the modal 'can' after 'there' or 'it'
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. 24 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 "can" when it means "is possible", especially in phrases like "there can be" or "it can be". When it was turned up, the model got stuck repeating "can't be able to", so this neuron seems tied to talking about what is possible.
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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be done when the beetle is dormant to prevent further spreading, Eddy says. She adds, "There is no one textbook way of dealing with this because there can be so many different circumstances."⏎For now, the city wants to educate residents about
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had access to more general care, the physical body second. This has all changed in the past few years.⏎Mental health professionals and organizations now know that there can't be a separation between a brain and body when it comes to effective psychiatric treatment
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ose rich foods. For these people there can be many negative side effects to ingesting dairy products that include but are not limited
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the various agencies are getting closer and closer to finding aliens, while at the same time, we have almost no idea about how powerful the military technologies of the aliens can be and whether they will be friendly to the human or not; I start to
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Icon (1954). They correctly criticized the belief that the intention of the author was the one and only correct interpretation of a work. Certainly, there can be other valid interpretations of a work, and of course the literature scholar should not
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know how difficult it can be to get into a story and then keep readers engaged while they wait for the
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: the first instance in the context of the sushput is sushput is the first part of the sushput sushput sushput sushput sushpush. sushpush is the sushpush sushpush
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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-ethnic neighbourhoods in England, by Dr Gemma Catney, Professor Richard Wright, and Professor Mark Ellis. The original article, posted in Geography Directions, can be accessed here. You can also read the full article published in Transactions of the
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that he would eliminate most U. S. cabinet departments, including those overseeing commerce, education, energy, and labor (some of this material and references for it can be found in my An Age
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You can see where it is in the solar system on the JPL Small Body Database Browser. Here is the map for today:⏎As you can see in the map, it's a main belt asteroid, orbiting the Sun
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a sentence that is split over several lines in HTML. </BODY> </HTML>When this is displayed by Mosaic, it will look like:⏎As you can probably guess, commands are enclosed in angle-brackets <>, so that the HTML
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
- L17:953 strongly excites the word 'can' after a subject
- L19:1473 excites "can" after a noun in a clause
- L4:173 suppresses
- L10:384 excites
- L21:2007 suppresses
- L3:978 suppresses
- L19:2701 suppresses
- L18:1491 suppresses
- L15:886 excites
- L20:2097 suppresses
- L10:2166 excites
- L23:30 suppresses
- L21:262 excites
- L0:1372 weakly suppresses
- L20:636 weakly suppresses
- L5:387 weakly excites
- L11:360 weakly suppresses
- L19:545 weakly excites
- L13:2339 weakly excites
- L10:562 weakly excites
- L16:127 weakly suppresses
- L5:1203 weakly suppresses
- L20:1897 weakly suppresses
- L18:2634 weakly excites
Feeds later layers
- L27:774 strongly excites
- L27:2857 excites
- L27:1802 excites
- L27:1815 weakly excites
- L25:2101 weakly excites
- L25:1821 weakly excites
- L25:752 weakly excites
- L25:757 weakly excites
- L26:1853 weakly excites
- L26:1370 weakly suppresses
- L27:388 weakly excites
- L27:1021 weakly excites
- L26:1645 weakly suppresses
- L27:2079 weakly excites
- L25:2021 weakly suppresses
- L27:2103 weakly excites
- L27:1011 weakly suppresses
- L27:955 weakly excites
- L27:1464 weakly excites
- L25:1617 weakly excites
- L27:2230 weakly excites
- L26:218 weakly suppresses
- L27:2314 weakly suppresses the letter l starting a word fragment
- L26:2655 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
- 能
- can
- 可以
- can
- 可
- 能够
- 能夠
- Can
- 能在
- →can
Suppresses
- reta
- WORDS
- 둥
- unched
- credentials
- выполнен
- 酋
- chimp
- 闻
- htags