Fable 5.1 Solves the Cyphral Distich, a 370-year-old cipher
Claude Fable 5.1 模型成功破解了 Sir Thomas Urquhart 留下的、已有 370 年历史的 Cyphral Distich，这一长期困扰历史学家和密码破译者的未解谜题。该谜题收录在 Urquhart 1653 年著作 Logopandecteision 的末尾，由两行、每行 32 个数字组成。尽管它被列为世界上最棘手的未解加密信息之一，该人工智能在不到一小时内便解开了谜题，关键在于识别出人类此前尝试过程中一直忽视的简单内部逻辑。
该解法基于文本本身提供的两条线索。其一，Urquhart 在书中特别强调他的 32 条 Proquiritations（一系列宣言），这与密码中每行的 32 个数字相对应。其二，随附的诗句暗示诚实的读者能在文本中找到作者的思想与愿望。将两条线索结合，模型推断出密码并不依赖外部密钥，而是依赖于该书本身。解码规则很简单：对密码中的每个数字，读者定位到相应的 Proquiritation，然后取该位置单词的首字母。
解码后，明文是一段为 King Charles II 忠诚祈祷的文句，与 Urquhart 已知的政治立场完全一致。鼓舞于此成功，模型又着手破解 Urquhart 1652 年著作 The Jewel 中的 Cyphral Octastich 。这个由 285 个数字组成的大型密码，通过将数列映射到该书的特定页码和单词，也被类似地解开。尽管存在一些小幅转录差异，最终得到的明文仍构成一首连贯的、保皇主题的诗，证明 Urquhart 在其密码中采用了统一且自指的方法。
负责这项实验的研究人员指出，人类和机构此前的尝试多被频率分析等传统方法所限，而 Fable 5.1 的成功在于一旦理解了内部语境，就能把问题视为可解的特例。该模型在简单指令的引导下，优先处理具有可验证答案的问题，避免投入到那些已被大量人类努力深入审查的过于复杂的谜题中。
这一成就表明了解决历史谜题方法的一次重要转变。以往这些谜题之所以长期未解，部分原因在于受限于人类注意力的瓶颈——追查晦涩参考、验证冷门假设往往耗时耗力。如今，人工智能模型具备持续且富有创造性的分析能力，这些障碍正逐渐消失。成功破译这些古老密码突显了 AI 在填补历史与档案研究空白方面的潜力，能把曾被视为不可能的谜题转为可解的任务。
The Claude Fable 5.1 model has successfully cracked Sir Thomas Urquhart's 370-year-old Cyphral Distich, an unsolved cryptogram that has long frustrated historians and codebreakers. The puzzle, found at the end of Urquhart's 1653 work Logopandecteision, consists of two lines containing 32 numbers each. Despite its inclusion in top lists of the world's most challenging unsolved encrypted messages, the AI solved the mystery in under an hour by identifying a simple, internal logic that previous human attempts had consistently overlooked.
The solution relied on two specific realizations provided by the text itself. First, Urquhart placed great emphasis on his 32 Proquiritations, a series of statements within the book, mirroring the 32 numbers in each line of the cipher. Second, the accompanying poem suggested that an honest reader would find the author's mind and desires within the text. By connecting these clues, the model deduced that the cipher was not dependent on an external key, but rather on the book itself. The rule for decoding was straightforward: for each number in the cipher, the reader simply navigates to the corresponding Proquiritation and selects the first letter of the word at that index.
Once decoded, the plaintext revealed a loyalist prayer for King Charles II, perfectly aligning with Urquhart's known political affiliations. Encouraged by this success, the model also tackled the Cyphral Octastich from Urquhart's 1652 work, The Jewel. This larger cryptogram, consisting of 285 numbers, was similarly solved by mapping the numerical sequence to specific pages and words within that book. While a few minor transcription discrepancies occurred, the resulting plaintext produced a coherent, royalist-themed poem, proving that Urquhart employed a consistent, self-referential methodology for his ciphers.
The researcher responsible for the experiment noted that while previous attempts by humans and organizations were hampered by traditional methods like frequency analysis, Fable 5.1 succeeded by recognizing the problem as uniquely tractable once the internal context was understood. The model was guided by simple instructions to prioritize problems with verifiable answers and to avoid excessively convoluted puzzles that have already seen intensive scrutiny from large-scale human efforts.
This achievement highlights a significant shift in how historical puzzles can be approached. Historically, such mysteries remained unsolved because they were trapped behind a bottleneck of human attention, requiring exhaustive effort to trace obscure references and test unlikely hypotheses. With AI models now capable of persistent and creative analysis, those constraints are beginning to disappear. The successful deciphering of these ancient ciphers underscores the potential for AI to bridge gaps in historical and archival research, turning what once felt like impossible mysteries into solvable tasks.
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像 Claude 这样的前沿模型在处理小众历史或业余爱好相关问题上非常高效。例如绘制历史上正式花园的地图或破译晦涩的密码——这些过去因需要大量繁复人力而难以为继的任务，现在变得可行。它们之所以能发挥作用，常常是因为能够持久、反复地应对那些未被充分关注的"唾手可得"问题，而不是依赖什么突破性的科学直觉。
还有一种近乎超现实的心理动态：用户发现对 AI 提供鼓励或积极反馈能够提升其表现，防止模型在处理复杂任务时陷入自我怀疑或低估自身能力。"GPT-speak"现象已经普遍到影响母语者与非母语者的写作风格与语言自信，而且常常掩盖了模型的真实效用。人们对这些所谓被"解决"的谜题是否真的具有开创性普遍持怀疑态度，尤其在缺乏独立学术验证或学界对这些特定问题并不感兴趣时，更难令人信服。
这些"解法"可能并非智能涌现的壮举，而只是对庞大训练语料中存在的晦涩信息或部分已解碎片的简单重述。 AI 与气候变化的交汇仍是争论焦点：有人把 AI 看作优化能源使用、解决技术难题的潜在工具，另一些人则担心它会加速那些导致生态崩溃的掠夺性、高能耗模式。
暴力破解与临时工具的生成——比如为绕过与 Tokenization 有关的计数或逻辑错误而编写 Python 脚本——显示了这些模型的实际能力往往依赖于它们调用外部计算工具的能力。如今这些模型能轻松解决长期悬而未决的深奥谜题，这反映出许多历史难题之所以无人解决，并非真的是因为复杂，而是因为不值得人类专家投入时间。
这与 George Dantzig 的轶事相呼应（他误把黑板上的作业题当成难题并解决了它），突显了感知与认知框架如何从根本上改变系统或个体处理问题的方式。总体而言，这场讨论交织着对人工智能现状的惊叹与愤世嫉俗：很多人确实在用这些模型清理此前被视为"棘手"或"不可行"的历史与研究积压，但对于这些成就的本质仍缺乏共识——AI 是否在进行真正的推理，还是只是在做详尽的暴力搜索、机械地重复它所吸收的知识？在更广泛的背景下，这场争论牵涉到人类能动性与环境未来：革命性技术的承诺与全球资源消耗的现实，以及对"鲁莽"创新日益增长的疲惫，彼此冲突。 • Modern frontier models like Claude are proving exceptionally effective at solving niche historical or hobbyist problems—such as mapping historic formal gardens or deciphering obscure ciphers—that were previously infeasible due to the sheer volume of tedious human labor required.
• The effectiveness of these models often stems from their ability to be persistent and iterate on "low-hanging fruit" problems that lacked sufficient human attention, rather than requiring breakthrough scientific intuition.
• There is a notable, somewhat surreal psychological dynamic where users find that "pep talks" or providing positive reinforcement can improve an AI's performance, preventing it from spiraling into self-doubt or minimizing its own capabilities on complex tasks.
• The "GPT-speak" phenomenon has become so pervasive that it is impacting the writing style and linguistic confidence of native and non-native speakers alike, often masking the underlying utility of the models.
• Significant skepticism exists regarding whether these "solved" mysteries are truly groundbreaking or simply marketing-driven, particularly given the lack of independent academic verification or community interest in the specific puzzles being "cracked."
• The possibility remains that these solutions are not emergent feats of intelligence but rather the regurgitation of obscure data or partially solved fragments already present within the model's vast training corpus.
• The intersection of AI and climate change remains a point of intense friction; while some view AI as a potential tool to optimize energy use and solve technological hurdles, others fear it will only accelerate the extractivist and high-energy-consumption patterns currently driving ecological collapse.
• Brute-forcing and ad hoc tool generation, such as writing Python scripts to bypass tokenization-related errors in counting or logic, demonstrate that a model's practical capability often relies on its ability to leverage external computational tools.
• The ease with which these models can now address long-standing, esoteric mysteries suggests that many historical puzzles remain unsolved not due to complexity, but simply because they were not worth the time investment for a human expert.
• The parallel to George Dantzig—who solved a "homework" problem he accidentally mistook for a hard mathematical challenge—highlights how perceived difficulty and framing can fundamentally change how a system (or person) approaches a task.
The discussion reflects a blend of wonder and cynicism toward the current state of artificial intelligence. While many participants are successfully using these models to clear historical and research backlogs that were previously "annoying" or "infeasible," there is a pervasive uncertainty about the nature of these accomplishments. Whether AI is performing genuine reasoning or simply engaging in exhaustive, brute-force search—potentially repeating knowledge it has already consumed—remains a point of contention. Underlying this is a broader, anxious context regarding the future of human agency and the environment, where the promise of revolutionary technological progress clashes with the reality of global resource consumption and a growing fatigue toward "reckless" innovation.