'Latin' in word origin notes
A neuronOne of thousands of tiny dials inside each layer. It reads the text so far and produces a number, its activation, that nudges what the model writes next. in layerOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. 10 of Qwen3-0.6B. Its strongest snippets are very consistent: they mostly peak on the same token. It is featuredA neuron picked for the full treatment: steering outputs and a written explanation. About 3,000 of the 86,000 neurons are featured., so it has steering outputs.
What this neuron does
This neuron lights up on the word 'Latin' (and nearby commas) in dictionary-style word origin notes, such as 'from Latin' or 'Middle English'. It goes negative on words in math and engineering text, like 'solution' or 'analytically'. Turning it up did not bring out a word-origin theme in the model's writing.
Written by Claude from this neuron's activations and steering outputs. Confidence: medium. Think it is wrong? Flag it (opens a prefilled issue on GitHub).
Fires most on
The text windows where this neuron's activationThe number a neuron produces for a token. Large positive means it fired hard; negative means it pushed the other way. was highest. Amber marks positive activation; the outlined tokenA piece of text the model reads at once: often a word, sometimes part of a word or a punctuation mark. is the peak. Hover a token for its strength.
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strontianite⏎- Named in a pseudo-Latin manner for the name of the Scottish town Strontian. (Wiktionary)⏎- From New Latin strontia, strontium oxide, from English strontian; see
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more fragrant sauce⏎- black, green or white pepper – for some sharpness⏎- nutmeg – a popular addition in many<|endoftext|>From Latin: arcus "a bow, arch,"⏎Try this Drag one of the orange
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) of praktikos (see practical); the noun from the verb within the earlier spelling practise, on the sample of pairs akin to advise, recommendation.⏎Word Origin Middle English (in legal<|endoftext|>Keywords: Sargent Mountain⏎Historical Items Showing
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Origin late Middle English: from Latin attentio(n-), from the verb attendere (see attend). Nonetheless,
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lia, from Latin, neuter pl. of rēgālis, regal⏎An explanation
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with thyroid conditions have a higher risk of getting caught in this endless “more stress – more hormonal disbalance” circle.⏎That is why<|endoftext|>Manufacturing (from Latin manu factura, "making by hand") is the use of tools and labor
SteeringAdding a push along a neuron while the model writes, to see how its output changes. Turning it up or down.
Push this neuron up or down while the model writes, and watch what changes. The outputs were generated ahead of time, so nothing runs in your browser.
Here is something I have been thinking about lately: I have a function that is a function of time, and I am going to take the derivative of the function to find the derivative, which is a complex and beautiful result. Let's call it $ f $, which is the derivative of
Most negative on
Where the activation went furthest below zero. Blue marks negative activation. Many neurons mean something different in each direction.
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circular and windings are modeled as current sheets placed on the core surface. The induction machine geometry is shown in Figure 2. Poisson’s equation is solved analytically for geometry and temperature as shown in Figure 3. The results
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somewhat disparate. The process is illustrated using a single, simple differential equation:⏎Integrate this equation from t = 0 to t = 1. The exact solution can be found by quadrature and is⏎To use MATLAB we first construct an
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the private market equilibrium socially efficient from the viewpoint of a social planner that seeks to maximize the representative consumers utility function ? Explain why or why not, by analyzing the solution to for c and k in the
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ators.⏎- Allow them to discover how each is related to the value of the common ratio and the sign of the first term.⏎- Have them develop an explicit formula for each sequence.⏎2. A company is offering to pay you one penny on
Connections
Show on mapWhich neuronsOne of thousands of tiny dials inside each layer. It reads the text so far and produces a number, its activation, that nudges what the model writes next. in earlier layersOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. feed this one, and which later ones it feeds. Amber links excite, blue links suppress; longer bars are stronger.
Fed by earlier layers
- L6:789 excites
- L9:41 excites
- L6:1147 excites
- L8:3011 suppresses
- L6:1164 suppresses
- L8:1189 suppresses
- L9:683 excites
- L7:1885 weakly suppresses
- L9:1692 weakly excites
- L9:86 weakly excites
- L9:728 weakly suppresses
- L6:1488 weakly suppresses
- L7:2789 weakly suppresses
- L9:470 weakly suppresses
- L9:569 weakly excites
- L6:2841 weakly suppresses
- L8:1555 weakly excites
- L8:1548 weakly excites
- L9:117 weakly excites
- L9:1650 weakly excites
- L2:1667 weakly excites
- L7:2022 weakly excites
- L9:1824 weakly excites
- L7:1195 weakly excites
Feeds later layers
- L17:1349 suppresses
- L17:951 suppresses
- L15:1850 suppresses
- L11:1829 suppresses
- L11:420 excites
- L23:339 suppresses
- L19:2721 suppresses
- L11:549 suppresses
- L11:743 suppresses
- L18:2200 suppresses
- L17:2576 suppresses
- L13:1055 suppresses
- L11:1106 excites
- L21:1633 suppresses
- L11:828 suppresses
- L23:410 suppresses
- L13:1455 suppresses
- L18:1282 suppresses
- L21:585 suppresses
- L11:2330 suppresses
- L11:602 suppresses
- L19:1821 weakly suppresses
- L11:2185 weakly suppresses
- L20:1545 weakly suppresses
Activation histogramA chart of how often a neuron takes each activation value.
How often this neuron fires at each strength. Almost always it does close to nothing (the tall bars in the middle); the snippets above are the rare moments it fired hard, amber for positive and blue for negative.
Logit lensReading which output tokens a neuron pushes toward or away from, straight off its output weights, before the later layers have their say.
The words this neuron nudges the model toward (amber) or away from (blue) when it fires. A hint, not a label: many neurons work through later layersOne step in the stack the text passes through. Qwen3-0.6B has 28 layers, each with 3,072 neurons. instead.
Promotes
- anks
- 逢
- 选手
- _scores
- icks
- Scores
- ocols
- vals
- 贰
- ographics
Suppresses
- logically
- rewritten
- analy
- quadratic
- Equation
- pler
- predict
- predict
- equation
- fourth