'trying' after 'were' or 'are'
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. 20 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 'trying' when it comes right after a pronoun and a form of 'to be', as in 'they are trying to' or 'you're trying to'. Its strongest negative reactions are on odd word pieces like 'star' in 'star-crossed'. Turning it up made the model produce repetitive, off-topic text, so the link to 'trying' is not confirmed by the steering.
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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faced with thousands of choices and for the student, such choice is overwhelming and confusing. By studying how and why other writers made decisions based upon the effects they were trying to produce in the reader, the student writer is guided through this confusion.⏎But
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lyrics in the song. If your melody sounds morose, sing words out that echo the sadness of the lyric, not something happy or silly, unless you're trying to convey some sense of irony. Sing whatever comes naturally. Sometimes a part of
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they afraid that they would again have a standing-room-only crowd of concerned citizens rejecting the HCP, as they did in 2016? Are they trying to avoid having to answer questions about why their flawed analysis claims the HCP is
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the hell we're doing, if you know C and have worked as a computer programmer a few years, you probably already have a rough idea of what I'm trying to do with those declarations. Humans read code, and humans infer things from the
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that will eventually form words. When she babbles and vocalizes now, your baby may sound as if she's making sense. That's because she's trying out tones and patterns similar to the ones
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structures (for example, "promoters") are not as sensitive to base pair sequences as proteins are to amino acid sequences, but these are exceptional; we are trying to account for the development of the large number of proteins that are very specific and
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 20th and 19th of the 2nd and 1st of a new date. Let me be more explicit. Let's say a new date is 1 and 2, then the 20
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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dells are much bothered by such conditions, with some sporting the beer-belly equivalent of eight centimeters of blubber. But what about nursing mothers or half-starved juveniles still honing their foraging skills?⏎“Smaller, lean
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formula is used when nothing about the behavior of a population is known at all. * How to Use Slovin's Formula⏎Slovin's formula is written as
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life it was her own foolish choice and actions that lead to her and Romeo's death. Though the death of Romeo and Juliet was a tragic love story of two star-crossed lovers, caused by their families and friends, more specifically Friar Lawrence
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Siefker, Daniel Wenger, Matias Mancini, John Thompson, Chris Krish, and Alvero Wiggins for supporting us this week.⏎NOTE: StarTalk+ Patrons can watch or listen to this entire episode commercial-free.⏎About
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
- L18:778 strongly excites
- L18:1474 suppresses
- L2:1724 suppresses
- L2:1975 suppresses
- L17:1313 excites
- L17:1128 excites
- L16:43 excites
- L15:115 excites
- L16:184 excites
- L5:954 suppresses
- L16:31 suppresses
- L16:1304 excites
- L16:92 excites
- L18:80 suppresses
- L5:244 suppresses
- L15:129 suppresses
- L10:951 suppresses
- L15:96 suppresses
- L15:89 excites
- L15:82 suppresses
- L10:947 excites
- L6:2680 excites
- L4:1716 suppresses
- L6:299 weakly suppresses
Feeds later layers
- L26:1367 strongly suppresses unclear: asterisk symbols in mixed text
- L21:1745 excites
- L21:1877 excites
- L23:2163 suppresses
- L21:1980 weakly excites
- L21:334 weakly suppresses
- L21:1016 weakly excites
- L21:1661 weakly excites
- L21:1246 weakly excites
- L26:2228 weakly excites
- L26:1428 weakly suppresses
- L26:2672 weakly suppresses
- L25:413 weakly suppresses
- L24:593 weakly suppresses
- L22:1848 weakly excites
- L26:1921 weakly suppresses
- L26:1638 weakly excites
- L26:1660 weakly suppresses
- L27:2975 weakly excites
- L27:1373 weakly excites
- L26:1190 weakly excites
- L26:2675 weakly excites
- L25:563 weakly suppresses
- L21:1890 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
- иров
- apolis
- ита
- нят
- пон
- urances
- ировки
- riendly
- pector
- ite
Suppresses
- light
- 睛
- nd
- icer
- pes
- بق
- MER
- 候
- .yahoo
- Terraria