Neuron Explorer

Qwen3-0.6B

"be" after modal verbs

A neuron in layer 23 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

This neuron lights up on the word "be" when it follows a helper word like "may", "will" or "would". Turning it down made the model repeat "not only not", so it seems tied to the negation words it can hold back.

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

  1. #1 peak

    main point; and evidence substantiation.⏎You will need to understand the sentence’s intention and keep an eye out for brevity and succinctness.⏎You will be quizzed on all the usual suspects: commas, semicolons and col

  2. #2 peak

    your children appropriate bathroom habits or want to know whether he or she may be hitting puberty too early? If you are a parent with an uncomfortable question, you might be worried about broaching sensitive topics for fear of being judged or embarrassed. Don’t

  3. #3 peak

    adult teeth and can prevent complications and the need for more orthodontic therapy. Removable computer-generated appliances can treat selective cases in which orthodontics would otherwise be needed, but these appliances also may cost more. Removable appliances that use wires

  4. #4 peak

    including any advertising you do for your business.⏎You may be proud to tell anyone who asks that you’ve processed $50,00

  5. #5 peak

    devalue the perspectives, opinions and contributions of other team members. However, while individuals with a high subjective sense of power tend to dominate team discussions, they may be able to do so only if other team members permit it. Most likely, team

  6. #6 peak

    inske2 IMAGE OF SCIENTIST)⏎Day2PMSchinske2 IMAGE OF SCIENTIST⏎In addressing this, however, faculty members may be reluctant to engage in frank discussions about race, gender, and identity with their students

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: not only not not only not only not not not not not not not not not not not not not not not not not not not not not not not not not not not not not not not not not not not not not not not not not

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

    times in our history where the U.S. was just not the country it was supposed to be.⏎In that sense, the story of how our nation has come to be is a story of cultural achievement, and we owe that to the people who

  2. #2 peak

    result, however, is<|endoftext|>The Getty Museum challenged people worldwide to recreate famous works of art using items from home.⏎Once again, Leah made my dream come to my life. Her chameleonic beauty makes it so easy to transform in

  3. #3 peak

    then update the positions and velocities for the next round of calculations. This scheme of computation is known as the n-body method. Perhaps Newton himself could have put it to work if

  4. #4 peak

    that has been hiding at the bottom of a lake ever since it rode to earth in a meteorite, validating the Panspermia theory of how life came to this planet?⏎Or does it lend credence to the idea that life could spring

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. L17:2050 excites the word 'be' after modals
  2. L19:1001 excites "be" after will or you'll
  3. L16:90 suppresses
  4. L20:111 suppresses
  5. L0:110 excites
  6. L16:61 excites
  7. L3:459 suppresses
  8. L16:15 excites
  9. L20:2633 suppresses
  10. L18:1048 suppresses
  11. L17:241 weakly excites
  12. L20:2437 weakly excites
  13. L16:97 weakly excites
  14. L22:607 weakly excites
  15. L16:160 weakly excites
  16. L7:2851 weakly excites
  17. L19:2033 weakly excites
  18. L1:2565 weakly excites
  19. L19:262 weakly excites
  20. L4:336 weakly excites
  21. L20:1081 weakly excites
  22. L1:1210 weakly excites
  23. L18:102 weakly suppresses
  24. L16:67 weakly suppresses

Feeds later layers

  1. L27:714 strongly suppresses
  2. L26:1233 strongly suppresses
  3. L27:644 suppresses
  4. L25:802 suppresses
  5. L24:1811 suppresses
  6. L26:1452 suppresses
  7. L24:298 suppresses 'would' in hypothetical or past statements
  8. L27:796 suppresses
  9. L27:1006 weakly suppresses
  10. L24:334 weakly suppresses
  11. L25:472 weakly excites
  12. L27:1163 weakly suppresses
  13. L27:655 weakly suppresses
  14. L24:1080 weakly excites
  15. L24:277 weakly excites
  16. L25:1672 weakly suppresses
  17. L24:1343 weakly suppresses
  18. L27:932 weakly suppresses
  19. L27:1620 weakly suppresses
  20. L24:864 weakly suppresses 'to' before an infinitive
  21. L25:2151 weakly suppresses
  22. L24:787 weakly suppresses
  23. L24:2093 weakly excites
  24. L24:1696 weakly suppresses

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.

4 values between -10.82 and -8.964 values between -8.96 and -7.093 values between -7.09 and -5.2316 values between -5.23 and -3.37107 values between -3.37 and -1.503176 values between -1.50 and 0.36391 values between 0.36 and 2.2292 values between 2.22 and 4.0939 values between 4.09 and 5.9522 values between 5.95 and 7.8112 values between 7.81 and 9.688 values between 9.68 and 11.544 values between 11.54 and 13.412 values between 13.41 and 15.272 values between 15.27 and 17.132 values between 17.13 and 19.003 values between 19.00 and 20.862 values between 20.86 and 22.722 values between 24.59 and 26.451 values between 26.45 and 28.311 values between 28.31 and 30.181 values between 30.18 and 32.041 values between 33.90 and 35.771 values between 35.77 and 37.632 values between 37.63 and 39.491 values between 41.36 and 43.221 values between 43.22 and 45.081 values between 45.08 and 46.951 values between 46.95 and 48.812 values between 48.81 and 50.672 values between 52.54 and 54.401 values between 56.27 and 58.131 values between 61.86 and 63.72offnegativepositiveoften

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. 求め
  2. 持ち
  3. erg
  4. embourg
  5. anger
  6. atters
  7. 作り
  8. 考え
  9. ULA
  10. runApp

Suppresses

  1. not
  2. not
  3. →not
  4. NOT
  5. _not
  6. Not
  7. Not
  8. .not
  9. (not
  10. NOT