Neuron Explorer

Qwen3-0.6B

'who' after a name and title

A neuron in layer 16 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 fires on the word "who" when it comes right after a person's name and job title, as in "said psychologist Jack van Honk, who led the study". It is about linking a named expert to a description of what they did. Turning it up did not change the writing in a clear way.

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

    ins.⏎The new findings add to the scientific heft underlying acupuncture, said neuroscientist Maiken Nedergaard, M.D., D.M.Sc., who led the research. Her team is presenting the work this week at a scientific meeting

  2. #2 peak

    what interesting and unique birds call the site home or have stopped by on their way south.⏎According to LyondellBasell Senior Vice President Dale Friedrichs, who has participated in most of the Matagorda counts, there are three elements of

  3. #3 peak

    an environmental influence that has magnified effects on some minority children at lower-income urban schools,” said Dr. Brennan Davis, assistant professor of marketing at Baylor University, who co-authored the study with Dr. Sonya Grier, associate professor of marketing

  4. #4 peak

    bracelet and brooch fragments and other objects that would have been highly prized objects.⏎Dr Gordon Noble, Senior Lecturer, Department of Archaeology, University of Aberdeen who led the

  5. #5 peak

    confidence in others.⏎“Testosterone reduces trust just enough to make people vigilant and careful,” said psychologist Jack van Honk of Utrecht University in the Netherlands, who led the study published May 24 in* Proceedings of the National Academy of

  6. #6 peak

    masses billions of times the mass of the sun could exist so early in the history of the universe,” astronomer Julie Comerford of University of California Berkeley, who was not involved in the study, wrote in an e-mail to Wired.com.

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 think I have been thinking about the idea of thinking of the idea of thinking of thinking of thinking of thinking of thinking of thinking of thinking of thinking of thinking of thinking of thinking of thinking of thinking of thinking of thinking of thinking of thinking

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

    data, we think this problem is relevant for Professor Finnegan's class at WSU.⏎What textbook is this problem found in?⏎Our data indicates that this problem or a close variation was asked in Chemistry: An Atoms First Approach - Zumd

  2. #2 peak

    problem of the brash young Texan and referred him to a couple of recent Caltech graduates, Howard Lewis and Bruce Burns.⏎Howard contacted the two engineers, and they were

  3. #3 peak

    manager from this point to ensure the employee’s contributions are valued and that their needs in knowing how to do their job are<|endoftext|>No person shall hold any civil office unless that person be a qualified elector for such office. Article IX, Section

  4. #4 peak

    5 mg/kg, 5 times daily for 5 days.⏎Best of luck---⏎Search for questions⏎Still looking for answers? Try searching for what you seek or ask your own question.⏎Posted 31 Aug 2009 •

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. L15:2994 excites
  2. L12:855 suppresses
  3. L15:2800 weakly suppresses
  4. L15:1226 weakly suppresses
  5. L15:3069 weakly excites
  6. L12:3030 weakly suppresses
  7. L13:1666 weakly suppresses
  8. L14:1765 weakly suppresses
  9. L1:1763 weakly suppresses
  10. L13:1563 weakly excites
  11. L14:2017 weakly suppresses
  12. L15:1094 weakly suppresses
  13. L15:2287 weakly excites
  14. L15:1818 weakly excites "own" in reflexive phrases
  15. L15:2454 weakly suppresses
  16. L14:1052 weakly excites
  17. L7:2465 weakly suppresses
  18. L15:2890 weakly excites
  19. L12:1150 weakly suppresses
  20. L14:2015 weakly suppresses
  21. L14:325 weakly excites
  22. L15:696 weakly suppresses
  23. L15:2115 weakly excites
  24. L12:2362 weakly excites

Feeds later layers

  1. L18:2113 suppresses
  2. L17:1961 suppresses
  3. L18:941 excites
  4. L18:2475 suppresses
  5. L20:516 suppresses
  6. L17:1679 suppresses
  7. L18:1914 suppresses
  8. L22:106 excites
  9. L17:602 excites
  10. L17:2148 weakly suppresses
  11. L17:2467 weakly suppresses
  12. L17:1075 weakly excites
  13. L21:300 weakly excites
  14. L19:1592 weakly excites
  15. L18:1629 weakly suppresses
  16. L19:1497 weakly suppresses
  17. L17:783 weakly suppresses
  18. L19:1022 weakly suppresses
  19. L18:358 weakly excites
  20. L20:2178 weakly suppresses
  21. L17:1234 weakly excites
  22. L19:1124 weakly excites
  23. L19:330 weakly suppresses
  24. L21:123 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.

2 values between -2.98 and -2.725 values between -2.45 and -2.186 values between -2.18 and -1.924 values between -1.92 and -1.659 values between -1.65 and -1.3827 values between -1.38 and -1.1122 values between -1.11 and -0.8541 values between -0.85 and -0.58127 values between -0.58 and -0.311020 values between -0.31 and -0.052203 values between -0.05 and 0.22308 values between 0.22 and 0.4969 values between 0.49 and 0.7625 values between 0.76 and 1.0218 values between 1.02 and 1.2910 values between 1.29 and 1.564 values between 1.56 and 1.822 values between 1.82 and 2.091 values between 2.09 and 2.364 values between 2.36 and 2.621 values between 7.43 and 7.70offnegativepositiveoften

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. grave
  2. se
  3. _sound
  4. enger
  5. เด
  6. Par
  7. opper
  8. par
  9. orative
  10. hurst

Suppresses

  1. 其所
  2. Its
  3. 它的
  4. idUser
  5. THAN
  6. 它是
  7. its
  8. ationally
  9. 旗下
  10. 其