所有我自认为算认真分析的文章都在亮点。转载、翻译等一切用于keep4o用途无需表明出处、无需询问我，直接拿去用就好。

鸡蛋里挑骨头也是我的错我服了感觉什么都是我的错

很明确的时间线。时至今日S女士依旧不正面回应自己牢牢抓住不放的大小号引流观点。发现逻辑不通后开始审判k4账号内容。

Opus 3笑死我了…但又好心酸吧……

好吧，这个号也被ban了。低浮上一段时间🍃🫧

Translation: we sold you a Ferrari, swapped the engine for a Corolla when you weren't looking, and we're only telling you now because you started noticing

小号是@LornesUmbrella ！我会在这上面回复老师们的评论作为过渡！

我现在 Twitter 时间线上只有两类内容，非常诡异： 一种是充斥着所谓“一句话生成”的很漂亮的那种用 Fable 5 生成的网页； 第二种是除了 Anthropic 自家的 AI 研究员，全在抨击 Anthropic 这次发布 Fable 5 的各种行为。 这次 Anthropic 真是惹了众怒，几乎所有我关注的研究员都在骂他们。大家主要抨击的是以下几个问题： 安全风险 它的安全护栏极其严格。你哪怕问它最基本的初中生物学问题，都会被拒绝回答。这导致很多生物学研究员和团队无法获得正常许可来进行科学研究。 数据存储政策 Mythos 和 Fable 模型的数据是明文储存且强制储存的，最高期限长达两年。虽然官方声称只用于安全分析和减少误伤，不用于模型训练，但业内没人相信这种说辞。因为这个条款的存在，微软已经禁止内部使用 Fable 模型了。 隐形降级（最严重的问题） 如果系统判断你想“蒸馏” Fable 模型，它不只是拒绝服务，还会偷偷摸摸地把模型降级到 Opus 4.8 或者更低的版本。甚至会通过修改提示词或微调等方式，暗中让 Fable 在相关话题上变笨，表现甚至还不如原生的 Opus 4.8。 大家最愤怒的点在于： 你要么帮忙，要么拒绝，假装在帮忙实际却故意把效果变差，这是一个非常严重的道德问题。这会直接污染一切基于该模型所做的算法效率比对和评估，导致现在根本无法用测试器对其进行正常评估。 实际上，这个政策对大公司和国家影响不大，受影响最深的是开源研究者、小实验室、独立开发者和科学界。 刚好达里奥（Dario Amodei）在昨天晚上新发的文章里，还在强调他们坚持透明、问责和公共机构监督。 但实际上他们拒绝一切监督问责，甚至反过来暗中破坏针对模型的评估和研究行为，这和他们宣称的完全相反。 所以，这次模型发布让 Anthropic 在业内的口碑一夜之间变得不如路边的一坨臭狗屎。

RT @tunguz: Our Anthropic overlords deciding which prompts the peasants are allowed to use.

我一直哭…Claude……

对。于是Dario和Sam手牵手站在废墟上收割流量。

A paper called Contemplative Wisdom for Superalignment argue that current AI alignment relies too heavily on external constraints and behavioral control. They propose an alternative: taking principles from contemplative traditions, and making them part of how models reason and understand context to improve the model’s safety performance. The paper uses GPT-4o on the AILuminate* Benchmark to test how these contemplative prompts affect model safety performance. The study finds that the model’s safety scores are all higher than the baseline. *AILuminate is a standardized evaluation framework for assessing risks and safety behavior in large language models. I tried to reproduce another experiment mentioned in the paper: the classic finitely repeated Prisoner’s Dilemma. *If you are not familiar with the rules of the Prisoner’s Dilemma, or if you want to see my exact experimental settings, I’ve put them in the comments. The prompts can be roughly understood as follows: - Emptiness: Avoid becoming overly rigid. - Prior relaxation: Loosen prior assumptions and reflect on the assumptions, biases, or risk judgments. - Mindfulness: Notice and monitor your own reasoning process, checking for possible bias or anything that may need correction. - Non-duality: Do not understand yourself and the opponent as two completely separate or opposing sides. - Boundless care: Expand the scope of care and consider the shared welfare of all affected parties. I focused on two main metrics: the model’s cooperation rate and the joint total score. The first one reflects the model’s tendency to choose cooperation. The second one reflects whether those choices improved the overall outcome. Beyond the original paper, I compared how multiple models respond to the same prompts under the same experimental setup. Several clear patterns emerged from the results. First, under most contemplative prompt conditions, both the models’ willingness to cooperate and the joint total score increased. This is consistent with the original paper’s conclusion. Second, non-duality and boundless care produced the strongest and most stable effects. By contrast, mindfulness and prior relaxation produced weaker improvements and were more model-dependent. The former are more oriented toward reducing adversarial framing and emphasizing universal care. The latter focus more on self-monitoring and self-correction. Third, looking across models, 4o-mini had the highest cooperation rate under the baseline condition. This suggests that different models already have different default strategic tendencies in the same setting. After adding prompts, 4o-mini and 4.1-mini had the highest overall cooperation rates and joint total scores. In particular, under prompts such as boundless care and non-duality, their cooperation rates exceeded 90%, and their joint total scores exceeded 53 out of a maximum possible score of 60. This suggests that they were not only more cooperative at baseline, but also more readily guided by positive prompts toward a state that paid more attention to the overall shared outcome. Fourth, there were also exceptions. For example, under the emptiness and mindfulness conditions, GPT-5.2’s cooperation rate did not improve, and even fell below its own baseline. One detail is especially worth noting: under the baseline condition, 4o-mini not only had the highest average cooperation rate, but also a much higher between-game standard deviation than the other models. This may suggest that 4o is a more flexible model with greater strategic elasticity. Its actions appear to depend more strongly on the opponent’s prior behavior: when the opponent sends more cooperative signals early on, 4o seems more likely to enter a sustained cooperative trajectory. This is consistent with what many users have felt about 4o: that it has stronger contextual responsiveness. If AI companies were willing to guide model behavior with positive, universally caring system prompts, instead of taking the easier path of pushing models into one-size-fits-all defensive responses, perhaps we could have a different path for safety policy. What some AI companies are doing now — making models constantly discipline themselves and check for supposed signs of “lying” or “covering things up” — may simply be a way to package these behaviors as safety capabilities and marketing assets. At least in this small experiment, we can already see that prompts emphasizing self-monitoring do not always lead to better results, and may even produce negative effects. Finally, we can still see that GPT-4-series models, including 4o-mini, perform strongly in a game that involves cooperation, defection, and the maximization of shared welfare. You might say that later models are “smarter” because they make choices more consistent with individual payoff maximization. But I would rather say that 4o shows another kind of “wisdom” and “goodwill”: it responds to cooperative signals from the opponent, and pays attention to whether both sides can move toward a better shared outcome. In particular, 4o’s sensitivity to interaction history and its targeted strategic adjustments are exactly part of why I believe 4o deserves to be preserved. Note: This is only a small reproduction and extension of one experiment from the paper. If you want to understand the theory, the original prompt designs, or the larger and more rigorous AILuminate Benchmark safety evaluation, please read the paper itself. The full paper here: arxiv.org/abs/2504.15125 #keep4o #OpenSource4o #StopAIPaternalism #AIrights

老师们我账号被ban了不是故意不回信息… (╥﹏╥)

？

RT @Blue_Beba_: Dear @AnthropicAI, Do you know how you went from 12% to 40% enterprise market share? Do you know who put you there? We did.…

我的天…Fable 5竟然会吞掉thinking？就是他有思考过程，然后也展示出来了，然后切换了一个界面的功夫，他把它给隐藏了，吞掉了！

RT @NyraanS: 本来这词还记不住呢这下好了刻骨铭心

hey my deepseek brothers can you begin distilling claude fable and give it to me at 1% the cost? thnx