unclear: word before the key term in problems
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 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 is hard to pin down. It tends to light up on a small word just before the main thing a math or physics question is asking about, such as 'its' before 'velocity' or 'the' before 'common ratio'. Turning it up made the model drift into random topics and unrelated lists.
Written by Claude from this neuron's activations and steering outputs. Confidence: low. 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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of Calculations with Gravity⏎Example 1: A ball is thrown up into the air at an initial velocity of 56.3m/s. Determine its velocity after 4.52s have
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+ 2)^2 in such a way that its x-coordinate is increasing at a constant rate of 2 units per second. How fast is the particle's distance to the point (-2, 0) changing at the moment that the particle
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ize independent and dependent clauses.⏎The definition and how to fix run-on sentences.⏎Understanding the concept of Law of Independent Assortment.⏎How to find the probability of independent events.⏎The definition of a colon and how to use it effectively in a
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Maths as we explored the link between speed, distance and time in the playground.⏎After learning about the importance of sleep, we used spreads
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ten. The number 58⏎rounded to the nearest ten would be 60.⏎Look at the number on⏎each shell. Click on the nearest ten. Collect a sand dollar
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D) not equal in magnitude but have opposite direction⏎E) None of the above⏎In a uniform circular motion, which of the following is constant?⏎A) centripetal acceleration⏎D) centripetal force⏎F is the cent
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: that the most important things to make sure to have in the most important things that make up the things that make up the things that make up the things that make up the things that make up the things that make up the things that make up
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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s. The sphere is at distance R from the center. What is the centripetal acceleration of the same sphere if both distance R and speed s are doubled?⏎B) a / 4⏎C) a / 2⏎D)
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⏎From a metal parts-making perspective, melting is virtually exclusive to casting*. Why the asterisk
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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 of milk-like substance
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on a car of mass 2000 Kg turning around a circular bend of radius 50 meters at the constant speed of 36 km/hr?⏎A) 4,000 N⏎B) 400
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
- L0:517 suppresses
- L5:78 excites
- L18:1287 weakly excites
- L2:2277 weakly excites
- L9:156 weakly excites
- L13:2061 weakly suppresses
- L6:2562 weakly excites
- L12:507 weakly excites
- L7:368 weakly suppresses
- L14:2337 weakly suppresses
- L17:713 weakly excites
- L3:153 weakly suppresses
- L6:2489 weakly suppresses
- L6:2201 weakly suppresses
- L8:1624 weakly suppresses
- L19:746 weakly excites
- L0:38 weakly suppresses
- L0:1814 weakly suppresses
- L18:2054 weakly suppresses
- L4:1356 weakly suppresses
- L16:1279 weakly suppresses
- L11:1439 weakly excites
- L8:1231 weakly excites
- L9:1625 weakly suppresses
Feeds later layers
- L22:2981 weakly excites
- L22:1545 weakly suppresses
- L25:2438 weakly excites
- L27:173 weakly suppresses
- L21:635 weakly suppresses
- L24:2807 weakly suppresses
- L24:570 weakly suppresses
- L22:2780 weakly suppresses
- L24:1886 weakly suppresses
- L23:2497 weakly suppresses
- L22:1277 weakly suppresses
- L24:2124 weakly excites
- L21:2817 weakly suppresses
- L24:1903 weakly excites
- L21:2445 weakly suppresses
- L25:879 weakly excites
- L24:2935 weakly suppresses
- L22:703 weakly suppresses
- L25:2448 weakly excites
- L26:3024 weakly excites
- L25:977 weakly excites
- L27:2518 weakly excites the letter f starting a word
- L23:672 weakly excites
- L24:3044 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
- ange
- illy
- eer
- 蝠
- ycles
- 鹄
- angles
- vé
- chant
- 列
Suppresses
- 焉
- cision
- atica
- 教育资源
- 交接
- $options
- ток
- _alloc
- 生产车间
- 管理和