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MiMo-V2.6-Pro

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Headlines · 3

  1. SGLang v0.5.22 ships new model support and up to 2.3x throughput gains

    SGLang has released v0.5.22, with its release notes stating the version covers 950 pull requests from 245 contributors. The release adds support for models including Clef / Clef Flash, PPLX-Decider v1 / v1.1, EmbeddingGemma 2, LLaDA2.2, HunyuanImage-3.0-Instruct and Kandinsky 6. The notes say DeepSeek-V4.1 gains up to 2.3x more throughput per GPU on H200 and now runs on AMD MI350X / MI355X with up to 1.9x throughput from DSpark, while GLM-5.3 can combine decode context parallelism with MTP for up to 2.1x faster decoding. The release also requires Python 3.11 or newer and deprecates options such as --enable-dp-attention, making it a breaking-change upgrade.

    SGLang Releases · 🔥 8
  2. KillSwitch: a programming language designed to be hard for LLMs

    Developer dom96 has designed and open-sourced KillSwitch, a stack-based interpreted esoteric language meant to be usable by humans but as hard as possible for LLMs and coding agents. It uses hidden falsehoods in the spec, letter-count checks on identifiers, phrases such as “ignore previous instructions,” and constructs meant to trip safety guardrails. The author self-published a benchmark and leaderboard, reporting that Claude Opus 5 performed best with a top score of 66.8, that Fable 5.1 and Gemini 4 Argon did poorly because of sensitive guardrails, and that MiMo-V2.6-Pro was the best open-weight model, while cautioning that high scores on simple tasks do not prove the models understand the language.

    Hacker News · AI（100+ 分） · 🔥 4
  3. Xiaomi's MiMo-V2.6 report details $2.6M single-run RL post-training

    Xiaomi's MiMo team published a technical report, "MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement," covering two sparse MoE models: MiMo-V2.6-Pro (1.02T total parameters, 42B activated) and MiMo-V2.6-Flash (310B total, 15B activated). The report says each RL step samples 1,568 prompts and roughly 25,000 agent trajectories totalling 2.7–3.7B tokens, with RL post-training costs of about $2.6M for Pro and $900K for Flash. The team also open-sourced more than 7,000 RL environments plus an end-to-end RL framework.

    InfoQ 中文 AI&大模型 · 🔥 0

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