'even' and 'no' in negated phrases
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. 14 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
It fires on words that qualify a negative or limit, such as 'even' after 'not', 'no extra cost' or 'at best'. Its strongest negative reactions come on phrases about things failing to compare or matching what is possible. Turning it up or down made the model's writing wander in no clear direction.
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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of our own homes, making it particularly convenient in our busy day-to-day lives.⏎This post may contain affiliate links. I will earn a small commission, at no extra cost to you, if you make a purchase through an affiliate link.⏎The
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United Farm Workers⏎1 photograph : b&w negative ; 5.5 x 5.5 cm⏎Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission⏎of the copyright owners. Works not in
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. You can communicate a thought, but your thought may not be understood. In some cases, your thought may not even reach the proper target. Thats why it pays to ask questions to make certain
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names found in the yellowpages, like the Trigonopterus moreaorum, which is based on the family name "Morea." The families might not even guess the honor they
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a difficult time for most students and weight-body image concerns make this time even more challenging," said Albion counselor Cathy Nelson in an email. "This program
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out the fully explained answer.⏎*This post may have affiliate links, which means I may receive commissions if you choose to purchase through links I provide (at no extra cost to you). As an Amazon Associate I earn from qualifying purchases. Please read
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: how can we make the concept of "time" in the face of all this? What does "time" mean to us? In the context of all the language, what does the word "time" hold? What does the word "
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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! We can eat the most amazing diet (which is, of course, extremely important), but if our nervous system is out of balance, even the best diet won’t keep us in a healthy state.⏎Our autonomic nervous system plays a role
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But why? What was happening there? Then I saw pictures of other friends enjoying fancy milkshakes at a trendy restaurant. Suddenly, my simple domestic pleasures paled in comparison with the things I could be doing."⏎Grohol said that fear is
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spectacular fall colors of New England and Upstate New York. I still can’t get used to seeing Christmas lights on palm trees and cacti. Nothing can compare to the beauty of the first, pure white snow dusting lush pines
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virtue. No amount of scolding, criticism, and punishment can turn a regrettable situation around. When you are not chided and you deserved
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
- L12:2868 suppresses
- L11:1149 weakly excites
- L11:1472 weakly suppresses
- L11:2641 weakly excites
- L12:2340 weakly suppresses
- L7:2509 weakly suppresses
- L11:448 weakly suppresses
- L5:1145 weakly suppresses
- L11:1918 weakly excites
- L11:2344 weakly excites
- L6:2129 weakly suppresses
- L11:2527 weakly suppresses
- L12:420 weakly suppresses
- L11:2490 weakly excites
- L6:2821 weakly suppresses
- L11:970 weakly suppresses
- L8:1325 weakly excites
- L11:126 weakly suppresses
- L2:2469 weakly suppresses
- L8:2854 weakly suppresses
- L13:2770 weakly suppresses
- L13:2907 weakly excites
- L9:985 weakly excites
- L7:693 weakly excites
Feeds later layers
- L17:2999 suppresses
- L16:950 weakly suppresses
- L16:1282 weakly suppresses
- L16:1752 weakly suppresses
- L26:890 weakly excites
- L16:2214 weakly suppresses
- L19:1688 weakly suppresses
- L24:2977 weakly suppresses
- L15:1713 weakly excites math word problem wording
- L23:499 weakly suppresses
- L16:1857 weakly excites
- L23:221 weakly excites
- L27:919 weakly excites
- L17:2856 weakly excites
- L23:2485 weakly excites
- L18:2672 weakly excites
- L15:1360 weakly suppresses
- L16:2465 weakly suppresses
- L16:804 weakly suppresses
- L17:2351 weakly suppresses
- L18:1190 weakly excites
- L27:2496 weakly excites
- L18:2400 weakly suppresses
- L17:2709 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
- venes
- LLU
- Fif
- מנה
- dek
- ELSE
- davon
- соответ
- deo
- screen
Suppresses
- whatsoever
- 一切
- nothing
- ability
- 万事
- _handles
- horizon
- isses
- ifiers
- arbitrarily