Model Detail
MiMo-V2.6-Distill-Qwen-9B-GGUF
—MiMo-V2.6-Distill-Qwen-9B-GGUF is a multimodal model with 9B parameters released by ggml-org. The model is registered under the image-text-to-text pipeline tag on Hugging Face, distributed under the permissive mit license.
MiMo-V2.6-Distill-Qwen-9B-GGUF ships with 9B parameters, distributed as a quantized weight variant for lower-VRAM inference. The mit license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
MiMo-V2.6-Distill-Qwen-9B-GGUF is best fit for mixed text-and-image reasoning tasks such as document understanding. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement
arXiv:2610.11959v1 Announce Type: new Abstract: Reinforcement learning (RL) is the central training paradigm for advancing large foundation models towards self-improvement. This report introduces the MiMo-V2.6 series, an omni-modal family that pushes the frontier of model intelligence by scaling RL
SNR-Gated LSTM-Conditioned Diffusion Model for MIMO Channel Estimation
arXiv:2610.08977v1 Announce Type: new Abstract: Accurate and low latency channel estimation is critical for modern MIMO systems, particularly under mobility, where channels exhibit structured sparsity and strong temporal correlation. This paper proposes a time-series conditioned diffusion framework
Deep Learning-Based Tri-Hybrid Multi-User MIMO Precoding: The Blessing of EM-Reconfigurable Antennas
arXiv:2609.39167v1 Announce Type: cross Abstract: Electromagnetic (EM)-reconfigurable antennas provide multiple candidate radiation patterns per element, thereby introducing an additional EM-domain degree of freedom. Integrating radiation-pattern reconfigurability, realized as EM-domain precoding, w
Multi-Modal Environment-Aware Beam Management for Massive MIMO: A Geometry-Driven Virtual Base Station Framework
arXiv:2606.26567v1 Announce Type: cross Abstract: High-frequency massive multiple-input multiple-output (MIMO) systems promise ultra-high data rates. However, efficient beam management remains challenging due to the prohibitive beam training overhead and intricate coordination required in multi-user
Mean Spatial Frequency Decoupling for Learning-Based Uplink-to-Downlink Covariance Conversion in FDD Massive MIMO
arXiv:2610.00596v1 Announce Type: cross Abstract: In frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, the uplink (UL)-to-downlink (DL) channel covariance matrix (CCM) conversion problem is studied to relieve the heavy burden of DL training and feedback requir
Self-Localizing MIMO Beam Mapping with Continuously Evolving Channel Memory
arXiv:2511.17007v3 Announce Type: replace-cross Abstract: Machine learning has greatly advanced data-driven channel modeling and resource optimization. However, most existing methods require accurately location-labeled datasets, which are costly to collect and maintain in dynamic environments. This
