user query topics or keywords.

mentions of personal names or name placeholders (e.g., "Name", "Your Name", salutations/signatures) in emails and templates.

tokens that indicate the speaker’s identity as a large language model (words like "large", "language", "model" and related self‑identifying phrases/questions).

the presence of person names (named-entity tokens identifying people).

The neuron strongly detects named entities—proper nouns like people, organizations, places, and other capitalized names.

It detects document titles/headings and prominent proper-noun phrases (section or project names).

This neuron detects personal names—capitalized named entities referring to people.

the neuron detects proper names (personal and other capitalized named entities) in text.

The neuron detects technical or domain-specific terms and acronyms/proper nouns in the assistant's responses.

Tokens that mark dates, numbers, or named news/events (e.g., event titles, competitions, conferences, and other time- or event-related proper nouns).

tokens related to CSS layout properties and values (especially CSS Grid properties like grid, grid-template-columns, gap, margin, width).

the neuron detects brand/product/software names and other proper nouns (named entities) often appearing in recommendation or commerce contexts.

mentions of AI/LLM model identity and ecosystem details—covering capabilities, training/data specs, APIs, affiliations, and release timelines—especially in the context of Google’s offerings.

the phrase "Key improvements" in code explanations or documentation.

This neuron detects the summary header “Key improvements and explanations.”

the word "soldiers" or references to military personnel/combatants.

This neuron is looking for numeric information (tokens representing numbers, measurements, or counts).

the neuron detects words and tokens related to military conflict and battle (e.g., battle types, troops, campaigns, and combat actions).

tokens that appear in error messages, logs, or I/O/diagnostic output (e.g., "Error", response/status/read operations).

It fires on prominent topical/content words and key informative nouns (tokens that mark main ideas or important concepts in a passage).