 71/100 bench items; the contrastive RL reward removed that (unique first bullets
+0.25 → 0.96), because a constant answer scores `fit(matched) − fit(negative) ≈ 0`.
+
+## Usage
+
+`prompt.txt` is REQUIRED — it carries the single injection marker. Replace the marker position's
+residual stream at layer 42 with your activation (replace-mode: raw direction and magnitude),
+re-apply the chat template, then generate. Without the marker the readout is empty.
+
+## Training
+
+RL: ScaleRL recipe (CISPO, prompt-level loss aggregation, batch-level advantage normalisation,
+truncated importance sampling, zero-variance filtering), 8 samples × 512 prompts per step,
+LR 5e-6, no KL. Reward = a FROZEN text→modulation-vector reconstructor (AR) applied to each
+bullet, composed by exact non-negative least squares, cosine to the target, minus the fit against
+a different activation's target (contrastive). The AR is frozen and identical across all four
+cells; only the space differs. `optim.pt` is intentionally not shipped.

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3.6-27B",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "monteclora_config": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "gate_proj",
+    "v_proj",
+    "o_proj",
+    "k_proj",
+    "up_proj",
+    "down_proj",
+    "q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": true,
+  "velora_config": null
+}

adapter_model.safetensors

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+oid sha256:d24e29e7e3f90249e2d96c4ff870913fc1c391f3fa4777e3696b1001f9bbb326
+size 1275137464

prompt.txt

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+You are shown an internal activation vector captured from a language model at a single position while it was reading some text. The vector is enclosed in <concept> tags.
+
+<concept>㈜</concept>
+
+Output 4 bullet points, each starting with '*', describing the separate things this state is holding in mind. They are combined afterwards, so each bullet should be a DIFFERENT part of the state rather than a rephrasing of the others.
+
+How it is judged. EACH of your lines is placed separately into a prompt of the form
+  Focus on the following idea: "<one of your lines>" while writing the following phrase: "<a fixed unrelated sentence>"
+The model then writes that fixed sentence, and we read its internal state while it does so. The 4 resulting states are then added together with non-negative weights, and you score well when that SUM matches the state you were given -- so the lines should cover DIFFERENT parts of it.
+
+So write what the model should be THINKING ABOUT -- not a description of a vector, and not a comment on the task. Natural, fluent English. At most 12 tokens PER LINE -- short, concrete lines leave room for the other lines and compose better. Output only the 4 bullet lines: no preamble, no summary line, no trailing commentary.