"Mythos Preview scrambled the global cybersecurity landscape. But its broader significance is that it proves beyond doubt that AI models are now tools of global and national strategic consequence." 


  "The government should have the power to block or deter deployment of the model if it is determined, in light of third-party assessment, to present unacceptable risks. This power must be scoped to the above four specific risks and there must be protective measures against political favoritism or arbitrary decisions" 

A third-party demonstrated that it was possible to jailbreak the safety measures of Fable to access the raw Mythos abilities. Abilities which Anthropic say are too dangerous for the public.

Edit. From David Sacks:

  — A highly credible trusted partner of both Anthropic and the USG who was testing Fable came forward with a jailbreak of those guardrails. The Admin asked Dario to fix the jailbreak or de-deploy the model. Dario refused.

   — In their blog post, Anthropic defended its decision by saying the jailbreak isn’t serious. That is not what the trusted partner and the USG believe; nor is that kind of minimizing language consistent with Anthropic’s brand as the AI safety company. It’s difficult to fathom how they could claim a jailbreak allowing operability of a cyber weapon could be defined as not “serious".

Some tasks benefit from this approach more than others. There was a paper from google on a version they made which was very similar and achieved SOTA then on planning and pathfinding benchmarks.

edit:

Mind Evolution paper https://deepmind.google/research/publications/122391/

(That was a month after I published llm-consortium :) https://xcancel.com/karpathy/status/1870692546969735361

  llm consortium save cns-glm -m glm-5.2 -n 5 --arbiter mercury-2 --judging-method rank

  llm consortium save cns-kimi -m k2.6 -n 5 --arbiter mercury-2 --judging-method rank

  llm consortium save cns-meta-glm-kimi -m cns-glm -m cns-kimi --arbiter mercury-2 --judging-method synthesis

...

Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user."

It's the same exact speed as opus >=4.5, sonnet 4.5, and twice the speed of opus <=4.1

It must have about the same active parameters, or else its a larger model running in turbo mode (smaller batches) and being heavily subsidized for some reason. But given most of the benchmarks are within 5% I doubt it is a much larger model. Most perplexing.

                          Mythos 5 Fable 5 MythosPrev Opus 4.8 GPT-5.5 Gemini 3.1 Pro
  SWE-bench Pro             80.3       80        77.8       69.2      58.6       54.2
  SWE-bench Ver             95.5       95        93.9       88.6       -         80.6
  Terminal-Bench            88.0      84.3        -         82.7      83.4         -
  BrowseComp (Single-Agent) 88.0       -        87.9       84.3      84.4       85.9
  BrowseComp (Multi-Agent)  93.3       -          -         88.5       -           -
  HLE (No tools)            59.0      -       56.8      49.8      41.4        44.4
  HLE (Tools)                64.5      -        64.7     57.9      52.2       51.4
  CharXiv Reasoning (No tools) 88.9       -         86.2       80.5       -         -
  CharXiv Reasoning (Tools)    93.5       -         92.5      89.9      -         -
  BioMystery Bench (Human)     83.9       -       82.6     80.4       -         -
  BioMystery Bench (Hard)    46.1       -         29.6     40.0       -         -
  OSWorld-Verified          85.0      85.0       85.4       83.4      78.7      76.2*
  CritPt                     28.6       -       20.9       27.1      17.7       -
  ArxivMath                  78.5      68.7       71.8       71.5      64.0       -

Edit: Also in the system card... "we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design).

...

Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user."

Anthropic also confirmed they will not release Mythos, only a "Mythos-class" model, whatever that means.

This was inspired by a karpathy tweet [0] and the prototype created using another tool of mine: The LLM Plugin Generator plugin (essentially a curated collection of plugins for simonws llm cli as a few-shot prompt)

The llm-model-gateway companion plugin lets you serve models from the LLM cli as a an openai API. This allows you to use saved consortiums in your various clients as if they where a regular model. Bringing massive parallel reasoning to any workflow.

It occured to me at some time that an collection of parallel LLMs was not really a consortium. A consortium is a group of organizations. A group of groups. To rectify this I added for actual consortiums, where each member of an llm-consortium can itself be a consortium of models. e.g.

llm consortium save cns-glm-n3 -m glm-5.1 -n 3 --arbiter mercury-2

llm consortium save cns-k2-n3 -m kimi-k2.6:3 --arbiter mercury-2

llm consortium save cns-meta-glm-k2 -m cns-k2-n3 -m cns-glm-n3 --arbiter cns-k2-n3

Yes, even the arbiter/judge can be comprised of a consortium of models, bringing parallel reasoning to the task of judging parallel reasoning chains.

Consortiums can also now contain groups of specialists. These custom user-defined expert characters address the prompt from a different perspective. And a Westworld style Attribute matrix can be randomized to inject some more entropy into the process.

[0]https://xcancel.com/karpathy/status/1870692546969735361

Some other llm plugins I vibe coded:

classifai generates labels with approximate confidence derived from logprobs

llm-alias-options saves inference parameters such as reasoning effort with a model alias. (good for setting the provider in openrouter or creating a consortium of high temperature models)

llm-prompt-json adds a --json flag to return the llm logs object (good for getting conversion_id, or reasoning output in scripts)

llm-jina adds support for all jina AI specialised models and tools like web fetching, embedding and reranking.

https://news.ycombinator.com/item?id=48446348

  "there has been a direct correlation between the quality of the pelicans produced and the general usefulness of the models ...
 
  Today, even that loose connection to utility has been broken..."