full stops closing a claim
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 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
The neuron fires on the full stop or question mark that ends a sentence making a complete point, especially in persuasive or explanatory English, such as 'Either true or false.' Turning it up did not produce anything clearly related, mostly drifting into Chinese text and repetition.
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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Non-cognitivists – moral statements are non-propositional and therefore subjective.⏎Propositional – deal with verifiable facts. Either true or false. We can legitimately ask ‘is it true?’⏎Non-propositional statements are
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day, the Government commissioned London’s sewer network, and one Thomas Crapper exploited the situation by patenting a range of flushing toilets.⏎And there we have it. For over 150-years the technology behind the flushing toilet has been faithfully
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in the future.⏎Our parenting advice is given as suggestions only. We recommend you also consult your healthcare provider, and urge you to contact them immediately
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But that’s neither here nor there. And kind of gross.)⏎It should be noted that the high diffusivity of caffeine is hardly a unique property, however. There are plenty of other compounds that have similar chemical backbone structures and will behave very
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so we don’t become rebellious and wayward.⏎We have two aspects. One is forgetful. It becomes wayward sometimes, absorbed with things that are
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ago, an ultra-conservative Floridian took his case to his state supreme court to fight for his right to wear a mask? Yep. Is this fake news? Nope.⏎So what gives? Why do so many people now philosophically insist that
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: that which which, which in which, with which, that which of which which which... on which, which which... which... in which, which which, with which, which, with which on which, with which on which... in
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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法、分析法来进行研究,探讨泰国学生在学习汉语助动词中的困难及偏误情况,分析他们产生偏误的原因。我们发现经过基于对比研究设计的汉语助动词课堂教学后,“
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东方在线 发布人:young⏎14. Energy Resources⏎China is rich in various energy resources.
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對 CNY 感興趣?⏎Early Currency in China⏎With a history of over 3000 years
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secretpassword';⏎mysql> GRANT ALL PRIVILEGES ON database_name.* TO user_name@localhost;⏎
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
- L13:44 strongly suppresses inside web addresses and citations
- L10:155 strongly excites
- L18:66 strongly suppresses
- L15:59 strongly suppresses
- L8:328 excites
- L11:223 excites
- L18:409 excites
- L18:515 suppresses
- L12:158 suppresses
- L15:390 excites
- L18:178 excites
- L17:56 suppresses
- L10:669 suppresses
- L18:227 suppresses
- L16:762 excites
- L7:92 excites
- L15:184 excites
- L9:74 excites
- L4:76 excites
- L18:382 suppresses
- L4:342 suppresses
- L16:513 excites
- L17:473 suppresses
- L17:35 excites
Feeds later layers
- L23:105 strongly suppresses
- L20:296 strongly excites
- L23:25 strongly excites
- L27:19 strongly excites
- L20:473 strongly excites
- L27:59 strongly excites full stops in running text
- L26:372 strongly excites
- L23:278 strongly excites
- L27:452 strongly excites
- L26:277 strongly excites
- L23:509 strongly excites
- L25:242 strongly suppresses
- L25:198 strongly excites
- L27:224 strongly excites
- L20:940 strongly excites
- L27:508 strongly excites
- L24:192 strongly suppresses
- L24:400 strongly excites
- L21:370 strongly excites
- L27:100 strongly excites
- L27:258 strongly excites
- L23:242 strongly excites
- L27:157 strongly excites
- L24:421 strongly 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
- And
- 既
- 就
- And
- Both
- 要
- 成
- Of
- Of
- The
Suppresses
- 四个意识
- แพทย
- yling
- myself
- as
- 今天小编
- lingu
- からは
- 虿
- with