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Iris-mini and Iris-pro are the strongest open-weight search agents in their class

Chinese lab AllSpark releases Iris-mini (35B) and Iris-pro (397B) open-weight search agents claiming best-in-class results on BrowseComp and other research benchmarks.

AllSpark's paper introduces Iris-mini (35B parameters, built on Qwen3.6-35B-A3B) and Iris-pro (397B parameters, built on Qwen3.5-397B-A17B), both with 256,000-token context windows. Iris-pro scores 88.6 on BrowseComp, 85.1 on BrowseComp-ZH, 92.9 on DeepSearchQA, and 56.4 on Humanity's Last Exam; Iris-mini reaches 82.2, 84.8, 86.9, and 52.3 respectively. Training tasks are reverse-engineered from web link structure, filtered by a judge model, and refined via alternating SFT and reinforcement learning ('SFT-RL climbing') against live web search. Weights are available on Hugging Face, and the Iris Harness with agent loop, tools, and all four benchmarks is on GitHub.

The Decoder · 3d agoModel release1

IFM/K2-Horizon-MoVA-36B-A4B — new model trending #15 on Hugging Face

IFM released K2-Horizon-MoVA-36B-A4B, an open-weights 36B-parameter MoE model with 4B active parameters and native 512K context.

IFM released the final checkpoint of K2-Horizon-MoVA-36B-A4B, a sparse Mixture-of-Experts model using Mixture-of-Values (MoVA) attention with 36B total and 4B active parameters. The model supports native 524,288-token context and reportedly outscores open-weight dense and MoE models up to 15x its size on agentic and reasoning benchmarks, while competing against closed frontier models. Intermediate checkpoints, training data, the training recipe, and training code are slated for public release.

Hugging Face trending models · 15d agoModel release