"life" after origins or search phrases
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. 8 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 fires on the word "life" when it follows phrases like "origins of", "search for" or "seeded", about how life began or was looked for. Turned up, the model's output became unfocused, so the steering does not clearly show this theme.
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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the comet, the lander will harpoon itself into place to keep it from ricocheting back off into space.⏎It may hold secrets on the origins of life⏎Comets are balls of ice and dirt left over after the dawn of the
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weird findings could be on the horizon. Rosetta could help scientists probe the origins of life in the solar system or even uncover some of the long-standing mysteries surrounding how a
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.⏎The organic material of comets could have contributed to Earth's water and even the beginning of life on the planet.⏎"We desperately need to know the composition [of comets]
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Researchers think life emerges through a series of chemical interactions at water-mineral interfaces, so a wet seafloor on Ganymede might be a key ingredient for life there.⏎› Unannotated version⏎Image credit: NASA/JPL-Caltech
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spacecraft Curiosity on mars has generated much interest in India and the world. Certainly, this is a major milestone in earth's search for life elsewhere in space. But another event of significance for India's national security went relatively
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could hold clues to the origin of the solar system and organic molecules that may have seeded life on Earth. NASA also is planning a crewed mission to an asteroid by
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 been working on this problem, and I can't help you. This is a problem. I am really stuck. Maybe I should look up the data. This is getting to the point of being a problem. Okay, let's
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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, distribution or composition of gardens.⏎The team of the STEREO III project GARMON looked into this and was able to use various types of satellite and aerial data to draw up a map of the gardens, showing the extent, composition
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of Wood Buffalo National Park in Northern Alberta, Canada.⏎The said biggest beaver dam can also be seen through Google Earth’s images and can be seen from outer space.⏎The Canadian beavers amazingly built a staggering 2790 feet wide
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and effective detection of natural gas leaks through evaluation of geobotanical stress signatures.⏎The remote sensing techniques being developed employ advanced spectrometer systems that produce visible and near infrared
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and after extreme storms to determine if or how the landscape changed.⏎"Computers cannot yet automatically identify damages and geomorphic changes to the coast from the oblique aerial photographs,"
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
- L3:1639 weakly suppresses
- L5:2101 weakly excites
- L0:2253 weakly excites
- L6:1207 weakly suppresses
- L3:1804 weakly suppresses
- L5:1357 weakly excites
- L0:370 weakly excites
- L7:3061 weakly excites
- L3:475 weakly suppresses
- L7:2491 weakly excites
- L5:2582 weakly excites
- L2:2849 weakly suppresses
- L0:555 weakly excites
- L6:2243 weakly suppresses
- L4:1754 weakly suppresses
- L7:2627 weakly suppresses
- L6:2576 weakly excites
- L1:1190 weakly excites
- L5:1047 weakly excites
- L3:1922 weakly excites
- L7:1908 weakly suppresses
- L2:2413 weakly excites
- L4:3035 weakly excites
- L5:189 weakly suppresses
Feeds later layers
- L12:1749 suppresses
- L20:2607 excites
- L9:289 excites
- L9:1429 weakly suppresses
- L11:2019 weakly suppresses
- L10:264 weakly suppresses
- L22:1713 weakly excites
- L21:2625 weakly excites
- L9:1688 weakly excites
- L10:883 weakly excites
- L15:2068 weakly excites
- L20:1514 weakly excites
- L20:1401 weakly excites
- L13:2116 weakly excites
- L24:3066 weakly excites
- L9:730 weakly suppresses
- L9:22 weakly excites
- L10:1659 weakly suppresses
- L23:1962 weakly excites
- L19:2745 weakly excites
- L11:1306 weakly excites
- L18:792 weakly excites
- L9:619 weakly suppresses
- L10:1608 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
- hans
- idelity
- $instance
- retty
- ivate
- 台上
- suce
- Truman
- [opt
- (instance
Suppresses
- elor
- /effects
- pression
- thood
- �
- difficulty
- .AddComponent
- League
- ather
- -league