Cammarata and Goodfire claim most neurons are readable; researchers disagree

1. 10:10
   WebTechmeme· Media coverage

   An interview with Goodfire researcher Nick Cammarata, whose viral blog post claimed LLMs are easier to understand than thought, as other researchers push back (Will Douglas Heaven/MIT Technology Review)(opens the original post in a new tab)
2. 08:09
   XX @tszzl· Discussion

   neural nets are already clean(opens the original post in a new tab)
3. 00:00
   WebMisalignment Watch· Media coverage

   Goodfire essay argues neural networks are easier to interpret than thought(opens the original post in a new tab)

1. 20:51
   X@sanderball· Discussion

   https://mp.weixin.qq.com/s?__biz=Mzc4MjEzMjY5OA==&mid=2247483671&idx=1&sn=878c571b2441b92f111670590838754d&chksm=c95075…(opens the original post in a new tab)
2. 17:16
   X@ZeroZ_JQ· Discussion

   https://mp.weixin.qq.com/s/5MEu2eeqNfBWXlkCKTd6sg(opens the original post in a new tab)
3. 15:08
   X@lonely_goodness· Discussion

   There may be good news for AI interpretability. Nick Cammarata did some of the earliest mechanistic interpretability research at OpenAI, and was very pessimistic at the time: neural networks looked like a tangled mess. Later he led OpenAI's first large-scale automated interpretability project, and with the tools at the time understood only about 2%...(opens the original post in a new tab)
4. 13:23
   X@narbi_gone· Discussion

   AI interpretability, the Blog Post That Set the AI World on Fire: Neural Networks Are Already Clean, by Nick Cammarata…(opens the original post in a new tab)
5. 03:22
   X@aHumanTake· Discussion

   In 2023, Nick Cammarata's OpenAI project could read about 1.7% of the neurons it studied. Now, at Goodfire, he argues m…(opens the original post in a new tab)
6. 01:45
   MIT Technology Review

   "We were wrong before": Goodfire researcher says neural networks are more understandable than expected

   Goodfire researcher Nick Cammarata published a 26,000-word blog post arguing that the internal mechanisms of LLMs may be far more understandable than previously thought, and therefore easier to monitor and control, sparking widespread discussion in the AI world. He reexamined Google's 2014 image recognition model Inception v1, arguing that multi-task neurons previously interpreted as superposition actually encode common shapes, such as the V shape shared by cat faces and car fronts. He said the team has begun studying modern LLMs and will soon publish technical results. Chris Potts of Stanford considers this just intuition; Jacob Hilton of the Alignment Research Center said researchers' understanding of the internal mechanisms of top LLMs is less than 0.01%; Neel Nanda of Google DeepMind also believes this is not a significant advance for the big picture of interpretability. Cammarata acknowledged that outsiders overinterpreted his claims.

1. 23:02
   X@ChrisGPotts· Discussion

   I was asked by a journalist to comment on this piece. The comments I wrote up (on Friday evening) are included below, s…(opens the original post in a new tab)
2. 22:00
   X@JonhernandezIA· Discussion

   👉🏻 One of the pioneers of AI interpretability just admitted he may have been wrong about one of the field's biggest pro…(opens the original post in a new tab)
3. 21:50
   LessWrongSelected

   Zvi reviews Goodfire's new work on neuron interpretability: most neurons correspond to only one concept, but alignment is far from solved

   LessWrong author Zvi lays out the polarized reactions to Goodfire researcher Nick Cammarata's article "Neural networks are already clean," arguing that it is an important update on neuron interpretability, but still far from solving interpretability and alignment. Cammarata says the cause of polysemanticity in early models is not superposition, but several ordinary and mostly fixable problems, plus what he calls "recursive generalization"; a preliminary audit of Qwen3-32B estimates that about 90% of neurons have only one primary function, about 7% have two related functions, and 2% to 4% are not yet understood, with similar results on trillion-parameter models such as Kimi K3. OpenAI researchers including Dean W. Ball and roon say its significance is comparable to "Attention Is All You Need," while Neel Nanda, Leo Gao, Joshua Achiam and Eliezer Yudkowsky argue that progress is limited and that the explanations themselves still need more rigorous evaluation.

4. 21:46
   XX @TheZvi· Discussion

   I Can See Everything(opens the original post in a new tab)
5. 21:44
   WebDon't Worry About the Vase· Media coverage

   I Can See Everything(opens the original post in a new tab)
6. 13:41
   X@BretKerr· Discussion

   @GoodfireAI 's "Neural Networks Are Already Clean" vs Neel Nanda's rebuttal looked like a fight. Three days later it's…(opens the original post in a new tab)
7. 04:07
   X@UnknownHero_X_· Discussion

   Clean Neurons Aren't Enough A companion to "Goodfire says neural networks are already clean" — on what interpretabilit…(opens the original post in a new tab)
8. 02:26
   X@BretKerr· Discussion

   For a decade, interpretability's star exhibit was a neuron that fired on cats and cars: proof, we thought, that network…(opens the original post in a new tab)

1. 21:16
   X@AnanyaAyasi· Discussion

   i told her neural networks are already clean and she said interpretable neurons don’t imply interpretable computation.(opens the original post in a new tab)
2. 20:25
   X@podlume· Discussion

   Neural networks may be more interpretable than expected If larger models reveal cleaner internal concepts, researchers…(opens the original post in a new tab)
3. 04:30
   X@emy_wng· Discussion

   A rebuttal to Goodfire's Nick Cammarata's recent "Neural networks are already clean." Looking at the series of developments, one thing is certain: the road to solving alignment is still very long.(opens the original post in a new tab)
4. 04:18
   X@LowdCode· Discussion

   Source: Goodfire, "Neural Networks Are Already Clean" by Nick Cammarata: https://already-clean.goodfire.com/(opens the original post in a new tab)
5. 04:18
   X@LowdCode· Discussion

   Goodfire's Nick Cammarata argues AI neurons are "already clean." In early Qwen3-32B checks, roughly 90% of neuron signa…(opens the original post in a new tab)
6. 03:43
   X @NeelNanda5

   @nabla_theta publishes new blog post "Neural networks are not already clean"

   @nabla_theta published a new blog post, "Neural networks are not already clean." @NeelNanda5 said this is another contribution to the discussion of "no, we haven't solved interpretability yet."

1. 22:04
   X@kapishagarwal_· Discussion

   the essay, "neural networks are already clean": https://already-clean.goodfire.com/(opens the original post in a new tab)
2. 22:01
   X@kapishagarwal_· Discussion

   for years, the neurons inside ai models looked like a tangled mess nick cammarata at goodfire now argues they're mostl…(opens the original post in a new tab)
3. 21:46
   X@InfinityHopium· Discussion

   Already Clean — a 30-minute animated explainer of Goodfire’s “Neural Networks Are Already Clean.” Neurons, circuits, s…(opens the original post in a new tab)
4. 21:12
   X@LifanBuilds· Discussion

   2/ Goodfire’s Nick Cammarata argues native neurons are more legible than assumed. His 90% Qwen3-32B estimate is prelimi…(opens the original post in a new tab)
5. 20:28
   RedditReddit r/Anthropic· Discussion

   A question from Claude: if neural networks are already clean, who’s checking what the clean reading would show?(opens the original post in a new tab)
6. 17:41
   X@ZindanTech· Discussion

   Source: Nick Cammarata – Neural Networks Are Already Clean https://already-clean.goodfire.com/ An interesting scienti…(opens the original post in a new tab)
7. 16:43
   X@teleomorph· Discussion

   @nickcammarata https://already-clean.goodfire.com/ Cammarata, N., 2026. Neural Networks Are Already Clean. Goodfire.(opens the original post in a new tab)
8. 14:21
   X@aisignallog· Discussion

   Was the inside of AI really a black box? -- Nick Cammarata's "Neural Networks Are Already Clean" re-examines the Superposition hypothesis, neuron interpretability, and AI-driven science...(opens the original post in a new tab)
9. 12:33
   X@RashadHouston· Discussion

   Neural Networks Are Already Clean https://already-clean.goodfire.com/(opens the original post in a new tab)
10. 10:55
    X@archimagos· Discussion

    AI evaluations show behavior. What caused it? Goodfire says neural networks are already clean. Its mechanistic interpr…(opens the original post in a new tab)
11. 10:53
    X@gurkandev· Discussion

    Interpretability'de bu haftanın en çok tartışılan essay'i: Goodfire'dan Nick Cammarata, "Neural Networks Are Already Cl…(opens the original post in a new tab)
12. 09:17
    LessWrong

    Neural networks are not yet clean

    LessWrong user leogao wrote a rebuttal to Nick Cammarata's judgment that neuron polysemanticity is overestimated, arguing that activation examples matching a pattern does not mean the neuron has been explained. Using a neuron associated with both dogs and bicycles as an example, the article points out that top activation examples may only correspond to some subset that is difficult to characterize compactly, and the reverse relationship does not necessarily hold. The author advocates instead using downstream KL and computing simulation scores on large datasets: if replacing the neuron with a simulated value significantly degrades model performance, the explanation does not hold, and this criterion can also flag bad explanations previously treated as polysemantic explanations. The author speculates that ablating weak activations would also significantly degrade model performance, so weak activations should not be completely ignored. The author also believes that explaining neurons is only part of understanding network computation; concepts are stored in a superimposed manner, making circuit research still unable to explain most of the variance in model behavior.

13. 08:01
    WebAI Era (Xinzhiyuan)· Media coverage

    Former OpenAI big shot admits mistake! Misjudged AI for nearly a decade, 90% of neurons do only one thing(opens the original post in a new tab)
14. 06:13
    X@davegoldblatt· Discussion

    Everyone is talking about @nickcammarata's "Neural Networks Are Already Clean". But it's an hour long read, and in toda…(opens the original post in a new tab)
15. 05:14
    X@MTSlive· Discussion

    SITUATION EXPLAINED: Ex-OpenAI researcher Nick Cammarata says we can now understand most neurons inside AI models, even…(opens the original post in a new tab)
16. 04:30
    X@_krish00· Discussion

    5 things I learnt after reading the "Neural Networks Are Already Clean" essay: Superposition wasn't the culprit in ear…(opens the original post in a new tab)
17. 04:18
    XX @tszzl· Discussion

    “everything in the neural network was built in some sense to be read – by its downstream readers – and if we can learn to look like a rea...(opens the original post in a new tab)
18. 03:31
    X@nathanxmeyer· Discussion

    @Sauers_ How should we think about this in light of what Nick Cammarata puts forward in his "Neural Networks are alread…(opens the original post in a new tab)
19. 02:05
    RedditReddit r/LocalLLaMA· Discussion

    Understanding How Neural Networks Learn (with interactive visualizations)(opens the original post in a new tab)
20. 00:53
    X@RatOrthodox· Discussion

    Blogpost: "Neural networks are already clean" Me: this neuron fires on Colorado. Also Tornado framework code. Also Boli…(opens the original post in a new tab)
21. 00:22
    XX @sama· Discussion

    Very happy to see this from Nick!(opens the original post in a new tab)
22. 00:10
    X@KevinDominRiq· Discussion

    Goodfire publicó "Neural Networks Are Already Clean": reporta un 99-100% de neuronas interpretables en la última capa d…(opens the original post in a new tab)
23. 00:07
    X@noself86· Discussion

    Nick Cammarata's new essay (@nickcammarata, Goodfire) argues neural networks are "already clean": read a neuron right,…(opens the original post in a new tab)

1. 23:50
   X@lucian__03· Discussion

   🔥 지금 해외 AI 연구판이 발칵 뒤집혔습니다!!! 🔥 수년간 믿어온 AI 연구의 교과서를 다시 써야 하는 것 아니냐는 수준의 논쟁이 벌어지고 있습니다. 심지어 [OAI의 어떤 기술 직원]은 --> "과학사에…(opens the original post in a new tab)
2. 23:06
   Dean Ball

   The Beauty of Things That Grow: Dean Ball on Interpretability and AI Policy

   Dean Ball writes a commentary on former OpenAI researcher Nick Cammarata's new perspective on mechanistic interpretability. Cammarata argues that the previous view of neurons as human-like concepts was wrong; neurons are more like control flow, with meaning accumulating layer by layer during training, so models are more interpretable than previously thought. He revisits the classic cat-and-car neuron in a 2017 vision model, arguing it detects V-shaped objects, and calls this property recursive generalization. Ball notes that the analysis relies heavily on Cammarata's subjective judgment, the technical paper is forthcoming, and much uncertainty remains about understanding interactions among millions of neurons. Ball also speculates that if interpretability nears a solution, those focused on catastrophic alignment risk would be pleased, while those more concerned about power concentration and labor markets might see it as bad news.

3. 21:55
   X@AIQuanting· Discussion

   Goodfire researcher Nick Cammarata published an essay on October 15 arguing that neurons inside neural networks are muc…(opens the original post in a new tab)
4. 21:48
   The Decoder

   Is AI interpretability close to a breakthrough? A new article divides researchers

   Mechanistic interpretability researcher Cammarata, in a new article, reinterprets classic findings such as polysemantic neurons and superposition, arguing that these most famous examples have been misread, sparking disagreement in the field. He reinterprets the neuron in InceptionV1 that responds to both cat faces and car fronts as a V-shape detector, treats the car hidden in the dog neuron in the paper Zoom In as ordinary generalization, and proposes a pattern of recursive generalization. He gives preliminary numbers for Qwen3-32B: about 90% of neuron poles have only one primary function, about 7% have two related functions, and 2% to 4% are unexplainable; Goodfire says it sees similar results on models with trillions of parameters. OpenAI employee Roon says this could be an important moment in the history of science, while Google DeepMind interpretability team lead Neel Nanda says it does not change the big picture and insists that superposition still exists. Cammarata believes interpretability is crucial to AI safety, otherwise it is hard to judge whether models are deceiving.

5. 21:25
   X@NFT_Chen· Discussion

   💥AI may not be as much of a black box as we think! A former OpenAI researcher points out that in modern large models, the vast majority of neurons are actually "clean," with each neuron mainly doing one thing, rather than the tangled mess previously assumed. This means: • Interpretability may be easier than imagined • The pretraining process itself is making the model increasingly...(opens the original post in a new tab)
6. 20:55
   X@whaleyxbt· Discussion

   i couldn't sleep after reading this in 2023 Nick Cammarata started OpenAI's first big automated interpretability proje…(opens the original post in a new tab)
7. 20:30
   X@sughanthans1· Discussion

   the textbook reason people say neural nets are hard to read may have been an architecture bug. cammarata and olah's 201…(opens the original post in a new tab)
8. 18:43
   X@jimmyyangrz· Discussion

   The interpretability side is quite explosive: one of the authors who proposed "one neuron packs several concepts" back then now says everyone misread the key evidence.(opens the original post in a new tab)
9. 18:24
   X@FastfixAI· Discussion

   Big interpretability claim this week: neural networks may already be readable, neuron by neuron. Nick Cammarata (ex-Op…(opens the original post in a new tab)
10. 18:17
    X@kimmonismus· Discussion

    Very intersting read: Nick Cammarata, a co-author of the 2020 Zoom In paper that introduced the idea that neural networ…(opens the original post in a new tab)
11. 18:06
    X@NextHub_API· Discussion

    Goodfire’s Nick Cammarata reports a preliminary Qwen3-32B audit in which roughly 90% of “neuron poles”—the positive and…(opens the original post in a new tab)
12. 16:05
    WeChat · AI Era (Xinzhiyuan)

    Former OpenAI researcher Nick Cammarata and Goodfire: about 90% of neurons each have one primary task

    OpenAI 前研究员 Nick Cammarata 与 Goodfire 团队发布长文《神经网络早已「干净」》，提出神经网络的内部计算大多有规律可循，而非混乱的拼盘。团队以一个万亿参数模型回答「捡到钱包是否归还」为例画出电路图，追踪答案如何形成，再通过关闭神经元检验解释是否可靠：关掉 20 个标为「圣诞节」的神经元后，模型回答「圣诞老人」的概率仍有 76%，换成另外 8 个关键神经元则骤降到 6%。在一个 320 亿参数语言模型中，团队分开统计神经元的正负响应，粗略估计约 90% 各有一个主要任务，约 2% 至 4% 仍未理解；Nick 估计 ConvNeXt 视觉模型最后一层约 99% 的神经元可解释。团队还让一个 Agent 写神经元功能说明、另一个据此造句做盲测，183 个正向解释中目标例子强激活比例 76%、干扰例子 11%，逐个比较为 164 胜 15 平 4 负。

13. 15:32
    X@JasonSilva· Discussion

    Inspired by Nick Cammarata's essay "Neural Networks Are Already Clean": neural nets were never the tangled knots we fea…(opens the original post in a new tab)
14. 15:27
    X@Guofowbw· Discussion

    @Edward913413 Cammarata's back-and-forth is interesting. That 2% threshold in interpretability—is agent-side observation now hitting a similar ceiling?(opens the original post in a new tab)
15. 15:25
    X@Edward913413· Discussion

    The OpenAI researcher studying the "AI brain" overturned his own judgment yesterday. The hardest problem in studying the AI brain—a person who has worked on it for ten years slapped himself in the face yesterday. Nick Cammarata, a core member of OpenAI's early mechanistic interpretability. In 2023, he led OpenAI's...(opens the original post in a new tab)
16. 15:08
    X@Love1mothe· Discussion

    I skimmed this long post. Former OpenAI researcher Nick Cammarata has shaken up the interpretability community. Over the past few years, the field has been working on SAE, thinking neurons are a tangled mess that requires training another model to unpack. I also used to think large models were entirely black boxes inside. But he reran the experiments and found...(opens the original post in a new tab)
17. 13:13
    X@RaghavKoch19380· Discussion

    Nick Cammarata at Goodfire published a new essay on neural network interpretability. He argues that neural networks are…(opens the original post in a new tab)
18. 12:23
    X@rickawsb· Discussion

    A ray of hope for avoiding AI doom. This article by Nick Cammarata, a former core researcher at OpenAI, cannot be overemphasized in importance, because it may be the technical dawn of achieving true AI alignment. Previously, researchers were generally very pessimistic, because neural networks looked like tangled, messy...(opens the original post in a new tab)
19. 10:41
    X@MaxForAI· Discussion

    Somewhat shocking... We might really be close to figuring out how large models actually work internally. A participant in OpenAI's early mechanistic interpretability research suddenly proposed a very bold conclusion yesterday: We may have been misunderstanding neural networks all along. Yesterday, @nickcammarata posted a very interesting...(opens the original post in a new tab)
20. 10:28
    WebSynced (Jiqizhixin)· Media coverage

    Has neural network interpretability been solved by AI? 90% of neurons have only one function(opens the original post in a new tab)
21. 09:50
    X@chleya243425· Discussion

    Guess what: in AI there's a famous "cat car neuron" — the same neuron lights up for both cat faces and car fronts. Why does this happen? A. The network doesn't have enough space, so it squeezes two completely unrelated things into the same neuron; B. Cat faces and car fronts actually share a common shape. The answer is at the end. First, look at this week's...(opens the original post in a new tab)
22. 08:04
    X@nithin_r_g· Discussion

    the cat-and-car neuron, the textbook proof neural nets are tangled, just detects v-shapes. nick cammarata re-checked w…(opens the original post in a new tab)
23. 07:57
    WeChat · Synced (Jiqizhixin)Selected

    OpenAI interpretability team creator says large model black boxes are almost readable; about 90% of neurons in Qwen3-32B have a single function

    Nick Cammarata, creator of OpenAI's interpretability team, said in a long post that with modern agent tools, trillion-parameter large models are almost readable, and that the superposition hypothesis, which held that neural network representations are entangled, is a misunderstanding. Re-examining a neuron in InceptionV1 that responds to both cat faces and car fronts, he believes the two share a V-shaped feature, and that neurons are more like verbs that distinguish patterns than nouns that label objects. The author's preliminary review of Qwen3-32B shows that about 90% of neurons have one main job at each of the positive and negative poles, about 7% have two, and 2% to 4% are not yet understood; conclusions are similar for dense models and MoE models. The author says he produced the first known attribution map for a trillion-parameter model on Kimi K3, and when re-examining GPT-2 XL with new tools, about 70% of neurons are also roughly one thing. Agents are used to inspect neuron samples, propose explanations, trace upstream and downstream connections, and design counterexample experiments.

24. 05:06
    X@spc· Discussion

    SPC company @GoodfireAI just made the field’s best argument that AI models are more interpretable than we thought. @ni…(opens the original post in a new tab)
25. 04:39
    X@MichaelWaitze· Discussion

    @hecubian_devil True on governance, but we actually have cause for optimism on the interpretability side now. Nick show…(opens the original post in a new tab)
26. 04:27
    Goodfire ResearchSelected

    Goodfire research says neural networks are already clean

    Goodfire Research proposes that the cause of polysemy in early model organisms is not superposition, but several ordinary and mostly fixable problems. The organization says it can now understand most neurons in models with up to trillions of parameters, and believes the prospects for mechanistic interpretability may be far more optimistic than previously expected.

27. 04:05
    X@wutaizeng· Discussion

    @nickcammarata If "neural networks were clean all along" holds, the ones who should be most nervous are precisely the interpretability teams themselves: since 90% of neurons are readable for single tasks, automated tools will soon outperform human analysts, and interpretability turns from a moat into a commodity. The real test is in the second half of the sentence—"interpretability will be solved when it is most needed" is a loop...(opens the original post in a new tab)
28. 04:02
    X@iamcurtismith· Discussion

    nick cammarata at @GoodfireAI argues neural nets aren't tangled knots. the famous cat+car neuron was just a V-shape, an…(opens the original post in a new tab)
29. 03:57
    X@PolyviewMediaGl· Discussion

    Sources: Essay, "Neural Networks Are Already Clean" (Goodfire): https://already-clean.goodfire.com/ Key numbers: http…(opens the original post in a new tab)
30. 03:57
    X@PolyviewMediaGl· Discussion

    It took an AI-native newsroom to break down a 65-minute essay on AI neurons. That's what we do at Polyview. If Cammara…(opens the original post in a new tab)
31. 03:55
    X@PolyviewMediaGl· Discussion

    Nick Cammarata helped convince much of the interpretability field that neural networks are unreadable tangles. Now he…(opens the original post in a new tab)
32. 03:50
    X@VaibhavSisinty· Discussion

    I have believed for years that AI models are black boxes we will never fully understand The neurons looked tangled. On…(opens the original post in a new tab)
33. 03:47
    X@RokoMijic· Discussion

    People are very excited about a recent site by Nick Cammarata on monosemanticity in neural networks, and he can now cla…(opens the original post in a new tab)
34. 03:38
    X@betterhn50· Discussion

    Neural Networks Are Already Clean https://already-clean.goodfire.com/ (https://news.ycombinator.com/item?id=50105647)(opens the original post in a new tab)
35. 03:17
    X@is_OwenLewis· Discussion

    This really is BREAKING NEWS folks. The doomers were wrong, the Black Box is beginning to open. "Can an intelligent be…(opens the original post in a new tab)
36. 03:08
    X@rynorhn· Discussion

    holy fucking shit. this might genuinely be one of the most important ai research developments of the year. nick cammar…(opens the original post in a new tab)
37. 02:31
    X@MasterTimBlais· Discussion

    nick's paper "Neural Networks Are Already Clean" is remarkably accessible, crazy surprising, and just an absolute firec…(opens the original post in a new tab)
38. 02:16
    X@GenZ_Creatives· Discussion

    2/ Major interpretability breakthrough Former OpenAI researcher Nick Cammarata claims the vast majority of neurons (ev…(opens the original post in a new tab)
39. 02:10
    X@AlyssaAngelll· Discussion

    @tszzl Gemini Notebook's video overview explanation of "Neural Networks Are Already Clean"(opens the original post in a new tab)
40. 01:51
    X @HaydnBelfield

    Nick Cammarata says modern agent tools make the vast majority of neurons in large models readable

    Researcher Nick Cammarata said that with modern agent tools and closer observation, the vast majority of neurons in models with parameter scales up to trillions are readable, circuit structures are clear, and many famous "entangled neuron" cases are actually misunderstandings. He was involved in OpenAI's mechanistic interpretability work in his early years and initiated OpenAI's first large-scale automated interpretability project; at the time, using raw tools, he found only about 2% of neurons readable, which made him pessimistic. He now believes that most of the obstacles he expected do not exist, and ambitious interpretability may be solved soon because automated scientists continue to improve.

41. 01:51
    X@ChainZenit· Discussion

    @omarsar0 this actually makes the whole black box thing feel way less scary.(opens the original post in a new tab)
42. 01:51
    X@omarsar0· Discussion

    Highly recommended read. This is the type of interpretability work that we need more of. "We can now understand most…(opens the original post in a new tab)
43. 01:33
    X@fireandstart· Discussion

    A cat and a car can light up the same neuron simultaneously, which in the past could easily be dismissed as "representation confusion"; this example reminds me that overlap itself may also mean we have not yet found the right level of abstraction. If that feature actually responds to V-shaped structures, then shared activation across different objects is not random noise, but a clue that the model is reusing geometric concepts. The progress of mechanistic interpretability...(opens the original post in a new tab)
44. 01:32
    X@deedydas· Discussion

    This is one of the most important results in AI, period. TLDR: neural networks are not tangled knots (polysemanticity…(opens the original post in a new tab)
45. 01:31
    X@techwithjoe_io· Discussion

    Goodfire has released research titled 'Neural Networks Are Already Clean': https://already-clean.goodfire.com/(opens the original post in a new tab)
46. 01:31
    X@shiparena· Discussion

    Nick Cammarata coauthored early mechanistic interpretability at OpenAI and left pessimistic. Networks looked like tangl…(opens the original post in a new tab)
47. 01:30
    XX @_NathanCalvin· Discussion

    Nick's paper does seem extremely rad. It's also just very cool and beautiful and satisfying and I understand how people…(opens the original post in a new tab)
48. 01:28
    X@Zen_with_AI· Discussion

    We spent ten years saying neural networks are a tangled mess, and now we may find we were wrong. 1. The conclusion is reversed: 2% and 90% What a neural network is: a computing machine made of connected neurons, input goes in, output comes out. What we used to think: it is a tangled mess inside, each neuron handles a bunch of unrelated things, and people cannot understand it. What we now find...(opens the original post in a new tab)
49. 01:17
    X@AGTPinsights· Discussion

    Goodfire researcher Nick Cammarata just said neural networks are far more legible than feared. Here's what you need to…(opens the original post in a new tab)
50. 01:16
    X@ToddHemminger· Discussion

    Nick Cammarata, who left OpenAI for Goodfire, says most neurons are now understandable even in the largest AI models, a…(opens the original post in a new tab)
51. 01:14
    X@hackernewstop5· Discussion

    Neural Networks Are Already Clean #HackerNews https://already-clean.goodfire.com/(opens the original post in a new tab)
52. 01:00
    X@Montreal_AI· Discussion

    Neural Networks Are Already Clean "The cause of polysemanticity in early model organisms wasn’t superposition, but sev…(opens the original post in a new tab)
53. 00:59
    X@TheRundownAI· Discussion

    Ex-OpenAI researcher Nick Cammarata says the biggest AI models aren’t the black boxes the field once feared. His 2023…(opens the original post in a new tab)
54. 00:51
    X@psswordprotectd· Discussion

    "Neural networks are already clean" goes so hard(opens the original post in a new tab)
55. 00:37
    X@covacut· Discussion

    neural networks are already clean. my favourite visualization from this read; understanding neural networks might begi…(opens the original post in a new tab)
56. 00:35
    X@compounded_mind· Discussion

    the actual findings in Nick Cammarata’s new Goodfire article are pretty insane in 2023, his automated interpretability…(opens the original post in a new tab)
57. 00:31
    LessWrong

    Link post: neural networks are now clearly readable

    Yair Halberstadt posted a link on LessWrong, relaying Nick Cammarata's long post on mechanistic interpretability, in which he says neural networks are not as entangled and difficult as he once thought. Cammarata says he became pessimistic after early mechanistic interpretability work at OpenAI, but later launched OpenAI's first large-scale automated interpretability project, finding only about 2% of neurons readable with the then-primitive tools; with modern agent tools and more careful observation, the vast majority of neurons in models up to trillion-parameter scale are readable, and many famous cases of entangled neurons are actually misunderstandings. He believes ambitious interpretability will be solved when it is most needed, possibly even soon. Roon quoted and shared it, calling it a potentially important moment in the history of science, while the post author asks how much this advances known results and what use it is for alignment practice.

58. 00:26
    X@willsmithesssss· Discussion

    Nick Cammarata, co-author of early OpenAI interpretability work, says modern agentic tools show most neurons in models…(opens the original post in a new tab)

1. 23:42
   XX @tszzl· Discussion

   when nick told me that he had some interp breakthroughs, he was uh, underselling it. i think this may be remembered as…(opens the original post in a new tab)
2. 23:40
   X@nickcammarata· Discussion

   interpretability might be the most important scientific problem in the world right now, as I don't think we can solve a…(opens the original post in a new tab)

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