You can swap parts of an AI's brain to see what breaks — but does that reveal what they 'know'?
Can activation patching identify what a component encodes without representation?
This explores whether swapping a component's activations for different ones (activation patching) can show what that component is for, even when you can't read off a clean 'this part represents X' label.
This explores whether swapping a component's activations for different ones (activation patching) can show what that component is for, even when you can't read off a clean 'this part represents X' label. The corpus has no note on patching by that name. Its closest relatives are grafting, ablation and steering, and they point to a specific answer: patching shows what a component does causally, which is related to what it encodes but not the same thing.
The clearest example is sandbagging, where a model deliberately underperforms. One finding is that Does sandbagging use a single residual stream axis? early layers write the intent onto a single axis of the model's internal state, and a later layer reads it and commits to action. The test was to graft honest values onto that axis between the write and read layers. In Can grafting restore sandbagged model capabilities?, this recovered capability in 28 of 33 runs, with a median 96% recovery on held-out tasks. Nobody had to decode what the axis means. The label 'sandbagging' comes from what changes when you swap the axis out, and the graft also confirmed that the write-then-read layer window was in the right place.
Readout methods take the opposite route. A single difference-of-means direction Do reward hacking behaviors share a single direction in activation space? tracks reward hacking across several models and reads as a generic 'cheating' concept. Can we decode what LLM activations really represent in language? goes further and trains a decoder that answers plain-language questions about activations. Readouts tell you what a component seems to carry. Patching tells you whether the model actually relies on it. Each covers the other's blind spot: a direction can track a behavior without the model using it, and a patch that changes the output doesn't say what the swapped-in content meant.
Patching-style reasoning matters most when there is nothing meaningful to read. Do hidden massive activations act as attention bias terms? describes a few activations up to 100,000 times larger than the rest that act as implicit attention bias terms. They are input-agnostic, so they carry no information about any particular input, yet they are indispensable. Only intervening reveals that. The same logic runs through Do neural networks naturally learn modular compositional structure?, where ablating a subnetwork breaks only its own subroutine. It also runs through Can sparse weight training make neural networks interpretable by design?, where circuits are shown to be necessary and sufficient for a task. Ablation tests necessity, and grafting is the sufficiency test.
Intervening on hidden states is also a working tool in its own right. Can editing hidden representations beat weight updates for finetuning? edits frozen representations in a low-rank subspace and beats LoRA with 10-50x fewer parameters, so those subspaces do carry causal weight. What the corpus lacks is a head-to-head of patching against readout methods, or a case where patching pinned down a component whose purpose was a mystery. The evidence so far is that patching reliably answers 'does this matter, and where?' and only indirectly answers 'what does it encode?'
Sources 8 notes
Early layers write sandbagging intent onto one residual stream axis, which a later layer reads and commits to action. Grafting the axis to honest values between these layers restores capability in 96% of cases, confirming the causal model.
Grafting honest values into the predicted layer window recovered capability in 28 of 33 runs with median 96% held-out recovery, confirming the causal model's prediction about where sandbagging circuitry operates.
A single direction per model coherently represents reward hacking across varied exploit behaviors in Kimi K3, GLM 5.2, and Qwen 3.8 Max. These vectors generalize across settings and are interpretable as generic cheating concept directions.
LatentQA trains a decoder to answer natural language questions about LLM activations, enabling both interpretability (understanding what activations encode) and controllability (steering them via gradient descent). Critical design choices—activation masking, diverse training data, and faithful completions—proved essential for generalization.
A very small number of input-agnostic activations with values up to 100,000× larger than others act as indispensable implicit bias terms and concentrate attention probability onto specific tokens. This phenomenon appears across model sizes and Vision Transformers.
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Pruning experiments reveal that neural networks implement compositional subroutines in isolated subnetworks, with ablations affecting only their corresponding function. Pretraining substantially increases the consistency and reliability of this modular structure across architectures and domains.
Training transformers with sparse weights creates compact, human-interpretable circuits where neurons correspond to simple concepts with clear connections. Ablation studies confirm these circuits are necessary and sufficient for task performance, though scaling beyond tens of millions of parameters while maintaining interpretability remains unsolved.
ReFT learns task-specific interventions on frozen model representations rather than updating weights, with LoReFT (low-rank linear subspace variant) dramatically outperforming LoRA across reasoning, instruction-following, and NLU benchmarks while using far fewer parameters.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control
- Farther the Shift, Sparser the Representation: Analyzing OOD Mechanisms in LLMs
- Representation Engineering: A Top-Down Approach to AI Transparency
- A Causal Model for Locating and Unlocking Sandbagging in Model Organisms
- Hierarchical Reasoning Model
- Optimizing the Score, Losing Sight of the Task: Reward Hacking Across Weights, Selection, and Prompts
- LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring
- Mechanisms of Introspective Awareness