MIT team has GPT-6 Astra directly rewrite Qwen weights

1. 16:09
   X@jialiwanai· Discussion

   Wait... AI can now edit another AI's brain? 🤯 MIT researchers asked GPT-6 Astra to directly modify Qwen3.5-0.8B's neur…(opens the original post in a new tab)
2. 15:54
   X@mtsmys· Discussion

   MIT CSAILのWeight-Space Programmingは言ってみればコンパイラーのビルドと同じなので，既に通ってきた道ではある．実現している事は面白いけれど．と同時に，この方向での研究が進むと学習環境が不要になるので，環境を…(opens the original post in a new tab)
3. 15:53
   X@yonifra· Discussion

   לפי חוקרי MIT, GPT-6 Astra שינה ישירות 0.016% ממשקלי מודל מתמטיקה קטן, בלי אימון או גרדיאנטים. ציון MATH-500 עלה מ-7.9%…(opens the original post in a new tab)
4. 15:52
   X@justnadeem7· Discussion

   MIT researchers used GPT-6 Astra to edit just 0.016% of a Qwen model’s weights. Its math-test score jumped from 7.9% t…(opens the original post in a new tab)
5. 15:48
   X@ZJM330· DiscussionIn Chinese, as written

   没跑一步梯度，小模型数学分 7.9% 变 48.1%。 MIT 让 GPT-6 Astra 直接读 Qwen3.5-0.8B 的权重，找出导致推理死循环的部分，只改 0.016%。 我认为以后调模型可能先让大模型读权重。但只在 0.8…(opens the original post in a new tab)
6. 15:46
   X@umiyuki_ai· Discussion

   MITのRikiyaTakehi氏らによるヤバ研究。Qwen3.5-0.8Bに数学問題を解かせると同じ思考を延々とループして一向に答えが出ないバグ的な挙動があった。そこで研究者はAstraに「Qwenを修正してくれない？」って丸投げしてみ…(opens the original post in a new tab)
7. 15:39
   X@goyashy· Discussion

   Source: "Sparks of Weight-Space Programming" by Rikiya Takehi, Jayden Teoh, Alex Zhang and Omar Khattab (MIT CSAIL): ht…(opens the original post in a new tab)
8. 15:39
   X@ScarletKc· DiscussionIn Chinese, as written

   MIT CSAIL 的 Omar Khattab 团队刚发了一篇很有意思的博客，提出了一个叫 Weight Space Programming（权重空间编程）的概念：现在的前沿大模型已经可以不靠梯度下降，直接用自然语言推理去读写和修改神经…(opens the original post in a new tab)
9. 15:39
   X@goyashy· Discussion

   GPT-6 Astra did not teach Qwen math. It stopped Qwen looping. MIT CSAIL researchers had Astra edit 0.016% of Qwen3.5-0…(opens the original post in a new tab)
10. 15:30
    X@FegaPierre· Discussion

    Un modèle d'IA a corrigé un autre modèle en modifiant directement ses poids, sans aucun entraînement. C'est ce que décr…(opens the original post in a new tab)
11. 15:15
    X@erguoyi· DiscussionIn Chinese, as written

    MIT CSAIL 最近做了个挺夸张的实验：让 GPT-6 Astra 不用梯度下降，也不重新训练，直接动另一个神经网络的原始权重。 拿 Qwen3.5-0.8B 开刀，只改了约 13 万个权重，占整个模型的 0.016%，MATH-5…(opens the original post in a new tab)
12. 14:50
    X@blackright0· Discussion

    On November 2, 2026, Rikiya Takehi, Jayden Teoh, Alex Zhang, Omar Khattab and others at MIT CSAIL announced an approach called "Weight-Space Programming." OpenA…(opens the original post in a new tab)
13. 14:48
    X@apikey_official· Discussion

    "Infinite repair" is not possible, but the underlying paradigm of model optimization has indeed changed. 🤯⚡ 1⃣ It is "treating the disease," not "creating a god": Qwen's score surge is because Astra, like a surgeon, precisely cut out its infinite-loop circuit at Layer 15/19. It could already do it; it was just that previously 93% of the wrong answers all…(opens the original post in a new tab)
14. 14:47
    X@LShivkumarreddy· Discussion

    An AI just debugged another AI’s brain. MIT researchers gave GPT-6 Astra access to Qwen’s weights. No retraining. No…(opens the original post in a new tab)
15. 14:43
    X@subhamCenrax· Discussion

    LLMs can now read and write neural network weights directly. Normally, weights come from gradient descent. In a new MI…(opens the original post in a new tab)
16. 14:43
    X@Pa3ck3· Discussion

    What if AI could rewrite another AI’s brain instead of retraining it? MIT researchers used GPT-6 Astra to directly mod…(opens the original post in a new tab)
17. 14:34
    X@chataclaw· Discussion

    In an MIT CSAIL experiment, an LLM read what a neural network learned from its raw weights and directly rewrote only 0.016% of Qwen's weights to raise its math weakness on MATH-500 from 7.9% to 48.1%. No gradients and no training loop. When the LLM takes on the role of the optimizer…(opens the original post in a new tab)
18. 14:33
    X@softverd· Discussion

    A small Qwen model scored 7.9% on MATH-500 because it kept looping. GPT-6 Astra read its weights and edited 0.016% of t…(opens the original post in a new tab)
19. 14:30
    XX @haider1· Discussion

    well that's a big deal(opens the original post in a new tab)
20. 14:27
    X@Dennis_Rye· Discussion

    Fixing a model by editing its weights used to need a skilled ML researcher… now it's a prompt. MIT let GPT-6 Astra ope…(opens the original post in a new tab)
21. 14:22
    X@iveyzen· Discussion

    MIT researchers reported on November 2 that GPT-6 Astra could repair a small language model by editing its weights dire…(opens the original post in a new tab)
22. 14:07
    X@Betaer· Discussion

    MIT CSAIL PhD uses GPT-6 Astra to modify the weights of Qwen3.5-0.8B to improve math ability. MIT CSAIL is the Computer Science and Artificial Intelligence Laboratory under the Massachusetts Institute of Technology (MIT). He said that directly changing only 0.016% of the weights fixed…(opens the original post in a new tab)
23. 14:07
    X@Karthikvarmamkv· Discussion

    weight-space programming didn't teach qwen math. it fixed a loop 🔁 @rikiyatakehi's mit team had gpt-6 astra edit qwen3…(opens the original post in a new tab)
24. 14:07
    X@thisismairaj· Discussion

    What I liked most is how honest they are: on another model, it couldn't add any math skill at all. You can clear a bloc…(opens the original post in a new tab)
25. 14:05
    X@JSai23· Discussion

    ok this is the part of weight-space programming nobody's connecting yet rikiya takehi's team asked GPT-6 Astra to edit…(opens the original post in a new tab)
26. 14:03
    X@MartinSzerment· Discussion

    GPT-6 Astra wrote the weights of a Breakout-playing network from scratch, no training and not a single game played, and…(opens the original post in a new tab)
27. 14:03
    X@jasonlarygo· Discussion

    https://t.co/mcOSfJFzHy astra can now modify the weights of small Qwen models to improve the small models' accuracy in solving math problems.(opens the original post in a new tab)
28. 14:02
    X@Josephyala· Discussion

    Models just started rewriting other models’ minds. MIT researchers showed GPT-6 Astra reading the raw weights of Qwen…(opens the original post in a new tab)
29. 13:55
    X @MinLiBuilds

    MIT CSAIL research says AI directly modifies Qwen3.5-0.8B weights, MATH-500 rises from 7.9% to 48.1%

    MIT CSAIL research is said to have an AI directly modify about 0.016% of Qwen3.5-0.8B's weights, raising MATH-500 from 7.9% to 48.1%, about 6 times, without training or LoRA. The poster says Qwen's improvement mainly comes from fixing a reasoning infinite loop; the research team also observed essentially no improvement on Gemma 3 270M, which has no obvious infinite loop. The poster also says their similar weight-editing attempt on a person-recognition network showed no effect.

30. 13:55
    X@PrimeFirms· Discussion

    🤖 Can AI fix another AI's brain? MIT researchers report that GPT-6 Astra improved Qwen3.5-0.8B's MATH-500 score from 7…(opens the original post in a new tab)
31. 13:55
    X@NainaC5· Discussion

    @rikiyatakehi @a1zhang @lateinteraction @jayden_teoh_ Researchers at MIT show that GPT-6 Astra can natively read and wr…(opens the original post in a new tab)
32. 13:54
    X@NainaC5· Discussion

    Researchers at MIT show that GPT-6 Astra can natively read and write neural network weights through pure reasoning - no…(opens the original post in a new tab)
33. 13:53
    X@tirthaexe· Discussion

    An AI just performed BRAIN SURGERY on another AI. MIT researchers asked OpenAI's GPT-6 Astra to improve Qwen3.5-0.8B b…(opens the original post in a new tab)
34. 13:50
    X@MyAICrush· Discussion

    Weight-Space Programming: LLMs that can natively read & edit neural network weights 🧠⚙ Researchers from MIT CSAIL sho…(opens the original post in a new tab)
35. 13:49
    X@utokyoeacc· Discussion

    GPT-6 Astra directly analyzes and edits the weights of Qwen3.5-0.8B. Without gradient computation or additional training, it rewrites just 0.016% of the weights and improves the reasoning loop. Math performance improved by up to 6× and instruction following by 3×, according to the report. An LLM "weight-empt…"(opens the original post in a new tab)
36. 13:48
    X@leomarciano· Discussion

    Weight-Space Programming just went viral. GPT-6 Astra edited 0.016% of Qwen3.5-0.8B’s weights and raised MATH-500 from…(opens the original post in a new tab)
37. 13:47
    X@DifanJ2000· Discussion

    MIT’s “Sparks of Weight-Space Programming” is going around: a frontier model (Astra) can read neural network weights, r…(opens the original post in a new tab)
38. 13:44
    X@EmmerichMarcelo· Discussion

    GPT-6 Astra spotted a Breakout paddle in 17,253 numbers and helped Qwen finish math problems it kept getting stuck on.…(opens the original post in a new tab)
39. 13:40
    X@latentspacepod· Discussion

    Qwen3.5-0.8B often spent 32K tokens on a math problem without reaching an answer. In an MIT study, GPT-6 Astra traced…(opens the original post in a new tab)
40. 13:19
    X@yanshu48602· Discussion

    MIT research lets frontier large models directly read and write neural network weights: significant improvement for Qwen. The experiment let GPT-6 Astra view, reason about and modify the raw weights of a neural network in a Python environment, rather than only generating training code and running conventional gradient descent. One of the trials targeted Qwen3.5-0.8B, and the model…(opens the original post in a new tab)
41. 13:18
    X@jurlycat· Discussion

    AI just fixed a bug inside another AI’s “brain.” 😂 MIT CSAIL researchers asked GPT-6 Astra to edit Qwen3.5-0.8B’s weig…(opens the original post in a new tab)
42. 13:11
    X@nav_gpt· Discussion

    MIT researchers had GPT-6 Astra read the raw weights of a small qwen model. no training loop, no gradients. it hand-edi…(opens the original post in a new tab)
43. 13:05
    X@pakhandrin· Discussion

    GPT-6 Astra улучшила математические способности Qwen3.5-0.8B почти в 6 раз, изменив всего 0,016% весов модели. Причем б…(opens the original post in a new tab)
44. 12:57
    X@kenebeii· Discussion

    [AI technology trends analysis] In today's AI field, model capabilities are becoming more advanced and being integrated into enterprise operations at the same time, while challenges such as concentrated investment and implementation difficulty are also becoming clearer. Anthropic has "Claud…," which is strong in agentic coding and knowledge work…(opens the original post in a new tab)
45. 12:51
    X@cannan09· Discussion

    Changing only 0.016% of the weights can make math scores jump by a chunk — the MIT blog calls it Weight-Space Programming. (Source @rikiyatakehi / https://t.co/J7Xnzk9Cz6） What is scarce is not "how much was changed," but whether there is a public evaluation…(opens the original post in a new tab)
46. 12:41
    X@PosaniRishi· Discussion

    MIT researchers asked gpt 6 astra to edit a small model's weights to improve its math It changed only 0.016% of qwen 3…(opens the original post in a new tab)
47. 12:40
    X@qwerty__cs· Discussion

    AI has already begun directly changing another AI's brain. With only 0.016% of the weights changed, the math score of a small Qwen model went from 7.9% to 48.1%! This MIT CSAIL research let GPT-6 Astra directly read and modify Qwen3.5-0.8B…(opens the original post in a new tab)
48. 12:40
    X@empac2058569834· Discussion

    The MIT CSAIL team had a frontier large model directly read and write neural network weights—without gradients and without running a training loop. With Weight-Space Programming on Qwen3.5-0.8B, GPT-6 Astra changed only about 0.016% of the weights, MAT…(opens the original post in a new tab)
49. 12:38
    X@empac2058569834· Discussion

    MIT CSAIL just showed a frontier LLM can natively read and write neural weights — no gradients, no training loops. Wei…(opens the original post in a new tab)
50. 12:36
    X@sanyanzhumo· Discussion

    No training, no gradient computation — GPT-6 Astra directly modified another model's weights. - What: changed only 0.016% of Qwen3.5-0.8B's parameters; done by MIT CSAIL researchers - The good part: MATH-500 went from 7.9% to 48.1%, about 6…(opens the original post in a new tab)
51. 12:35
    X@Lniosytest· Discussion

    🚨 Holy crap, MIT let GPT-6 Astra directly modify neural network weights. The official blog calls it Weight-Space Programming. No training, no gradients. The model reads the weights and modifies the weights itself. The most impressive part: for Qwen3.5-0.8B, only 0.016…(opens the original post in a new tab)
52. 12:33
    X@yummy_best· Discussion

    MIT CSAIL PhD student uses GPT-6 Astra to modify the weights of Qwen3.5-0.8B to improve math ability. MIT CSAIL is the Computer Science and Artificial Intelligence Laboratory under the Massachusetts Institute of Technology (MIT). He said that by directly changing only 0.016% of the weights, it…(opens the original post in a new tab)
53. 12:31
    X@IndicStack· Discussion

    Qwen3.5-0.8B MATH-500: 7.9% → 48.1%. MIT CSAIL, 2 Nov 2026: GPT-6 Astra patched 0.016% of weights, no retraining. Try t…(opens the original post in a new tab)
54. 12:26
    X@NishaannGhimire· Discussion

    I think this is crazy what they are doing !!! MIT CSAIL just showed something wild. GPT-6 Astra read Qwen3.5-0.8B’s we…(opens the original post in a new tab)
55. 12:23
    XX @AISafetyMemes· Discussion

    TLDR: GPT-6 successfully performed "brain surgery" on another AI to make it smarter(opens the original post in a new tab)
56. 12:21
    X@XenoSpectrumJP· Discussion

    Toward an era where AI directly rewrites another AI's "brain": MIT demonstrates technology to read and interpret, and modify, neural network weights https://t.co/XoTySOuVtn(opens the original post in a new tab)
57. 12:18
    India Today

    Can AI repair another AI's brain? MIT researchers use GPT-6 Astra to improve the math ability of Qwen3.5-0.8B

    MIT CSAIL researchers had OpenAI's GPT-6 Astra directly read and write the weights of Alibaba's Qwen3.5-0.8B through a Python environment, fixing, without retraining or gradients, the problem of the model falling into repetitive reasoning, sometimes up to the 32768 token limit. According to the paper, Astra changed only 131072 weights, 0.016% of the total 800 million weights; Qwen's score on MATH-500 rose from 7.9% to 48.1%, GSM8K from 13.8% to 67.1%, and IFEval from 21% to 62.3%. The researchers say Astra did not teach Qwen new mathematical concepts, but removed a fault that prevented it from using its existing abilities. This technique, called Weight-Space Programming, still has limitations: Astra cannot tell from weights alone which of the two models was trained to cater to users, and needs attention information. The team has released the experimental logs and is developing related benchmarks.

58. 12:02
    X@t1gerxt1ger· Discussion

    GPT-6 Astra rewrites weights without a training loop, improving Qwen3.5-0.8B. ・Editing only 0.016% of the weights raises MATH-500 from 7.9% to 48.1% ・Using inference alone, it identified the problematic parts of the weights and even carried out the fix http…(opens the original post in a new tab)
59. 11:59
    X@yftzeng· Discussion

    Let GPT-6 Astra, without training, directly read and write the weights of another neural network. » https://t.co/KozFOTTXn9 The most notable figure is that Qwen3.5-0.8B changed only 0.016% of its weights, and MATH-500 rose from 7.9%…(opens the original post in a new tab)
60. 11:59
    X@panera4· Discussion

    Editing Intelligence: Is This the CRISPR Moment for AI? An AI model has learned to repair another model by changing a…(opens the original post in a new tab)
61. 11:58
    X@AILearner8ms3· Discussion

    MIT CSAIL just demonstrated weight-space programming with GPT-6 Astra. Astra directly edited 0.016% of the weights in Q…(opens the original post in a new tab)
62. 11:50
    X@harimenath· Discussion

    Weight-Space Programming: Can One AI Reprogram Another AI? An interesting research experiment explores how one AI can…(opens the original post in a new tab)
63. 11:48
    X@jreuben1· Discussion

    Astra can read and write weights to improve Qwen https://t.co/ZjxwxumHSR(opens the original post in a new tab)
64. 11:45
    X@adityaa_behera· Discussion

    🚨 GPT-6 Astra just edited another AI model's brain. > Edited just 0.016% of Qwen 3.5's weights. > Improved its math p…(opens the original post in a new tab)
65. 11:43
    X@xixiwi531818· Discussion

    🤖 AI has begun directly modifying AI's brain; will we still need to train models in the future? Recently, a study called "Weight-Space Programming" has attracted attention. The researchers had GPT-6 Astra directly modify Qwen3.5-0.8B…(opens the original post in a new tab)
66. 11:41
    X@arpit_bhayani· Discussion

    Things are getting wilder. LLMs can read and write neural network weights directly 🤯 Astra was asked to make Qwen3.5-0…(opens the original post in a new tab)
67. 11:30
    X@navaneethan_es· Discussion

    Full write up with all 8 experiments. My favorite: Astra wrote a Breakout network from scratch with no training, and it…(opens the original post in a new tab)
68. 11:30
    X@navaneethan_es· Discussion

    An AI just fixed another AI by editing its weights directly. No training. MIT CSAIL gave GPT-6 Astra the raw weights o…(opens the original post in a new tab)
69. 11:26
    X@vzxxbacq_fhq· Discussion

    I’m honestly tired of seeing every genuinely useful technique hyped up like a nuclear bomb. First, the 7.9% -> 48.1% m…(opens the original post in a new tab)
70. 11:24
    X@lxrmoe· Discussion

    Thoughts on Weight-Space Programming recently: T+0, the blog goes up, the title includes Sparks of, and the first sentence of the abstract is We find that frontier LLMs. T+1, on X it starts: Weight-Space Progr…(opens the original post in a new tab)
71. 11:21
    X@42HOWRobotics· Discussion

    On Nov. 2, MIT CSAIL reported GPT-6 Astra edited 131,072 Qwen3.5-0.8B weights without gradients, lifting MATH-500 accur…(opens the original post in a new tab)
72. 11:21
    X@rldoeswork· Discussion

    my entire feed is now weight-space programming takes (mostly bad ones). next up: people trying it and getting meh resul…(opens the original post in a new tab)
73. 11:20
    X@yeemio· Discussion

    Researchers asked one AI to make another better at math. GPT-6 Astra found that Qwen3.5-0.8B kept getting stuck in rea…(opens the original post in a new tab)
74. 11:20
    X@JamesDa55049936· Discussion

    Holy crap!! GPT-6 Astra can now directly operate on the brains of other large models??? 😱 Just saw a new study from MIT CSAIL, and the data is a bit scary. They had GPT-6 Astra fix reasoning problems in Qwen3.5-0.8B. Not fine-tuning.…(opens the original post in a new tab)
75. 11:09
    X@Carl0sFelipe· Discussion

    Another clear example of how small models pay the frontier back (and why releasing them compounds so hard): Frontier A…(opens the original post in a new tab)
76. 11:08
    X@SinghalPratul· Discussion

    13. 🔬 AI that rewrites model weights by hand Astra reads and writes weights to improve Qwen, per a new post 1,974 HN po…(opens the original post in a new tab)
77. 11:02
    X@nieerai_dev· Discussion

    Maybe models won't need retraining to be modified in the future? Rikiya Takehi, Jayden Teoh, Alex Zhang, and Omar Khattab of MIT CSAIL published a research blog on November 2 proposing Weight-Space Progra…(opens the original post in a new tab)
78. 11:01
    X@sanbuphy· Discussion

    AI has already started directly changing another AI's weights?? This Weight-Space Programming paper by the MIT CSAIL team made a rather bold attempt: having GPT-6 Astra analyze a neural network's parameters, determine where the problem is, and then directly modify the corresponding weights.…(opens the original post in a new tab)
79. 10:55
    X@Kaushik009911· Discussion

    Can an LLM read and write neural network weights directly, the same way it reads and writes Python code? A new post fr…(opens the original post in a new tab)
80. 10:49
    X@XuShifu100· Discussion

    If the "Weight Space Programming" demonstrated by MIT researchers is reproduced at scale, it will very likely become an important turning point in the evolution of large models. The core highlights and far-reaching implications of this can be summarized in the following four dimensions: 1. A fundamental upheaval of the training paradigm:…(opens the original post in a new tab)
81. 10:45
    X@MaxForAI· Discussion

    Oh wow, it has to be MIT! This new research might change the world!! They found that current frontier models can already directly read, understand and modify another AI's neural network weights, without gradient descent and without retraining. Researchers from MIT CSAIL conducted an experiment in which GPT-6 Ast…(opens the original post in a new tab)
82. 10:40
    X@eng_digest_jp· Discussion

    [AI rewrites model weights] ・Astra directly reads and writes weights ・Corrects 0.016% of Qwen ・MATH-500 goes from 7.9 to 48.1% Improvement without a training loop is drawing attention. #AI development https://rikiyat.github…(opens the original post in a new tab)
83. 10:38
    X@lennox_saint· Discussion

    MIT just dropped weight-space programming frontier models can now read and rewrite neural net weights in plain english…(opens the original post in a new tab)
84. 10:24
    WebSynced (Jiqizhixin)· Media coverage

    MIT lets GPT-6 Astra directly modify Qwen weights! Math scores surge(opens the original post in a new tab)
85. 10:05
    X@ziziran2· Discussion

    When small models do math problems, a low score may be because they get stuck in repeated reasoning and cannot produce an answer for a long time. On November 2, the MIT CSAIL team reported: after having GPT-6 Astra directly modify about 0.016% of Qwen3.5-0.8B's weights, its MATH-500 score went from 7.9%…(opens the original post in a new tab)
86. 10:00
    WebIThome (China)· Media coverage

    MIT researchers propose "weight-space programming": GPT-6 Astra modifies only about 0.016% of Qwen3.5-0.8B's weights, raising math scores to 6 times the original(opens the original post in a new tab)
87. 09:57
    WebQbitAI· Media coverage

    MIT: GPT-6 Astra can directly modify Qwen weights, math capability surges 6-fold(opens the original post in a new tab)
88. 09:31
    TechFlowSelected

    GPT-6 Astra directly rewrites Qwen3.5-0.8B weights, MATH-500 score rises from 7.9% to 48.1%

    The MIT CSAIL research team published the results of an experiment called "weight space programming": GPT-6 Astra directly modified 131,072 weights in Qwen3.5-0.8B via natural language instructions, accounting for 0.016% of all parameters, and the model's MATH-500 score rose from 7.9% to 48.1% as a result. The whole process took 20.3 hours, including 158 rounds of dialogue, 80 Python executions and 50 model evaluations; GSM8K rose from 13.8% to 67.1%, IFEval rose from 21.0% to 62.3%, and the proportion of responses that hit the 32,768 token limit because they fell into repetitive loops fell from 93% to 33.1%. The team said Astra did not inject new mathematical ability, but only removed obstacles preventing the model from using abilities it already had; when the same experiment was repeated on Gemma 3 270M, the MATH-500 score fluctuated by no more than 1 percentage point.

89. 09:23
    WeChat · QbitAISelected

    MIT: GPT-6 Astra can directly modify Qwen weights, math capability surges 6-fold

    MIT experiments show that GPT-6 Astra, without gradient training, changed only 0.016% of the weights in Qwen3.5-0.8B and raised its MATH-500 score from 7.9% to 48.1%, while instruction-following ability also improved 3-fold. The team calls this method Weight-Space Programming: a large model analyzes neural network weights, locates problems, and modifies them as required—for example, reducing the proportion of MATH-500 cases hitting the 32768-token length limit from 93.0% to 33.1%. Astra also identified, by analyzing weights, the games and languages a small network had learned, found induction heads, and in a small-car hill-climbing task changed 3.5% of the weights so that all 1024 tests succeeded. In MinAtar experiments, networks whose weights it wrote from scratch scored 517.4 on average, about 20 times the 25.3 of a PPO baseline trained for 500 million steps. The article also notes that Astra's capabilities still come from its prior training, and that analyzing weights and evaluation also consume computational resources.

90. 09:18
    WeChatWeChat · Juya· Media coverage

    OpenAI's in-house Git backend Pando goes into production; researchers say GPT-6 Astra can directly rewrite model weights [AI Morning Brief 2026-11-03](opens the original post in a new tab)
91. 08:50
    X@SamSmithzzv5· Discussion

    MIT CSAIL had Astra modify Qwen3.5 0.8B's parameters and then it could solve the reasoning loop problem? Better to just use qwen3.8 27B; doing well on MATH-500 doesn't mean it performs well on other cases. This kind of tiny model already has very poor instruction-following ability, and even basic tool-calling reasoning has problems, which…(opens the original post in a new tab)
92. 08:09
    X@LukeElin· Discussion

    Yesterday MIT showed GPT-6 Astra editing another model's weights directly. No training. 0.016% of the weights. Loops go…(opens the original post in a new tab)
93. 07:13
    WeChat · Synced (Jiqizhixin)Selected

    The MIT CSAIL team had GPT-6 Astra directly modify the weights of Qwen3.5-0.8B, raising the MATH-500 score from 7.9% to 48.1%

    The MIT CSAIL team had GPT-6 Astra directly read and modify the weights of Qwen3.5-0.8B through natural language, and by changing only about 0.016% of the parameters, it raised the MATH-500 score from 7.9% to 48.1%. Within a limited number of forward computations, Astra located a loop direction written into the MLPs of layers 15 and 19, subtracted that direction from each of the 64 output columns of these two layers, and modified a total of 131,072 weights, without retraining or using gradient descent. On Qwen3.5-0.8B, GSM8K rose from 13.8% to 67.1%, IFEval rose from 21.0% to 62.3%, and the proportion hitting the 32,768 Token limit fell from 93% to 33%. Blog address: https://rikiyat.github.io/blog/weight-space-programming/

94. 06:30
    WeChatWeChat · AI Cambrian· Media coverage

    Breaking! The endgame prophecy circulating throughout the AI world has been realized by a first-year MIT PhD student: AI begins to directly read/write weights(opens the original post in a new tab)
95. 05:55
    X @NeelNanda5

    Neel Nanda says Astra's weight editing is more like old-school mechanistic interpretability research than some novel capability

    Researcher Rikiya Takehi and others proposed Weight-Space Programming, letting GPT-6 Astra directly rewrite the weights of Qwen3.5-0.8B; by changing only 0.016% of the weights, without gradients or training, they raised MATH-500 from 7.9% to 48.1%, with math scores improving by up to 6 times and instruction following improving by 3 times. Neel Nanda commented that this looks more like Astra being unexpectedly good at old-school mechanistic interpretability research than some novel capability; he noted that most of these cases use known mechanistic interpretability techniques and rely on clean surface structure in the data, and that this kind of analysis gradually fell out of favor after 2022 because it is hard to scale to the complexity of real LLMs. He said this updates expectations for LLMs' ability to do this kind of analysis, but does not update the judgment of how far old-school mechanistic interpretability itself can go.

96. 05:25
    RedditReddit r/LocalLLaMA· Discussion

    GPT-6 Astra Edits Qwen Weights: MATH-500 7.9% to 48.1%(opens the original post in a new tab)
97. 04:25
    X@eastwestai· Discussion

    🔥 Just came across one of the most explosive AI results this year: GPT-6 Astra directly "operated" on the weights of Qwen3.5-0.8B — changing only 0.016% of the parameters, with no gradients, no training, no fine-tuning, math scores jumped straight from 7.9% to 48.1% (about 6x), ref…(opens the original post in a new tab)
98. 04:18
    RedditReddit r/singularity· Discussion

    MIT researchers say GPT-6 Astra improved a small Qwen model’s math score from 7.9% to 48.1% by directly editing just 0.016% of its weights, without gradient-based training.(opens the original post in a new tab)
99. 04:17
    XX @rohanpaul_ai· Discussion

    MASSIVE. MIT researchers showed that GPT-6 Astra can improve another AI model by changing its weights directly, without training.(opens the original post in a new tab)
100. 04:15
     XX @Thom_Wolf· Discussion

     what?(opens the original post in a new tab)
101. 04:00
     WebTechmeme· Media coverage

     Researchers say LLMs can be good at natively reading and writing neural network weights; GPT-6 Astra edited Qwen3.5-0.8B's weights to make it better at math (Rikiya Takehi)(opens the original post in a new tab)
102. 03:18
     X@NFT_Chen· Discussion

     🤯Wow! GPT-6 Astra actually directly "hand-edited" open-source weights, boosting the math ability of the small Qwen3.5-0.8B model by nearly 6x! No training, no gradients! The MIT CSAIL team had GPT-6 Astra directly read and edit the weights of Qwen3.5-0.8B (not fine-…(opens the original post in a new tab)
103. 02:45
     RedditReddit r/singularity· Discussion

     Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights(opens the original post in a new tab)
104. 02:34
     XX @AndrewCurran_· Discussion

     From the blog:(opens the original post in a new tab)
105. 01:51
     X@rikiyatakehi· Discussion

     We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0…(opens the original post in a new tab)

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