Problem Solver, Machine Learner, RT means fuck yeah endorsing
Shanghai
Joined April 2009
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而且新药的研发成本其实也主要集中在后几个流程，ai 能加速的比例很低
Dario路演视频里说，癌症、心脏病这些「不到 10 年就会成为历史」。
但一周前，Vertex 执行董事长刚把同类说法骂成「完全是胡说」。他们用 1 万种化合物测了约 50 家 AI 公司，没有一家做出有效药物。
药企的逻辑很简单：新药研发 10 到 12 年，AI 只能加速最前面的分子筛选，后面的动物实验、三期临床、监管审批一步都省不掉。
更何况，约四分之一的中后期失败源于安全性问题，AI 现在还预测不了。
163.com/dy/article/LAV6R8RK0…
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Google 的这个 Android Bench 2.0 一看小肥鱼的 api cost 就知道根本没全力而为。小肥鱼 spam token 的能力可不是盖的。另外从这个榜里得到的信息就是 Terra max 档位不值得用，更贵能力更差。
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推测这个问题和 benchmark 如何判定任务是否成功的有很大关系，所有这种通过率测试，完全找不到北和小的判定出错都是不通过。榜单上这些模型，就算成功率跌了，也就是一定比例的判定没过，任务本体其实是大差不差的。很多测试的 verifier 其实写的也不是很好。
Nice DeepSWE score! Does it survive a lowercase filename?
Introducing MaxxBench: 89 DeepSWE tasks with small changes to the instructions or starting code. Same engineering problem, slightly different requirement.
Models clustered around 74% on DeepSWE end up 23 pp apart.
Good at coding, or good at DeepSWE? 🧵
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Chen Zhang retweeted
just in case anyone doesn't know what "Western Values" are baked in, some quick demonstrations from their dataset.
they baked in CIA propaganda and want the model to ignore anything that's not from CIA sources.
it's actually a comprehensive collection of CIA talking points🤡
Introducing Arete-27B: Qwen3.8-27B, post-trained to reinforce Western values.
Chinese open models are currently the strongest available, but they ship with state-aligned behavior in the weights. We built an 803-prompt eval across seven value categories, including freedom of expression, democracy, and privacy, and measured how often a model adopts China’s party line.
After fine-tuning on roughly 53 million tokens, Arete-27B's propaganda rate fell from 22% to 1%, in line with the Western models we tested. Its Hermes Bench score shows no significant performance tradeoffs.
The values in an open model are one fine-tune away.
Our eval, weights, and training data are available now along with the full blog post: nousresearch.com/arete
难怪 K3.1 Pro 现在给的量多了，原来了优化了注意力层
训练数据很重要
We’re introducing Hydrogen-1.0, our first open-weight model for scientific research. We post-trained GLM-5.3 (@Zai_org) on our internal scientific data, lab records, and discovery trajectories.
It scores 38.3% on FrontierScience-Research vs GPT-6-Astra’s 33.3% and scores 93.0% on GPQA-Diamonds.
We believe science should be open, collaborative, and democratized, rather than proprietary. Only open weights let researchers inspect, adapt and build on the unprecedented speed of AI growth. There are many recent models named by chemical elements, but we believe Hydrogen-1.0 is the first aiming towards and open scientific superintelligence for discovery. 🧵
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Chen Zhang retweeted
Same recipe
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math.
It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training.
We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵
Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math.
rikiyat.github.io/blog/weight-sp…
github.com/RikiyaT/lm_und…
huggingface.co/Qwen/Qwen3.5-0…
huggingface.co/RikiyaT/Qwen3.…
