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    2. Gemma-3-27B-IT
    3. 15-GEMMASCOPE-2-TRANSCODER-262K
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    * `h` often followed by numbers or `set`* `Wak` followed by `anda`* `hashes` followed by `,`* `:` followed by `The`* `h1` and `h3` appear in texts.The `TOP_POSITIVE_LOGITS` are a mix of foreign language words and some English-looking fragments (`bieter`). This indicates the neuron might be sensitive to non-English text or specific linguistic features across languages.Looking at `MAX_ACTIVATING_TOKENS` and `TOKENS_AFTER_MAX_ACTIVATING_TOKEN`:* `h` followed by `set`: suggesting command-like structures.The neuron is looking for specific sequences, often involving 'h' followed by numbers or commands like 'set', and also recognizing segments of proper nouns like 'Wakanda'. The top positive logits also include a diverse set of tokens from various languages, suggesting a broad, potentially multilingual, pattern detection capability, possibly related to code or structured text segments.Let's refine based on the most common patterns:`h` followed by `set` or numbers.`Wak` followed by `anda`.`h1`, `h3` in text.Non-English words.The neuron seems to be capturing specific structural elements or tokens, especially those leading to or forming specific words or commands. `h` followed by `set` or numbers, and segments of proper nouns like "Wakanda". The presence of diverse foreign language tokens in `TOP_POSITIVE_LOGITS` is also a significant clue.Focusing on the strongest signal: `h` followed by `set`, `h` followed by numbers. Also `Wak` followed by `anda`.And the broader context suggests it might be picking up on specific token sequences or language markers.Let's try to find a unifying theme. The `h` followed by `set` or numbers could be related to commands or identifiers. `Wakanda` is a proper noun. The foreign tokens are diverse.Could it be sequences related to specific commands/identifiers *and* foreign language words/phrases?Let's check the prompt again: "find patterns that helps you explain the behavior".The explanation should be "concise (3 to 20 words)".`h` `set``h` `0`, `h` `3`, `h` `1``Wak` `anda``hashes` `,``:` `The``h` followed by command/number.`Wak` part of name.Foreign words.Is there a connection between the structural parts and the foreign words? Not directly obvious.Let's prioritize the clearest structural patterns:`h` + `set``h` + number`Wak` + `anda``h` tags in HTML-like context.The `TOP_POSITIVE_LOGITS` are very diverse: `pessoas`, `ໃຊ`, `imi`, `bieter`, `הר`, `oneg`, `においても`, `രിച്ചി`, `ழிப்பு`, `犟`. These are clearly non-English. This hints at a neuron that might activate on non-English text or specific characters within them.However, `MAX_ACTIVATING_TOKENS` like `h` and `hashes`, and `TOKENS_AFTER_MAX_ACTIVATING_TOKEN` like `set` and numbers are very specific English/code-like patterns. `Wak` `anda` is also English/proper noun related.Let's re-evaluate if there is a conflict.If `TOP_POSITIVE_LOGITS` are prominent, they should be considered.What if the neuron detects specific sequences *within* certain languages, including English and non-English?Consider the prompt: "explain the behavior of a neuron in a single short phrase"."finding patterns in lists".The neuron is detecting specific prefix patterns (`h` + `set`/number, `Wak`+`anda`) AND potentially activating on non-English characters/words.Let's focus on the most concrete patterns from `MAX_ACTIVATING_TOKENS` and `TOKENS_AFTER_MAX_ACTIVATING_TOKEN`:`h` followed by `set``h` followed by digits (0, 1, 3)`Wak` followed by `anda``hashes` followed by `,`This suggests patterns like:`h` `set``h` `0``h1``h3``Wakanda` (from `Wak` `anda`)`hashes` `,`So, it's about specific sequences, often starting with 'h' or related to commands/names. The foreign

    np_acts-logits-general · gemini-2.5-flash-lite
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    google/gemma-scope-2-27b-it/transcoder_all/layer_15_width_262k_l0_small_affine
    Prompts (Dashboard)
    238,145 prompts, 512 tokens each
    Dataset (Dashboard)
    lmsys + oasst1
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    Negative Logits
    vas
    0.12
     aan
    0.12
    St
    0.11
    All
    0.11
     A
    0.11
     de
    0.11
     E
    0.11
    -(
    0.11
    per
    0.11
    st
    0.10
    POSITIVE LOGITS
    pessoas
    0.12
    ໃຊ
    0.12
    ими
    0.11
    bieter
    0.11
    הר
    0.10
    oneg
    0.10
    においても
    0.10
    രിച്ചി
    0.10
    ழிப்பு
    0.10
    犟
    0.10
    Activations Density 0.010%

    No Known Activations