'be' after modal verbs like 'can'
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. 1 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 'be' right after a modal such as 'can', 'need to' or 'can also', as in 'can be used'. Turning it up did not produce an obvious change in what the model wrote about.
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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building cascading deck stairs, it is important to make sure that the stairs are properly supported and that each stair is of the same height. The following steps can be used to build cascading deck stairs: 1. Decide on the height of
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renewable sources to (1) convert CO2 into carbon-neutral fuels, such as methanol, or (2) transform biomass into long-chain molecules that can also be used as fuel.⏎CO2 is kinetically and thermodynamically inert, making
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not include the transfer of personal property on that real property.⏎Determining Real vs. Personal Property⏎The following methodology can be used to determine whether an asset is personal or real property.⏎Method of attachment:
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high incidence of asbestos exposure. More specific information about asbestos exposure on the following jobs can be found at Asbestos Network.⏎Insulators, or people that worked extensively around asbestos
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/AIDS requires an increased amount of investment. These investments need to be amplified in both sexual education and innovative HIV/AIDS prevention programs.⏎Both the Executive Director
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atmosphere. This project aims to prove the feasibility of using a technique to separate water vapor from the exhaust stream to generate a high energy steam cycle, which can then be used to help reduce the engine's fuel consumption.⏎Commenting on the projects,
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 have been thinking about the idea of a well-known, well-known, and well-known as a number of well-known and well-known in the same area, and the same area, and the same area. I think this is the first
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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should watch the talk and decide whether the sentences are true or false according to the video. This general English lesson plan finishes with a short
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. incompatible persons or things.⏎2. an incompatible drug or the like.⏎3. incompatibles, Logic.⏎a. two or more propositions that cannot be true simultaneously.⏎b. two or more attributes that cannot simultaneously belong to the same object
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in character; discordant: incompatible colors.⏎3. that cannot coexist or be conjoined.⏎a. (of two or more propositions) unable to be true simultaneously.⏎b. (of two or more attributes of an object) unable to
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) to assume several births simultaneously.⏎The philosophy of pariśeṣanyāya or the doctrine of elimination of the alternatives accepts only the last alternative to be true. Among the accumulated karmas, one major karma along with several minor k
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
- L0:418 weakly suppresses
- L0:313 weakly suppresses "and" joining paired nouns
- L0:2528 weakly suppresses
- L0:6 weakly suppresses
- L0:2093 weakly suppresses
- L0:2699 weakly suppresses
- L0:1730 weakly excites
- L0:2035 weakly excites
- L0:942 weakly suppresses
- L0:282 weakly excites
- L0:673 weakly suppresses
- L0:485 weakly suppresses
- L0:1572 weakly suppresses
- L0:3 weakly excites
- L0:2808 weakly excites
- L0:988 weakly excites
- L0:1514 weakly suppresses
- L0:924 weakly suppresses
- L0:909 weakly suppresses
- L0:568 weakly suppresses
- L0:2886 weakly excites
- L0:624 weakly excites
- L0:1875 weakly suppresses
- L0:1581 weakly suppresses
Feeds later layers
- L7:1462 suppresses
- L26:1731 weakly suppresses
- L4:1934 weakly suppresses
- L21:1625 weakly suppresses
- L7:21 weakly suppresses
- L2:1778 weakly suppresses
- L14:121 weakly excites
- L21:208 weakly excites
- L21:2838 weakly suppresses
- L14:1309 weakly excites
- L15:1364 weakly excites
- L2:327 weakly excites
- L18:865 weakly excites
- L18:1379 weakly suppresses
- L20:2908 weakly excites
- L4:284 weakly suppresses
- L20:482 weakly suppresses
- L14:29 weakly excites
- L16:2143 weakly suppresses
- L22:1237 weakly suppresses
- L23:1139 weakly excites
- L17:248 weakly excites
- L5:1322 weakly excites
- L10:726 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
- over
- mişti
- ove
- strar
- submitted
- ARC
- jected
- RR
- written
- .ImageField
Suppresses
- annya
- ropriate
- onom
- 大哥
- doctype
- osterone
- 그것은
- rằng
- sophistic
- appropriate