© Neuronpedia 2026
    Privacy & TermsBlogGitHubSlackTwitterContact
    Neuronpedia logo - a computer chip with a rounded viewfinder border around it
    Neuronpedia
    Jacobian LensNEW
    Natural Language
    Autoencoders
    MAY 26
    Assistant AxisJAN 26Circuit TracerAPR 26SteerSAE EvalsExportsinterp-engineNEW Community BlogPrivacy & TermsContact
    1. Home
    2. Gemma-3-27B-IT
    3. 30-GEMMASCOPE-2-TRANSCODER-262K
    4. 54750
    Prev
    Next
    INDEX
    Explanations

    pre-* **Pre-Move Help:*** **Pre-Goldfish Life:*** **Post-Move Help:*** **Pre-election polls:*** **Pre-Linux Era:*** **Post-Strike Assessment:*** **Pre-Revolution Cuba:*** **Pre-Independence:*** **Pre-European Settlement:*** **Pre-date:*** **Pre-Offer/Due Diligence:**The common theme is context before or after certain events/periods. The `TOKENS_AFTER_MAX_ACTIVATING_TOKEN` combined with `MAX_ACTIVATING_TOKENS` strongly suggests phrases like "Pre-" followed by a noun or event.For example:- Move -> Post-Move Help- fish -> Pre-Goldfish Life- election -> post-election polls- Linux -> Pre-Linux Era- Strike -> Post-Strike Assessment- Revolution -> Pre-Revolution Cuba- Independence -> Pre-Independence- European -> Pre-European Settlement- date -> Pre-date- Offer -> Pre-OfferThe "pre-" prefix is very dominant, and "post-" also appears. The `TOP_POSITIVE_LOGITS` are diverse and relate to design, places, and languages, not immediately obvious to a specific pattern for this neuron's *behavior*. However, the question asks what the neuron *detects or predicts by finding patterns in lists*. The most striking pattern here is the occurrence of "pre-" or "post-" modifiers around event/noun tokens.Considering the structure "pre-X" or "post-X", the most direct representation of this pattern is simply "pre-".pre-

    np_acts-logits-general · gemini-2.5-flash-lite
    New Auto-Interp
    Top Features by Cosine Similarity
    Configuration
    google/gemma-scope-2-27b-it/transcoder_all/layer_30_width_262k_l0_small_affine
    Prompts (Dashboard)
    238,145 prompts, 512 tokens each
    Dataset (Dashboard)
    lmsys + oasst1
    No Configuration Found
    Embeds
    IFrame
    Link
    Negative Logits
     largely
    0.28
     effectively
    0.26
     marshmallows
    0.25
     one
    0.24
     distinctly
    0.24
     largement
    0.24
     nennt
    0.23
     satu
    0.23
    홧
    0.23
     thirds
    0.23
    POSITIVE LOGITS
    ोग्रा
    0.30
     diseñada
    0.30
     Thiết
    0.27
     וח
    0.27
     thiết
    0.27
     디자인
    0.27
     अम
    0.27
     Patrim
    0.27
    लता
    0.26
     realizada
    0.26
    Activations Density 0.004%

    No Known Activations