math and physics formulas
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. 15 of Qwen3-0.6B. Its strongest snippets are mixed: they peak on different tokens. 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 responds to maths and physics writing, especially the symbols inside equations such as sin, cos, omega, and variables like x or Vm. It fires most strongly on those symbols. When turned up the model wrote on maths-style problems, and when turned down it moved to other topics, but the link is loose.
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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x c x c h x L g x = = then ( ) lim x c f x exists and is equal to L. Three Special Limits 1. sin lim 1 x x x = 2. 1 cos lim x x
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as follows:⏎① The angular velocity of the gear blank is ω 1 and ω 1, and the angular velocity of the gear blank is ω 1;⏎② The blade moves with the revolution of (q)
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the energy effect is selective (varying through the spectrum). The distortions magnify the original factor 1 + d / to a factor that approaches 1 + 3d / . More precisely, the photographic magnitudes, to use the
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component of current ( I RMS cos φ) which is in phase with the voltage is called active component. The power consumed by this current VRMS IRMS cos φ . So that it is also known as ‘Wattful’ current.⏎(ii
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,⏎- times the derivative of sine of 5x with respect to 5x, that's just going to be cosine of 5x.⏎- If we want to write this in differential form, which is useful when
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40 N⏎What is the period of a point moving along a circular path of radius 5 cm at the linear speed of 200π cm / minutes?⏎A) 1<|endoftext|>Galactorrhea is a discharge
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: and the. for the, and. for the, and. for the, and. for the, and. for the, and. for the, and. for the, and. for the
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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instant and then it is averaged over a complete cycle.⏎The alternating voltage and alternating current in the series RLC circuit at an instant are given by⏎v=Vm sin ωt and i = I m sin( ω t + φ
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both sides. Part A: Short Answer (1 problems, 12 points). 1. Let S and T be linear spaces and A : S T be a linear map. Say
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structures of data that can aid learning.⏎- The $M$ features of user linguistic response can be saved within a user session as a time series of vectors $\sigma(m, n, t)$.⏎- Those series can be collected across $U
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006 at five locations were analysed, three of which had been affected by DFTD during this time interval. Significant increases in inbreeding coefficient (𝑓) and non-significant reductions in effective population size were observed for both diseased
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
- L14:2940 excites
- L9:280 suppresses
- L11:2127 suppresses
- L11:774 excites
- L12:1781 suppresses
- L12:1534 excites
- L8:2184 suppresses
- L11:1794 excites
- L13:1140 excites
- L8:925 weakly excites
- L14:2120 weakly suppresses
- L9:498 weakly excites
- L9:987 weakly suppresses
- L4:352 weakly excites
- L14:1972 weakly excites
- L4:249 weakly excites
- L14:1739 weakly excites
- L9:981 weakly excites
- L9:117 weakly excites
- L12:2490 weakly excites
- L13:1073 weakly excites
- L11:2532 weakly excites
- L14:2232 weakly suppresses
- L5:1117 weakly excites
Feeds later layers
- L16:825 suppresses
- L16:1280 suppresses
- L20:790 suppresses
- L16:807 suppresses
- L16:1331 suppresses
- L19:2038 suppresses
- L18:416 suppresses
- L21:470 suppresses
- L19:414 suppresses
- L17:688 suppresses
- L20:2657 suppresses
- L17:1547 suppresses
- L17:1007 suppresses
- L17:482 suppresses
- L21:362 weakly suppresses
- L18:1189 weakly suppresses
- L20:806 weakly suppresses
- L22:203 weakly suppresses
- L25:1230 weakly excites
- L18:1452 weakly excites
- L19:491 weakly suppresses
- L24:149 weakly suppresses
- L16:1328 weakly suppresses
- L19:1617 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
- 持有的
- 布莱
- 兵团
- eler
- يتها
- holding
- behind
- "](
- ...",
- olin
Suppresses
- depend
- ornado
- site
- filePath
- =User
- ượt
- себ
- DIAG
- dotenv
- stitutions