Neuron Explorer

Qwen3-0.6B

the word 'question' referring back

A neuron in layer 11 of Qwen3-0.6B. Its strongest snippets are very consistent: they mostly peak on the same token. It is featured, so it has steering outputs.

What this neuron does

The neuron fires on the word 'question' when the text is pointing back to a question someone already asked, as in 'answers your question' or 'the initial question'. When it was turned up, the model's output kept circling back to answers and 'the first question', so the link to questions shows up in the steering too.

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 activation was highest. Amber marks positive activation; the outlined token is the peak. Hover a token for its strength.

  1. #1 peak

    't squash (compress) it at all. At KS3 and GCSE you'll be taught that you cannot compress liquids, so the the short answer to your question is yes, liquids have a definite volume :0).⏎Interested in booking a

  2. #2 peak

    its climate and agriculture production. Before you go further, it is good for you to enrich your agriculture production countries here. Now, let’s get started answering your question about why Southeast Asia is the largest agricultural production region.⏎Behind the scene of becoming

  3. #3 peak

    become a habit.⏎Luke 6:31 (GNBDK)⏎Do for others just what you want them to do for you.⏎To answer the original question, its seems the NT favors the LXX.⏎Of the places where the New

  4. #4 peak

    volume of a lego brick⏎We will need to use the following two formulas:⏎You can now code this algorithm using Python to reveal the answer to the initial question: how many lego bricks are needed to build planet Earth!⏎Number of leg

  5. #5 peak

    it is sufficient.⏎Of course the best way to see where the Ganges river is by using a map of the River Ganges.⏎I hope this answers your question Rebecca. You can find out more information on the River Ganges by looking at

  6. #6 peak

    how can I determine whether two edges will be crossing? In graphs coordinates are not used, so there must be a different way than geometric to determine this.⏎For your information, the graph is implemented as a table:⏎bool adjacency_table [MAX_V

Steering

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.

turned down a lot
Prompt

Here is something I have been thinking about lately: I was reading, I was writing, and I was learning. How much I wanted to share and make something beautiful and helpful, I was writing and learning, I was sharing with a friend, I was teaching, etc. I was looking

Most negative on

Where the activation went furthest below zero. Blue marks negative activation. Many neurons mean something different in each direction.

  1. #1 peak

    0 00:10:22 CEST 2010⏎On 9/9/10 4:39 PM, Baba wrote:⏎> In below code "

  2. #2 peak

    jing Xiaozhuang University, Nanjing, Jiangsu 211171, China⏎*Authors to whom correspondence should be addressed.⏎Received: 2020-8-4 / Accepted: 202

  3. #3 peak

    ’s study, the measure of ACO was the child’s report of how open they felt the family atmosphere was in terms of discussing adoption with their Cited by: Low self-esteem Identity issues can explain some low self-esteem, a classic adoptee

  4. #4 peak

    across the country who are pushing for effective and equitable climate policies in their states. The views, thoughts, and opinions expressed in this article do not necessarily reflect those of Climate XChange.⏎Interested in submitting an article to be posted on our website?⏎

Connections

Show on map

Which neurons in earlier layers feed this one, and which later ones it feeds. Amber links excite, blue links suppress; longer bars are stronger.

Fed by earlier layers

  1. L9:1541 suppresses
  2. L8:355 excites
  3. L10:1225 excites
  4. L7:1044 excites
  5. L10:1749 suppresses
  6. L9:1743 excites
  7. L6:2879 excites
  8. L7:61 suppresses
  9. L8:778 suppresses
  10. L8:2240 excites
  11. L9:1101 excites
  12. L10:1363 suppresses
  13. L10:2230 suppresses
  14. L10:2219 excites
  15. L6:1095 excites
  16. L9:1146 weakly excites
  17. L8:643 weakly excites
  18. L7:746 weakly excites
  19. L10:721 weakly suppresses
  20. L10:572 weakly suppresses
  21. L8:147 weakly excites
  22. L1:2389 weakly excites
  23. L3:147 weakly excites
  24. L4:443 weakly suppresses

Feeds later layers

  1. L13:1887 excites
  2. L12:2588 excites
  3. L16:2257 excites
  4. L13:148 suppresses
  5. L14:2498 excites
  6. L13:516 suppresses
  7. L16:2806 excites
  8. L15:2020 excites
  9. L14:1469 suppresses
  10. L18:1342 excites
  11. L17:2635 excites
  12. L14:2563 suppresses
  13. L15:826 weakly excites
  14. L13:1501 weakly excites
  15. L15:822 weakly suppresses
  16. L13:1942 weakly suppresses
  17. L14:1263 weakly excites
  18. L12:252 weakly excites
  19. L15:2008 weakly suppresses
  20. L21:2211 weakly excites
  21. L13:1594 weakly excites
  22. L14:377 weakly suppresses
  23. L17:2445 weakly excites
  24. L19:2179 weakly excites

Activation histogram

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.

1 values between -0.93 and -0.874 values between -0.61 and -0.541 values between -0.54 and -0.482 values between -0.48 and -0.4115 values between -0.41 and -0.3545 values between -0.35 and -0.28118 values between -0.28 and -0.22278 values between -0.22 and -0.15521 values between -0.15 and -0.09732 values between -0.09 and -0.021052 values between -0.02 and 0.04584 values between 0.04 and 0.11317 values between 0.11 and 0.17126 values between 0.17 and 0.2457 values between 0.24 and 0.3032 values between 0.30 and 0.3710 values between 0.37 and 0.435 values between 0.43 and 0.502 values between 0.50 and 0.562 values between 0.56 and 0.632 values between 0.63 and 0.691 values between 1.08 and 1.141 values between 1.60 and 1.66offnegativepositiveoften

Logit lens

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 layers instead.

Promotes

  1. ptom
  2. ään
  3. answers
  4. 当之
  5. виз
  6. пунк
  7. pecific
  8. ENTION
  9. nage
  10. изатор

Suppresses

  1. udson
  2. 支架
  3. ティブ
  4. sometimes
  5. interrupted
  6. ++++++++
  7. yanında
  8. recent
  9. (open
  10. Foreign