© Neuronpedia 2026
    Privacy & TermsBlogGitHubSlackTwitterContact
    Neuronpedia logo - a computer chip with a rounded viewfinder border around it

    Neuronpedia

    Jacobian LensNEW
    Natural Language
    Autoencoders
    NEW
    Assistant AxisNEWCircuit TracerUPDATESteerSAE EvalsExportsinterp-engineNEWAPI Community BlogPrivacy & TermsContact
    1. Home
    2. Gemma-3-27B-IT
    3. 37-GEMMASCOPE-2-TRANSCODER-262K
    4. 200483
    Prev
    Next
    INDEX
    Explanations

    The neuron appears to be activated by terms related to "GeeksforGeeks" and "GeekSquad". The pattern seems to be related to common phrases or entities that start with "Geek" and are followed by specific subsequent terms like "forge" or "Squad".Let's refine this.MAX_ACTIVATING_TOKENS: `eks`, `ek`, `eks`, `ek`, `ek`, `Geek`, `Geek`, `ek`, `eks`, `Geek`TOKENS_AFTER_MAX_ACTIVATING_TOKEN: `forge`, `hack`, `for`, `.`, `.`, `Squad`, `Squad`, `bench`, `for`, `Squad`The combinations are:- `ek` + `forge` -> geeksforgeeks- `ek` + `hack` -> geckhack (less common but possible)- `ek` + `for` -> geeksfor- `Geek` + `Squad` -> GeekSquad- `Geek` + `bench` -> GeekbenchTOP_POSITIVE_LOGITS: `Programming`, `Computing`, `चलर्स` (could relate to coding), `Wikidata` (could relate to information portals).The TOP_ACTIVATING_TEXTS contain:- "geeksforgeeks.org"- "Geekhack Group Buys"- "GeeksforGeeks"- "How-To Geek"- "Geek Squad"- "Geekbench"The pattern is very strong. It's centered around "Geek" followed by a specific marker word or sequence.Possible explanations:- Geekforgeeks, Geekbench, Geeksquad- Geek followed by specific terms- Terms like "GeeksforGeeks" and "GeekSquad"- Entities starting with "Geek"Let's try to be more abstract but specific. The neuron seems to recognize entities that are formed by the word "Geek" followed by specific continuations."Geek" + "for" or "Squad" or "bench"This is too literal with the tokens.The neuron detects phrases like "GeeksforGeeks", "Geekbench", "GeekSquad".This fits the count and specificity.Let's check the length: 7 words. This is within the 3-20 word limit.It does not start with forbidden phrases.It is specific.Consider the commonality:- GeeksforGeeks (articles, programming)- GeekSquad (tech support)- Geekbench (benchmarking)- Geekhack (community forums, keyboards)The neuron is detecting these specific compound nouns or entities.Let's try to phrase it as a discovery of a particular *type* of compound word or entity."Entities formed by 'Geek' followed by specific words" - This is too descriptive of the process."Geek-related entities and resources" - This is too broad.Let's look at the example provided: "GeeksforGeeks", "Geekbench", "GeekSquad".These are distinct named entities or common service names.Maybe focus on the structure "Geek" + specific word."Geek followed by forge, hack, bench, squad" - This is listing tokens, not a pattern description.The core pattern is `Geek` + `[specific continuation]`The continuations are: `forge`, `hack`, `for`, `Squad`, `bench`.The entities are: GeeksforGeeks, Geekhack, Geekbench, GeekSquad.Let's try: "Geek followed by specific terms like forge or squad" - This reveals the pattern but is a bit long.How about summarizing the *types* of entities?"Programming sites

    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_37_width_262k_l0_small_affine
    Prompts (Dashboard)
    238,145 prompts, 512 tokens each
    Dataset (Dashboard)
    lmsys + oasst1
    No Configuration Found
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
     satellites
    0.42
     vert
    0.40
     cláss
    0.39
     ত্যাগ
    0.38
     cel
    0.37
     matters
    0.37
     ნ
    0.37
     incessant
    0.37
    ELS
    0.37
     TU
    0.36
    POSITIVE LOGITS
    ColorEffects
    0.39
    Programming
    0.37
     plufieurs
    0.37
    힙
    0.36
    Computing
    0.35
    गू
    0.35
    चलर्स
    0.34
     Wikidata
    0.34
    变化的
    0.34
    athar
    0.34
    Activations Density 0.000%

    No Known Activations