the word 'case' in set 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. 18 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 'case', as in 'in point', 'the case for', 'case study' or 'in the case of'. When it was turned up, the model kept repeating 'case study' and similar phrases. Its negative side is weak and mostly the word 'depending'.
Written by Claude from this neuron's activations and steering outputs. Confidence: high. 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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that our writers write in simple English, with as little use of technical language as possible. Some cases justify the use of some technical words. The following is a case in point. This
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, limited the part it can play in shaping the economy, undervalued its contribution to national output, wrongly led to excessive privatization and outsourcing, ignored the case for the taxpayer sharing in the rewards of a collective—public—process of value
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one Million units of the Coronavirus – a virus, which would get a person in the case of a natural infection never. This was given to the anesthetized monkeys
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global poor. We think it is important to add this to the case study to allow readers to explore it fully, and to encourage discussion and wider dissemination
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says Vyn.⏎If farmers and the industry are concerned about phosphorus run-off, the case for banding gains significant strength, argues Vyn. “It provides an opportunity
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, complementary or even equivalent to the ecosystem approach (e.g. the Code for Responsible Fisheries, the Sustainable Forest Management approach, adaptive forest management). These and the case studies in the searchable component of the sourcebook provide examples of how the ecosystem approach
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: how to design a system that can be used to determine the level of human intelligence levels of people who are currently in the world today, from the most accurate, how to design a way to determine how people are actually capable of doing things,
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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enter the ID and Password and then we should all be able to speak and see each other at the same time (you may need to have some earphones, depending if your device has a built in speaker or not).⏎PLEASE NOTE PARENTS
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to Newfoundland in the mid 20th century. In fact, the only common thread was a mutual childhood memory of playing on a wreck of varying descriptions – depending on the age and recall power of the narrator.⏎In spite of the odds against
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?⏎By definition, an aperture is the size (in inches or millimetres) of the optical piece collecting light in a telescope. That optical instrument is different depending on the type of telescope you are using. In a refracting telescope, the
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financial obligations and debts. These can include loans, accounts payable, and accrued expenses. Similar to assets, liabilities can also be classified as current or long-term, depending on their repayment timeline.⏎Shareholders' equity, also referred to as net worth
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
- L16:2444 weakly suppresses
- L16:260 weakly excites
- L13:1166 weakly excites
- L5:971 weakly excites
- L16:282 weakly excites
- L17:1445 weakly excites
- L0:2450 weakly suppresses
- L10:2711 weakly suppresses
- L4:1521 weakly suppresses
- L9:1001 weakly excites
- L11:2327 weakly suppresses
- L0:794 weakly excites
- L4:1124 weakly suppresses
- L12:457 weakly suppresses
- L7:1486 weakly excites
- L3:2064 weakly excites
- L0:1175 weakly excites
- L15:699 weakly excites
- L8:1061 weakly suppresses
- L10:2520 weakly excites
- L17:148 weakly excites
- L2:1233 weakly suppresses
- L3:2304 weakly excites
- L0:1848 weakly excites
Feeds later layers
- L24:2442 weakly excites
- L19:263 weakly suppresses
- L21:2716 weakly excites
- L24:2092 weakly suppresses "self" before esteem or image
- L25:25 weakly suppresses
- L21:2218 weakly excites
- L19:337 weakly suppresses
- L21:463 weakly suppresses
- L24:1018 weakly suppresses
- L24:3003 weakly excites
- L21:2800 weakly excites
- L19:1147 weakly suppresses
- L21:997 weakly excites
- L22:1541 weakly suppresses
- L21:1661 weakly excites
- L20:1374 weakly suppresses
- L26:1547 weakly excites
- L20:2709 weakly suppresses
- L24:2444 weakly suppresses
- L23:2916 weakly excites
- L19:898 weakly suppresses
- L20:2199 weakly excites
- L21:2656 weakly suppresses
- L19:784 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
- aways
- ña
- ñas
- icates
- uits
- away
- spared
- 违
- aneous
- uit
Suppresses
- 主意
- 口气
- 灵
- spir
- Bin
- 撸
- 辦法
- oko
- Span
- Phaser