'to' after 'what' or 'where' (what to do)
A neuronOne of thousands of tiny dials inside each layer. It reads the text so far and produces a number, its activation, that nudges what the model writes next. in layerOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. 5 of Qwen3-0.6B. Its strongest snippets are very consistent: they mostly peak on the same token. It is featuredA neuron picked for the full treatment: steering outputs and a written explanation. About 3,000 of the 86,000 neurons are featured., so it has steering outputs.
What this neuron does
This neuron fires on the word 'to' when it follows 'know what' or 'where', as in 'know what to do' or 'where to start'. It is pointing at the start of a phrase that says how to act or what to pick. Turning it up made the model write about what to say or what to do next, which fits the trigger reasonably well.
Written by Claude from this neuron's activations and steering outputs. Confidence: high. Think it is wrong? Flag it (opens a prefilled issue on GitHub).
Fires most on
The text windows where this neuron's activationThe number a neuron produces for a token. Large positive means it fired hard; negative means it pushed the other way. was highest. Amber marks positive activation; the outlined tokenA piece of text the model reads at once: often a word, sometimes part of a word or a punctuation mark. is the peak. Hover a token for its strength.
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our lives is to improve our experiences.⏎Improvement requires change, and change requires work. And with encompassing holistic changes, most people are challenged to know where to start.⏎In epistemology, the philosophical study of knowledge, “tabula
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library supports. Invoking the tag library imports custom processing and actions into the Java environment so a Java runtime environment like JRun 3.0 will know what to do with such tags as they're recognized. Tag libraries are usually packaged as Java
SteeringAdding a push along a neuron while the model writes, to see how its output changes. Turning it up or down.
Push this neuron up or down while the model writes, and watch what changes. The outputs were generated ahead of time, so nothing runs in your browser.
Here is something I have been thinking about lately: let's go! Wait, I can't do that. Wait, let's go! Okay, let's see! Let's go! Wait, let's go! Okay, I'm not sure about this
Most negative on
Where the activation went furthest below zero. Blue marks negative activation. Many neurons mean something different in each direction.
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given/known data If an athlete jumped 2 feet high and left the ground at an angle of 20 degrees with respect to the horizontal, how fast was the athlete going in the forward (positive horizontal) and upward (positive vertical)
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Connections
Show on mapWhich neuronsOne of thousands of tiny dials inside each layer. It reads the text so far and produces a number, its activation, that nudges what the model writes next. in earlier layersOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. feed this one, and which later ones it feeds. Amber links excite, blue links suppress; longer bars are stronger.
Fed by earlier layers
- L3:393 excites
- L4:1936 weakly excites
- L4:2855 weakly excites
- L1:2354 weakly excites
- L3:368 weakly excites
- L4:182 weakly suppresses
- L4:423 weakly excites
- L0:599 weakly suppresses
- L2:899 weakly suppresses
- L1:1779 weakly excites
- L2:410 weakly suppresses
- L4:12 weakly suppresses
- L0:2931 weakly suppresses
- L2:2551 weakly suppresses
- L3:2369 weakly suppresses
- L1:1515 weakly excites
- L3:456 weakly suppresses
- L3:842 weakly suppresses
- L1:2440 weakly suppresses
- L2:1106 weakly excites
- L4:87 weakly excites
- L3:843 weakly excites
- L4:443 weakly excites
- L1:2083 weakly suppresses
Feeds later layers
- L8:2795 excites
- L6:781 excites
- L7:1651 excites
- L21:1427 excites
- L10:2169 weakly excites
- L17:1822 weakly excites 'should' in advice and claims
- L19:2844 weakly excites
- L7:2459 weakly excites
- L15:955 weakly excites
- L8:973 weakly excites
- L11:1514 weakly suppresses
- L22:798 weakly suppresses
- L15:522 weakly excites
- L15:1986 weakly excites
- L12:2544 weakly suppresses
- L9:556 weakly excites
- L19:2472 weakly excites
- L6:1921 weakly suppresses
- L8:632 weakly excites
- L10:1121 weakly excites 'to' or verb after 'what' or 'whether'
- L9:1789 weakly suppresses
- L13:1687 weakly excites
- L17:2171 weakly suppresses
- L8:1169 weakly suppresses
Activation histogramA chart of how often a neuron takes each activation value.
How often this neuron fires at each strength. Almost always it does close to nothing (the tall bars in the middle); the snippets above are the rare moments it fired hard, amber for positive and blue for negative.
Logit lensReading which output tokens a neuron pushes toward or away from, straight off its output weights, before the later layers have their say.
The words this neuron nudges the model toward (amber) or away from (blue) when it fires. A hint, not a label: many neurons work through later layersOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. instead.
Promotes
- 应当
- 最合适
- should
- altung
- Should
- ала
- abilidade
- nels
- кал
- 应该
Suppresses
- ored
- ouri
- orary
- 蝠
- ChangeEvent
- \V
- pressive
- cono
- ,dim
- our