"to" after made or need-type verbs
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 lights up on the word "to" when it introduces an action someone or something is forced, made, or needed to do, as in "made to work" or "need people to fight." It goes quiet, and slightly negative, in "beaten to death." Turning it up made the model's writing drift into unrelated rambling rather than anything about this pattern.
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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a Chemistry Tutor<|endoftext|>Those who fight for us, also need people to fight for them.⏎The military physically protects us and sacrifices for us, yet some
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Of International Conference On Communication Science⏎This work is licensed under a Creative Commons Attribution 4.0 International License.<|endoftext|>How do you explain how babies are made to an 8-year-old?⏎This topic has been the subject of countless jokes over
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at 18:48 BST⏎It is a form of modern day slavery otherwise known as forced labour. Millions of men, women and children are made to work against their will for little or no pay.⏎The UN's International Labour Organisation
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, liberty or possessions; for men being all the workmanship of one omnipotent and infinitely wise Maker… they are His property, whose workmanship they are made to last during His, not one another's pleasure.⏎This idea found its way into
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what counts as ‘health data’.⏎We put forward evidence that the datafication of health has created an ‘Internet of Health’, in which everyone is made to perform as a patient, subject to continuous monitoring, diagnosis and prediction. We ask
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The substrate is made to be the cathode and the coating the sacrificial corroding anode. An
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: "I am, as the first time, the one I have been thinking about... but I need a little more than that." I need to be clear, and I need... and I need again and again. Let me see... and
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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the woman's story, and when the crowd took notice of a hearse parked outside of the store, the rumor began to circulate that Rivera had been beaten to death. The woman who had raised the alarm was arrested for disorderly conduct,
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Pandits fled the very next day. In April 1990, a Kashmiri Pandit nurse Sarla Bhat was gang-raped and beaten to death, in June Girija Tickoo was also gang-raped and then sliced
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.⏎Once again, Leah made my dream come to my life. Her chameleonic beauty makes it so easy to transform in every possible character, from Sleeping Beauty to Wonderwoman, to my very own Girl with a Pearl Earring.⏎To
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century it was burned to the ground and was rebuilt shortly afterwards only to be abandoned a century later, the
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
- L4:227 suppresses
- L3:1152 suppresses
- L4:328 suppresses 'to' after 'so as' and infinitive cues
- L2:1883 weakly suppresses
- L3:2734 weakly excites
- L4:1254 weakly excites
- L4:1909 weakly excites
- L3:2613 weakly suppresses
- L3:2358 weakly suppresses
- L1:906 weakly suppresses
- L3:1703 weakly suppresses
- L4:90 weakly suppresses
- L2:1634 weakly excites
- L3:1573 weakly suppresses
- L3:1227 weakly excites
- L3:987 weakly excites
- L4:771 weakly suppresses
- L1:1949 weakly excites
- L0:2842 weakly suppresses
- L0:2394 weakly suppresses
- L0:1132 weakly suppresses
- L4:2693 weakly excites
- L2:1790 weakly excites
- L4:1381 weakly suppresses
Feeds later layers
- L7:681 excites 'from' after prevent or stop
- L17:2834 excites
- L11:2149 excites
- L6:1022 excites
- L8:369 suppresses 'of' after 'learned' or 'because'
- L6:2841 weakly excites
- L19:2724 weakly suppresses
- L16:1291 weakly excites
- L18:2120 weakly excites
- L8:132 weakly excites
- L8:2146 weakly excites "as" in "it seems as if" phrases
- L11:1641 weakly excites
- L12:896 weakly excites
- L20:1290 weakly suppresses
- L17:1661 weakly excites
- L17:2368 weakly excites
- L6:233 weakly excites
- L15:1047 weakly excites
- L17:1994 weakly excites
- L16:1836 weakly excites
- L6:1209 weakly excites
- L17:1495 weakly excites 'without' introducing a contrast
- L14:1096 weakly suppresses
- L8:1491 weakly excites
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
- ��
- .Func
- orque
- insula
- behaving
- :NS
- ORIA
- },⏎⏎
- 操纵
- })(
Suppresses
- zzo
- ilitation
- 卡
- زة
- geführt
- bursement
- iotic
- lette
- cluding
- ckt