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    thoughtsThe user wants me to explain the behavior of a neuron based on four lists: `MAX_ACTIVATING_TOKENS`, `TOKENS_AFTER_MAX_ACTIVATING_TOKEN`, `TOP_POSITIVE_LOGITS`, and `TOP_ACTIVATING_TEXTS`.The explanation must be concise (3-20 words) and capture a pattern.Let's analyze each list:1. **MAX_ACTIVATING_TOKENS**: `story`, `0`, `Amazon`, `0`, `wasn`, `iver`, `of`, `story`, `of`, `of` * Keywords: `story`, `Amazon`, `of`.2. **TOKENS_AFTER_MAX_ACTIVATING_TOKEN**: `in`, `s`, `(`, `s`, `!`, `your`, `about`, `You`, `you` * Keywords after tokens: `in`, `s`, `!`, `your`, `you`. * Notice `story` is followed by `in` (e.g., "story in"). * Notice `of` (appears multiple times) can be followed by `your` or `you` in the context of the texts.3. **TOP_POSITIVE_LOGITS**: `wavelength`, `applicator`, `juiste`, `odpowiedzi`, `znacz`, `cardNumber`, `െടുത്ത`, `]),`, `স্থান`, `"।` * This list contains several foreign language words (Polish, Hindi) and technical terms. It doesn't immediately form a clear pattern with the other lists. This might indicate a broader, less specific function or a complex combination.4. **TOP_ACTIVATING_TEXTS**: * "Write the summary of a short chillig **story** in a victorian setting." (MAX_ACTIVATING_TOKEN: `story`, TOKENS_AFTER_MAX_ACTIVATING_TOKEN: `in`) * "## The Pallid Gardener - **Story** Summary" (MAX_ACTIVATING_TOKEN: `story`) * "**Amazon** (AMZN): This is the biggest player by far due to **Ring**." (MAX_ACTIVATING_TOKEN: `Amazon`) * "Hope that gave **you** a little shiver!" (MAX_ACTIVATING_TOKEN likely `story` or related from context, TOKENS_AFTER_MAX_ACTIVATING_TOKEN: `you`) * "Thinking of **your** leadership" (Likely related to a "story" context, TOKENS_AFTER_MAX_ACTIVATING_TOKEN: `your`) * "Write a short sci-fi **story** about a robot learning to feel" (MAX_ACTIVATING_TOKEN: `story`, TOKENS_AFTER_MAX_ACTIVATING_TOKEN: `about`) * "Thinking of **You** & [Community Name]" (Could follow a context like "let's tell a story about..." or just general text. TOKENS_AFTER_MAX_ACTIVATING_TOKEN: `You`) * "Thinking of **you** & Rockside!" (Similar to above. TOKENS_AFTER_MAX_ACTIVATING_TOKEN: `you`)Let's try to find a unifying pattern:- `story` is a strong signal.- `Amazon` is present but seems secondary or an example.- The presence of `your`, `You`, `you` in `TOKENS_AFTER_MAX_ACTIVATING_TOKEN` alongside the `TOP_ACTIVATING_TEXTS` which include phrases like "Thinking of your leadership", "Thinking of You &...", "Thinking of you &..." suggests a personal/direct address component, often following a narrative or informational context.The neuron seems to relate to stories, and perhaps personal addresses or reflections within or after those stories. The `TOP_POSITIVE_LOGITS` are very diverse, so they are unlikely to be the primary driver of the pattern for a concise explanation."story" and "you" together.What follows "story"? `in`, `about`. What follows `of` (which is frequent)? Seems potentially related to a phrase like "story of your life" or "story about you".The phrases like "Thinking of you" or "Thinking of your leadership" are more about reflection or personal connection.Consider the `MAX_ACTIVATING_TOKENS` and `TOKENS_AFTER_MAX_ACTIVATING_TOKEN`:`story` -> `in`, `about``of` -> `your`, `you` (from context)It's picking up on narrative context (`story`) and personal address/reflection (`you`/`your`).Let's look at `TOP_ACTIVATING_TEXTS` again:"Write the summary of a short chillig **story** in a victorian setting.""The Pallid Gardener - **Story** Summary""Amazon (AMZN): This is the biggest player by far due to **Ring**.""Hope that gave **you** a little shiver!""Thinking of **your** leadership""Write a short sci-fi **story** about a robot learning to feel""Thinking of **You** & [Community Name]""Thinking of **you** & Rockside!"The neuron seems to activate for:1. Mentions of `story` or narrative summaries.2. Phrases involving personal address or reflection (`you`, `your`).Can we combine these? "stories about you" or "personal stories".The `TOKENS_AFTER_MAX_ACTIVATING_

    np_acts-logits-general · gemini-2.5-flash-lite
    New Auto-Interp
    Top Features by Cosine Similarity
    Configuration
    google/gemma-scope-2-27b-it/resid_post/layer_16_width_262k_l0_medium
    Prompts (Dashboard)
    238,145 prompts, 512 tokens each
    Dataset (Dashboard)
    lmsys + oasst1
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    IFrame
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    Negative Logits
    ين
    0.48
    મ
    0.48
     सीनियर
    0.47
     Qui
    0.46
     વિધાનસભા
    0.46
    蒯
    0.46
    quin
    0.45
    Methyl
    0.45
    мын
    0.45
    त्
    0.45
    POSITIVE LOGITS
     wavelength
    0.48
     applicator
    0.48
     juiste
    0.47
     odpowiedzi
    0.46
     znacz
    0.45
     cardNumber
    0.45
    െടുത്ത
    0.44
     ]),
    0.44
     স্থান
    0.43
    "।
    0.43
    Activations Density 0.000%

    No Known Activations