question mark after a rhetorical question
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. 17 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 question mark at the end of a question an essay asks itself, like 'But what exactly is mulch?'. Turning it up made the model's output wander into loose, unfocused text, with no clear topic.
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 practice isn’t effective we run the risk of making permanent misconceptions.⏎But what is “perfect” practice? Can “perfect” practice really exist? I do not think that “perfect”
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other, because both incur charges of “Denier!” to those who dare to question the consensus. Yet how solid is the consensus on human-caused climate change? Consider some recent news:⏎Angkor Wat’s Collapse From Climate Change Has Lessons for
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of Damascus,<|endoftext|>You’ve probably heard the term or maybe know someone who claims to be following such a food regimen: Clean Eating. What exactly does it mean? I read several articles, all with varying definitions and exceptions. I’ll do my
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question is whether the evidence is substantial enough to claim a discovery. And if a discovery can be claimed, the final question is what, exactly, has been discovered? Scientists can have complete confidence in their results but remain uncertain about how to interpret them
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doubled, to over 1.2 million between 2001 and 2011.⏎But how prevalent are highly diverse neighbourhoods across England? And should we expect their numbers to keep growing?⏎Multi-ethnic neighbourhoods in
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not the only thing requiring amends, and Indigenous people still face a great deal of social issues in modern day Canada. So what is the extent of these issues? And most importantly, what is being done about it?⏎Whilst only constituting
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 now, now and now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now
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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and building was sanctified" [POOLE].⏎the tower of Meah--This word is improperly considered, in our version, as the name of a tower; it is the Hebrew word for "a hundred," so that the meaning is:
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“pumpkin pie” smoothie! Place the following in a blender:⏎• 3-4 tablespoons of canned organic pumpkin (just the pumpkin puree – because pumpkin pie filling has lots of added sugar)⏎• Two tablespoons of Greek yogurt
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ron sputtering is widely used to deposit thin films on a range of substrates, including glass and ceramics. The technology can be easily scaled to larger dimensions; it offers a high deposition rate (allowing a high level of productivity) and
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people.) For pints, use one tablespoon bottled lemon juice or ¼ teaspoon citric acid. Acid can be added directly to the jars before filling with product; in fact, this is recommended to be sure you get the acid in each and
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
- L9:103 excites "as" or "since" giving a reason
- L16:1112 suppresses
- L15:403 suppresses period inside an abbreviation
- L9:12 suppresses
- L9:7 excites
- L4:96 excites
- L1:441 weakly suppresses
- L10:33 weakly suppresses
- L14:35 weakly suppresses
- L14:1209 weakly excites
- L15:1861 weakly excites
- L10:612 weakly excites full stop ending boilerplate or a title
- L11:132 weakly excites period ending an opening topic sentence
- L15:1260 weakly excites
- L14:451 weakly excites
- L16:1081 weakly suppresses
- L9:1113 weakly suppresses
- L8:304 weakly suppresses
- L16:797 weakly excites
- L6:250 weakly excites
- L9:819 weakly suppresses
- L13:345 weakly excites
- L12:878 weakly suppresses
- L10:1459 weakly suppresses
Feeds later layers
- L25:1316 weakly suppresses
- L26:405 weakly suppresses
- L25:1902 weakly excites
- L19:2811 weakly excites
- L18:613 weakly excites
- L18:2262 weakly suppresses
- L27:528 weakly suppresses
- L20:1190 weakly excites
- L24:2610 weakly excites
- L19:2041 weakly suppresses
- L24:1630 weakly excites
- L25:553 weakly excites
- L19:43 weakly suppresses
- L19:2787 weakly suppresses
- L24:2581 weakly suppresses
- L26:825 weakly excites
- L18:2593 weakly excites
- L26:509 weakly excites
- L27:1244 weakly suppresses
- L22:2701 weakly suppresses
- L25:509 weakly excites
- L20:467 weakly excites
- L19:973 weakly excites
- L24:205 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
- ellers
- ryo
- propri
- Overs
- }`
- plete
- appers
- }\"
- ltr
- bers
Suppresses
- 这点
- 这也
- 这样
- thus
- así
- 这项
- this
- 这样一个
- hence
- 这么