On September 22, Xiaomi's MiMo-V2.6-Pro was reported to have taken the top spot among open-weight models on evaluation benchmarks. Last time we covered the release of MiMo-V2.6-Flash-RL from the same series; this time we have a roundup of news that raises the bar for open model research another notch: the evaluation details of the Pro model, a 9B distilled model based on Qwen3.5-9B, and even an attempt to speed up existing models "after the fact."
Xiaomi's MiMo-V2.6-Pro Tops the Open-Weight Charts — One RL Run, About $2.6M and 130 Hours (Updated from Last Time)
According to a Latent Space roundup, MiMo-V2.6-Pro is a MoE model with 1.02 trillion total parameters and 42B active, and it reportedly scored 46 on the Intelligence Index from the evaluation organization Artificial Analysis, debuting at number one among open-weight models. API pricing sits in the low range at $0.435 per million input tokens and $0.87 per million output tokens, and the license is MIT. The series also includes MiMo-V2.6-Pro-UltraSpeed, which raises output speed by up to 20x while preserving quality.
What deserves attention is the RL (reinforcement learning) scaling approach. According to reports, the single RL training run behind these results took 130 hours, 75 billion tokens, and about $2.6 million (the headline describes it as a $3-million-scale effort) — orders of magnitude cheaper than the giant-scale investments in pretraining, yet reaching toward near-frontier performance. Training is built on JAX and TPUs, and some point out that scaling amounts to "little more than a config change."
Xiaomi has already released the RL environment code and training recipe, and plans to release roughly 7,000 RL environments as well (a dataset of more than 7,000 tasks remains unreleased). Combined with the unusual transparency we covered last time — broadcasting the final RL training run live — one interesting observation is that high-quality open RL environments may carry the same strategic importance that pretraining corpora held in the previous cycle.
MiMo-V2.6-Distill-Qwen-9B, Built on Qwen3.5-9B — Agent Performance Jumps at the 9B Scale
Also released was MiMo-V2.6-Distill-Qwen-9B, a 9B agent model created by supervised fine-tuning Qwen3.5-9B on MiMo-generated data. It has climbed the trending charts on Hugging Face and is also drawing attention on Reddit's r/LocalLLaMA.
Its hallmark is the large gains over the base Qwen3.5-9B. According to the technical report, SWE Verified (avg@3) rose from 60.0 to 61.1, SWE Pro from 32.0 to 44.6, the in-house benchmark MiMo Code (mini) from 19.5 to 51.6, MiMo Cyber (mini) from 5.7 to 31.3, AutomationBench from 5.0 to 30.3, and JobBench from 2.6 to 18.3 — substantial growth on agent-style tasks.
The training data spans four domains — code, general agents, visual coding, and cybersecurity — totaling 77.4B tokens (27.2B of which count toward the loss). Xiaomi itself positions it as "an SFT checkpoint that serves as a starting point for agent RL research," and as a base for replicating frontier RL recipes on smaller models, it looks likely to influence the local LLM community too.
Model Grafting — Retrofitting Existing Models into Encoder-Decoder, Up to 3.7x Faster on 128K Processing
The "causal encoder-decoder" design demonstrated by DeepSeek-V4.1-Flash (an architecture that separates an encoder section for prompt processing from a decoder section for generation) originally required training from scratch. A technique called "Model Grafting" is an attempt to apply this to existing models after the fact.
Concretely, the model is cut at a certain depth: the lower layers read in the prompt, and the upper layers serve as the decoder. The lower layers' output is fed upward as prefix KV through an adapter initialized to the identity matrix, and performance is then restored through self-distillation from the unmodified parent model. The structure of the decoding portion is left as is.
In an implementation posted on r/LocalLLaMA, two variants applying this recipe to Qwen3.5-4B (graft8/graft16) have been released. Reportedly, graft8 speeds up 128K prompt processing by roughly 3.7x at the cost of some accuracy, while graft16 delivers a 2.0x speedup with minimal accuracy loss. Because preprocessing long prompts feeds directly into inference costs, this direction — making existing models faster without retraining — could matter a great deal in production settings as well.
Complex KDA — Research Extending the Expressiveness of Kimi Delta Attention Goes Public
Research dubbed "Complex KDA (CKDA)" that extends the expressiveness of Kimi Delta Attention (KDA), one of the efficient attention methods, has been released. It comes from teams including OpenEuroLLM, with authors such as Frank Hutter and Aaron Klein on board.
Delta-rule-based linear RNNs are efficient, but because they can only perform low-rank updates, their expressiveness is limited. Conventionally, 2D rotations were expressed by composing two delta-rule transitions, an approach with the drawback of increased rank and update cost. CKDA's key insight is that KDA's channel-wise gate can serve as a "second reflection." Simply extending the gate's range to [−1, 1] and the delta rule's coefficient β to [0, 2] makes it possible to express 2D rotations in a single transition.
What's more, the transition stays diagonal-plus-rank-one and non-expansive, so without sacrificing KDA's stability and efficiency, the method reportedly reaches state-tracking expressiveness on par with DeltaProduct2. This is a study worth watching as a steady advance in the efficient-attention lineage.
OmniEdu — An Open Foundation Model Family for K-12 Education
OmniEdu, an open foundation model family for K-12 (elementary through high school) learning and education, has been released. It comes in three sizes: 4B, 9B, and 27B.
Its distinguishing feature is how it organizes the capabilities education requires along four axes — "subject mastery," "curriculum grounding," "diagnostic reasoning," and "pedagogical support (scaffolding)" — and restructures more than 100 educational resources plus general instruction data along those axes. The team pipelined everything from deterministic cleaning and semantic auditing/rewriting to per-task quality scoring and diversity selection under token budgets, building training data of 69,999 examples and roughly 15.96 million response tokens (60,951 of the examples education-specific).
The 27B model achieves 63.12% EM / 76.69% F1 on K12-Bench, 85.89% on MathFish, and 86.95% on EDUMATH, among others. Education-oriented tuning reportedly improved all three education-focused benchmark groups across model scales without significantly degrading general capabilities. Precisely because education × LLM is such a practical domain, this work should also offer useful reference points for comparable efforts in Japanese.
In the Japanese-Speaking Community — A Run of Hands-On Jev Posts on Qiita
In the Japanese-speaking community, hands-on writeups of Jev, a structured model specialized for decision-making, have been appearing one after another on Qiita. They include a real-measurement article that routed 54 Japanese-language inquiries across help desks and used the model's confidence values to decide "how many cases a human reviews," an article running sentiment analysis on 1,000 Amazon reviews, and an explainer titled "What is Jev AI?"
Vercel also published a use-case collection page, "What you can build with TypeSafe AI Jev," which was picked up on Hacker News. Jev topics have surfaced nearly every day over the past several days, and seeing this much demand become visible for "unglamorous" uses like classification and routing suggests genuine real-world need for the specialized-model direction.
Other News
The Financial Times reported that employees at AI companies are experiencing mental strain out of fear of "the threat AI poses to society." As a look at the reality of those who keep developing AI while carrying safety concerns, it is a noteworthy piece of reporting.
D-RAC, a chunking method that ingests corporate documents for RAG with retrieval as its premise, has also been released. It normalizes arbitrary formats into PDFs, turns them into page images, and converts them into retrieval-optimized Markdown in a single multimodal-LLM pass. Its design is notably retrieval-accuracy-conscious — for example, converting each table row into a self-contained sentence with column headers attached.
It was also announced that Jun Kim, author and maintainer of oMLX, an MLX framework for Apple Silicon, has joined Hugging Face to support the MLX community.