word just before a 'reason is' explanation
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. 19 of Qwen3-0.6B. Its strongest snippets are mixed: they peak on different tokens. 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 lights up on the word right before a point is explained, like 'the reason was simple' or 'for good or ill'. It seems to expect that an explanation is coming next. Turning it up made the text wander without a clear theme.
Written by Claude from this neuron's activations and steering outputs. Confidence: medium. 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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down all of your classes, the assignments, the time and day things are due. For some men, this could be very helpful. Planners exist for a reason–they actually work. Maybe it'll help you be more responsible in school.⏎
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seen as newsworthy.⏎But the closure of the Samchully factory attracted the attention of the major media. The reason was simple: the factory was the last in Seoul to produce coal briquettes, known
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The latter can be defended, but the former demands us to jettison its nationalistic undertones, and implicitly acknowledges the complexities of nationalism that drives societies for good or ill.⏎Nationalism becomes
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are essential for keeping millions of women and families above the poverty line. We know that women disproportionately rely on these programs, making the message from the state poverty data clear: Programs for low-income families and individuals must be protected. We sincerely hope Congress
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) tells us what the audience will see and hear, starting with the beginning<|endoftext|>Unearthing Ancient History in Tuscany⏎The goal of this expedition is simple: to help experts reconstruct the complex past of Populonia as fully as possible
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clearly accelerating rapidly – from a crawl in biblical times, to a jog in the 20th century, to a sprint now.⏎The reason for this is simple. The more technology you have at your disposal, the easier it is to invent
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: to in from of from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from from
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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of Superfund Sites in the United States. As part of NIEHS's Superfund Research Program, the Puerto Rico Testsite for Exploring Contamination Threats (PROTECT) is exploring the relationships
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War, either through their own family history, links to their local community or because of its long-term impact on the world we live in today. Through projects like Faces of the First World War and the
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public to help find out more about all the servicemen whose images make up the online exhibition but about whom very little else is known.⏎The collection of photographs - Faces of the First World War - is part of the IWM's preparations to mark
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side Congress and the CFPB, the Native American Financial Services Association (NAFSA) is dedicated to providing financial literacy to everyone. Through their Financial Literacy Program, NAFSA provides free financial literacy modules and tools with easy-to-under
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
- L18:579 excites
- L17:1167 excites
- L16:1855 excites
- L17:870 suppresses
- L13:2078 excites
- L2:86 excites
- L18:2748 excites
- L17:118 weakly suppresses comma after a clause ending
- L18:519 weakly excites
- L17:1111 weakly excites
- L16:1375 weakly suppresses
- L16:163 weakly suppresses
- L8:1955 weakly suppresses
- L6:2950 weakly suppresses
- L17:1039 weakly suppresses
- L15:1554 weakly excites
- L16:2715 weakly suppresses
- L15:2459 weakly suppresses
- L17:1639 weakly excites
- L17:89 weakly excites
- L17:677 weakly suppresses
- L9:7 weakly suppresses
- L11:2484 weakly suppresses
- L17:1771 weakly suppresses
Feeds later layers
- L22:132 suppresses
- L20:55 excites
- L22:160 excites
- L27:453 suppresses
- L21:140 excites
- L20:833 suppresses
- L20:873 suppresses
- L20:2925 suppresses
- L21:875 suppresses
- L20:1327 suppresses
- L22:1481 suppresses
- L21:514 suppresses
- L22:288 suppresses
- L24:116 excites
- L24:333 suppresses
- L27:338 suppresses
- L20:450 suppresses
- L23:570 suppresses
- L26:711 suppresses 'years' after an age number
- L21:2783 suppresses
- L21:818 suppresses
- L25:553 suppresses
- L25:277 suppresses
- L26:585 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
- 煌
- 番
- جة
- ühr
- werp
- 宙
- 不清
- _Msk
- 正当
- emean
Suppresses
- ForKey
- of
- _of
- Of
- -of
- Of
- for
- ForObject
- ofType
- _for