sentence-end punctuation in speech
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 fairly consistent: several of them 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 full stop or question mark that closes a sentence, especially in spoken or explanatory talk. When it was pushed down the model fell into repeating filler like 'in fact' and 'indeed'.
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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the only reason I can acturally comment on it is because I have my C book here with an example of a recursive function to calculate factorials... heh. Sadly I don't think I'm *allowed* to post the code up.
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say I think … yeah. I think you’re talking about more than 80 million people.⏎Wow. Yeah. Okay. Can you define it for us? Can you describe what insulin resistance is in layman’s terms?⏎So to understand
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have made a lot of cattle grazing land. And as a result their natural resources such as wallabies are disappearing off the land and they’ve got to eat something. And so sheep are easy to catch and there’s a lot of meat on them
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you all are waiting for. Teachers, parents and student, now you will find below the Solutions of Class 10 Science Chapter 4 Environmental management questions answers. From the subject English of Maharashtra State Board.⏎Below you can see
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-happy all of the time? No. But, they have a better chance than the children of non-authoritative parents.⏎Sounds kind of like authoritative, right? Sure. In reality this is a very different type of parenting style. Like authoritative
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, the more vegetables kids will eat.⏎"It's not just mom or dad is making me eat it, but I want to eat it because I helped grow it. If a child pulls a carrot out of the dirt they're more likely to eat
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: in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact, in fact,
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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with are called one-to-one functions. DEFINITION A function is called a one-to-one function if it never takes on the same value twice; that is, If a horizontal line intersects the graph of in more than one point
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what a third neural network named Eve learns from eavesdropping on the communication between Alice and Bob. We do not prescribe specific cryptographic algorithms to these neural networks; instead, we train end-to-end, adversarially. We demonstrate that the
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important to clarify what exactly a percentage is, and this is nothing more than a quantity or number that represents a fraction of the number 100.⏎In other words, it is a part of a general number that should be considered divided by
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izations. These unique doublets give the axoneme an inherent structural polarity. All three specializations are present in the proximal portion of the axoneme; based on their frequency in random cross-sections of isolated axonemes, 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
- L14:155 excites sentence-ending periods in prose
- L11:132 excites period ending an opening topic sentence
- L13:53 excites period ending a claim before elaboration
- L17:347 excites full stops ending informative sentences
- L18:227 suppresses
- L9:2447 excites
- L10:612 excites full stop ending boilerplate or a title
- L9:16 suppresses unclear: periods inside numbers and names
- L5:168 suppresses
- L18:2434 excites
- L17:944 suppresses
- L6:250 weakly excites
- L18:2613 weakly suppresses
- L15:403 weakly suppresses period inside an abbreviation
- L16:432 weakly suppresses period after month or initial
- L1:1384 weakly suppresses
- L9:12 weakly suppresses
- L16:1112 weakly suppresses
- L16:1545 weakly excites
- L18:2658 weakly suppresses
- L14:1187 weakly suppresses
- L17:348 weakly excites
- L8:304 weakly suppresses
- L4:102 weakly excites
Feeds later layers
- L22:1416 suppresses
- L22:1729 suppresses
- L21:1490 excites
- L20:727 suppresses
- L20:173 suppresses
- L21:809 suppresses
- L20:2891 suppresses
- L24:887 suppresses
- L22:885 excites
- L21:1132 suppresses
- L20:513 weakly excites
- L20:1519 weakly suppresses
- L20:2354 weakly excites
- L20:2634 weakly suppresses
- L21:642 weakly suppresses
- L26:236 weakly suppresses
- L20:2718 weakly suppresses
- L20:501 weakly excites
- L22:2020 weakly suppresses
- L27:1084 weakly suppresses
- L21:1070 weakly suppresses
- L21:2135 weakly suppresses
- L21:1117 weakly excites
- L27:677 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
- because
- 因为它
- -purpose
- Purpose
- sie
- Few
- covering
- lander
- aña
- way
Suppresses
- indeed
- 实际上
- moreover
- 确实
- actually
- действительно
- furthermore
- 事实上
- 的实际
- 確實