'to' in 'to do with'
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. 23 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 'to' in phrases like 'has to do with' or 'nothing to do with'. Turning it down made the model output only the word 'to' over and over.
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.
-
<|endoftext|>Forget the batteries for a second, thats just one of a thousand analogies you could use to describe voltage/current and the reason that no current flows has nothing to do with the electro-chemical properties of batteries, its far simpler.⏎The easiest
-
build a modest addition to its building for Sunday school classes. Despite the church demonstrating that the addition is critical to<|endoftext|>STEM and Wall Street⏎What does STEM have to do with Wall Street?⏎When you think about CAREERS on Wall Street, what
-
by a single or multiple earthworks of massive proportions. Hillforts must have formed symbols of power within the landscape, while their function may have had as much to do with ostentation and display as defence. Pentre Camp comprises a sub
-
carbon dioxide does. Methane gas (CH4) traps about 34 times more heat within the earth’s atmosphere.⏎So what does worm manure have to do with dog waste?⏎Well, dog waste is not just full of bacteria that
-
using the disciplinary methods passed down by his father.⏎“I have always believed that the way my parents disciplined me has a great deal to do with the success I have enjoyed as a man,” Peterson said in a statement
-
ictions like gambling, that have nothing to do with food. Tolerance is both a result and a cause of addictive behavior
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: to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to to
Most negative on
Where the activation went furthest below zero. Blue marks negative activation. Many neurons mean something different in each direction.
-
workable solution for polygonal transects remains elusive.⏎Keywords: Boundary overlap, Inclusion region, Reflection method, Transect configuration⏎- We recommend that you also print this page and attach it to the printout of the article
-
, they will absorb more nutrients and end up a similar size as they would in their natural habitat.⏎But, of course, the idea of a bonsai tree is to produce a plant that looks
-
has unique experiences with a unique home environment. Her current attitude is a by-product of her life experiences and the people around her. My advice to you is stop worrying about whether your teen’s attitude measures up to other teens’ attitude but
-
invented thousands of years ago but stairs are a 13th century invention. Claude Stairs was working on the court of Count Of Flanders and his idea was laughed at when he first introduced it in 1208.⏎The idea
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
- L22:589 excites
- L22:192 weakly suppresses
- L21:2453 weakly suppresses
- L6:2287 weakly suppresses
- L19:2347 weakly suppresses
- L17:2215 weakly suppresses
- L20:2262 weakly excites
- L19:2664 weakly excites
- L1:1487 weakly suppresses
- L2:1482 weakly suppresses
- L19:1613 weakly suppresses "is" after a sentence-opening "It"
- L19:2472 weakly excites
- L19:693 weakly suppresses
- L10:2551 weakly suppresses
- L5:2332 weakly excites
- L18:1768 weakly excites
- L18:621 weakly suppresses
- L3:2844 weakly suppresses
- L11:565 weakly excites
- L20:1636 weakly suppresses
- L20:2794 weakly excites
- L19:1626 weakly suppresses
- L6:2145 weakly excites
- L12:539 weakly excites
Feeds later layers
- L26:408 strongly suppresses
- L27:456 strongly suppresses
- L24:959 suppresses
- L25:70 excites
- L24:381 suppresses
- L27:158 excites
- L24:396 excites
- L27:2194 suppresses the letter t starting a word fragment
- L25:1240 weakly suppresses
- L24:2825 weakly excites
- L25:1719 weakly excites
- L27:1461 weakly suppresses
- L26:148 weakly excites
- L24:2797 weakly suppresses single letters starting names
- L25:361 weakly suppresses
- L24:2304 weakly excites
- L24:1784 weakly suppresses
- L27:1037 weakly suppresses
- L24:445 weakly excites
- L26:1610 weakly suppresses
- L27:1809 weakly excites
- L25:226 weakly excites
- L27:1008 weakly excites
- L24:2737 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
- cade
- 接入
- úc
- ://%
- 坊
- pector
- 路
- 好处
- setC
- 航道
Suppresses
- to
- to
- To
- →to
- To
- .to
- _to
- -to
- 到
- ToDevice