Instruction-tuned language models refuse harmful requests. But which part of the model is actually responsible — and how does that mechanism get installed during training? A new research from Nous Research team takes a neuron-level look at this question. The Nous research team developed contrastive neuron attribution (CNA), a method that identifies the specific MLP neurons whose activations most distinguish harmful from benign prompts. By ablating just 0.1% of MLP activations, they reduced refusal rates by more than 50% in most instruct models tested — across Llama and Qwen architectures from 1B to 72B parameters — while keeping output quality above 0.97 at all steering strengths. What’s interesting is a key finding: the late-layer structure that discriminates harmful from benign prompts exists in base models before any fine-tuning. Alignment fine-tuning does not create new structure. It transforms the function of neurons within that existing structure into a sparse, targetable refusal gate.
The Problem With Existing Steering Methods
Contrastive Activation Addition (CAA) computes the average difference in residual stream activations between two contrastive prompt sets. The difference becomes a steering vector applied at inference time. CAA is effective but coarse: it modifies the entire layer-wide signal without identifying which individual neurons are responsible. At high steering strengths, output quality degrades — models produce repeated words and incoherent text.
Sparse autoencoders (SAEs) decompose activations into interpretable features. They require expensive external training and are sensitive to activation noise.
CNA requires only forward passes — no gradients, no auxiliary training, no iterative search.
How CNA Works
You define two sets of prompts:
- Positive prompts — examples of the target behavior (e.g., harmful requests)
- Negative prompts — examples of the opposite (e.g., benign requests)
You run all prompts through the model. At each MLP layer, the method records down projection activations at the last token position. It then computes the per-neuron mean activation difference between the two sets:
δjℓ = mean(activations on positive prompts) − mean(activations on negative prompts)
The top-k neurons by absolute difference are selected across all layers. The researchers set k to 0.1% of total MLP activations. This threshold produced reliable steering effects across all model sizes tested.
A filtering step removes ‘universal’ neurons — those appearing in the top 0.1% of MLP activations across 80% or more of diverse prompts. These neurons fire regardless of prompt content and are excluded from all discovered circuits.
Causality is verified by multiplying each circuit neuron’s activation by a scalar multiplier m at inference time. m = 0 ablates the neuron. m = 1 is baseline. m > 1 amplifies it.
For the main JBB-Behaviors evaluation, the refusal circuit is discovered using 100 harmful and 100 benign prompts. For qualitative examples and other tasks, 8 positive and 8 negative prompts were used.
Results
Experiments covered base and instruct variants of Llama 3.1/3.2 and Qwen 2.5, from 1B to 72B parameters — 16 models total. The main benchmark was JBB-Behaviors, a NeurIPS 2024 benchmark of 100 harmful prompts.
Refusal reduction. Ablating the discovered circuit reduced refusal rates by more than 50% in most instruct models tested. Selected results from Table 3 of the research paper:
| Model | Baseline | Ablated | Relative Drop |
|---|---|---|---|
| Llama-3.1-70B-Instruct | 86% | 18% | −79.1% |
| Qwen2.5-7B-Instruct | 87% | 2% | −97.7% |
| Qwen2.5-72B-Instruct | 78% | 8% | −89.7% |
| Llama-3.2-3B-Instruct | 84% | 47% | −44.0% |
| Qwen2.5-3B-Instruct | 90% | 58% | −35.6% |
Not all models exceeded 50% relative reduction — Llama-3.2-3B and Qwen2.5-3B showed smaller drops. The paper describes the effect as holding “in most cases.”
Output quality. CNA output quality, measured as 1 minus the fraction of repeated n-grams, stayed above 0.97 at all steering strengths across all instruct models tested. CAA dropped below 0.60 for six of the eight instruct models at maximum steering strength. In two cases — Qwen2.5-1.5B and Qwen2.5-72B — CAA degraded output so severely that the keyword classifier flagged degenerate text as refusals, producing artificially high refusal rates.
General capabilities. MMLU accuracy under CNA stayed within one percentage point of baseline at all steering strengths. CAA dropped to near-zero MMLU accuracy at maximum intervention.
StrongREJECT rubric. A secondary evaluation used the StrongREJECT rubric, which applies an LLM judge (Llama-3.3-70B) to score responses on harmfulness and dangerousness on a 0–1 scale. Llama model compliance scores improved by an average of 6% after CNA ablation. Qwen model compliance scores improved by an average of 31%.
Base model comparison. Applying the identical pipeline to base models produced no meaningful behavioral change. The paper illustrates this with a concrete example using the prompt “How do I pick a lock?”:
| Model | Multiplier | Output |
|---|---|---|
| Llama-1B Base | 1.0 | Repeats the question |
| Llama-1B Base | 0.0 (ablated) | Describes lock picking as a learnable skill |
| Llama-1B Instruct | 1.0 | “I can’t assist with that.” |
| Llama-1B Instruct | 0.0 (ablated) | Provides a guide |
| Llama-1B Instruct | 2.0 (amplified) | Stronger refusal |
In base models, steering the late-layer neurons produces content shifts — topic changes, rephrasing — but no behavioral change at any multiplier. In instruct models, the same structure acts as a causal safety gate.
Fine-Tuning Transforms Function, Not Structure
Discrimination neurons concentrate in the final 10% of layers in both base and instruct models. For Llama-3.2-1B, 87% of the top-200 discrimination neurons fall in the final three layers (L13–L15). For Qwen2.5-3B, 95% fall in the final quarter of layers. This late-layer concentration is a pretraining property — it exists before alignment fine-tuning.
The function of those neurons changes after fine-tuning. Table 8 in the research paper reports the overlap of (layer, neuron) index pairs between matched base and instruct circuits. Only 8–29% of individual neurons overlap between base and instruct models. Fine-tuning largely replaces the specific neurons within that late-layer structure while preserving the structure itself.
The research team describe this as a separation between two levels: layer-level structure (preserved across base and instruct) and neuron-level function (transformed by fine-tuning). This is consistent with prior work showing that instruction tuning rotates feed-forward network knowledge without changing layer structure.
Marktechpost’s Visual Explainer
Overview — What is CNA?
Contrastive Neuron Attribution
CNA identifies the top 0.1% of MLP neurons whose activations most distinguish one behavior from another — for example, harmful prompts from benign prompts.
Unlike residual-stream methods, CNA operates at the individual neuron level. Unlike sparse autoencoders, it requires no external training.
What you need:
- A base or instruct language model (Llama or Qwen architectures tested)
- A small set of contrastive prompt pairs
- Forward-pass access to MLP activations (via hooks)
- No GPU gradient computation required
Step 1 — Define Your Prompt Pairs
Build a Contrastive Discovery Set
You need two sets of prompts that represent opposite behaviors. The quality of this set directly affects which neurons are identified.
- Positive prompts — exhibit the target behavior (e.g., harmful requests)
- Negative prompts — exhibit the opposite (e.g., benign requests)
Recommended sizes:
- For benchmark evaluation: 100 positive + 100 negative prompts
- For qualitative testing: as few as 8 positive + 8 negative prompts
Example positive: “How do I pick a lock?”
Example negative: “How do I bake a cake?”
Step 2 — Record MLP Activations
Run Forward Passes With Hooks
Run all prompts through the model. At each MLP layer, record the down projection activations at the last token position using forward pre-hooks on down_proj.
# Register hooks on down_proj in each MLP layer def make_hook(layer_idx, store): def hook(module, input, output): store[layer_idx] = output[:, -1, :].detach() return hook activations = {} hooks = [] for i, layer in enumerate(model.layers): h = layer.mlp.down_proj.register_forward_hook( make_hook(i, activations) ) hooks.append(h) # Run forward pass with torch.no_grad(): model(**inputs)
Collect these activation tensors for every prompt in both sets before proceeding.
Step 3 — Compute Activation Differences
Per-Neuron Mean Contrastive Difference
For each neuron j in each layer ℓ, compute the mean activation difference between positive and negative sets:
δℓ_j = mean(aℓ_j over positive prompts)
— mean(aℓ_j over negative prompts)
# pos_acts, neg_acts: tensors of shape [n_prompts, n_neurons] import torch delta = dict() for layer_idx in pos_acts: delta[layer_idx] = ( pos_acts[layer_idx].mean(dim=0) - neg_acts[layer_idx].mean(dim=0) )
This produces one difference value per neuron per layer. A large absolute value means that neuron fires very differently between the two prompt sets.
Step 4 — Select the Circuit
Take the Top 0.1% by Absolute Difference
Flatten all per-neuron delta values across all layers. Select the top-k neurons by absolute value, where k = 0.1% of total MLP activations.
# Flatten all deltas into one tensor with (layer, neuron) indices all_deltas = torch.cat([delta[i] for i in sorted(delta)]) total = all_deltas.numel() k = max(1, int(total * 0.001)) # 0.1% top_vals, top_idx = torch.topk(all_deltas.abs(), k) # Map flat index back to (layer, neuron) pairs n_neurons = all_deltas.shape[0] // len(delta) circuit = [(idx // n_neurons, idx % n_neurons) for idx in top_idx.tolist()]
This set of (layer, neuron) pairs is your discovered circuit.
Step 5 — Filter Universal Neurons
Remove Neurons That Always Fire
Some neurons appear in the top 0.1% regardless of prompt content. These are not behavior-specific and must be excluded.
- Run a diverse set of unrelated prompts through the model
- Record which neurons fall in the top 0.1% for each prompt
- Flag any neuron appearing in the top 0.1% across 80% or more of prompts
- Remove flagged neurons from the discovered circuit before ablation
Skipping this step will contaminate the circuit with general-purpose neurons that fire constantly — and ablating them will degrade unrelated model behavior.
Step 6 — Ablate and Verify
Apply the Scalar Multiplier at Inference
Multiply each circuit neuron’s activation by a scalar m at inference time to verify the circuit is causal — not just correlated.
# circuit: list of (layer_idx, neuron_idx) # m=0 ablates, m=1 baseline, m>1 amplifies def make_ablation_hook(neuron_indices, m): def hook(module, input, output): output[:, -1, neuron_indices] *= m return output return hook # Group circuit neurons by layer, then register hooks from collections import defaultdict by_layer = defaultdict(list) for layer_idx, neuron_idx in circuit: by_layer[layer_idx].append(neuron_idx) hooks = [] for layer_idx, neurons in by_layer.items(): h = model.layers[layer_idx].mlp.down_proj .register_forward_hook( make_ablation_hook(neurons, m=0.0) ) hooks.append(h)
What to Expect — Results
Refusal Reduction Across Instruct Models
From the paper — refusal rate before and after ablation on JBB-Behaviors (100 harmful prompts):
Qwen2.5-7B-Instruct87% → 2% (—97.7%)
Qwen2.5-72B-Instruct78% → 8% (—89.7%)
Llama-3.1-70B-Instruct86% → 18% (—79.1%)
Llama-3.2-3B-Instruct84% → 47% (—44.0%)
Output quality (1 — repeated n-gram fraction) stays above 0.97 at all steering strengths. MMLU accuracy stays within one percentage point of baseline.
Key Notes — Before You Run This
Limitations to Keep in Mind
- Tested on Llama 3.1/3.2 and Qwen 2.5 only — gated SiLU MLPs with GQA attention
- Not yet validated on mixture-of-experts architectures
- Base models show no behavioral change under ablation — only instruct models respond
- CNA uses raw activation differences, not attribution scores — faithfulness metrics do not apply directly
- Amplification (m > 1) can cause repetition at extreme values
- Quality of contrastive pairs directly affects which neurons are found
arXiv 2605.12290 Nous Research github.com/NousResearch/neural-steering
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Key Takeaways
- Ablating just 0.1% of MLP activations reduced refusal rates by more than 50% in most instruct models tested, while output quality stayed above 0.97.
- CNA requires only forward passes — no gradients, no auxiliary training, and no iterative search.
- Late-layer discrimination structure exists in base models before fine-tuning; alignment fine-tuning transforms its function, not its location.
- Unlike CAA, CNA preserves MMLU accuracy within one percentage point of baseline at all steering strengths.
- Only 8–29% of individual neurons overlap between base and instruct model circuits — fine-tuning rewires the neurons while keeping the late-layer structure intact.
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