Model Detail
command-a-plus-05-2026-w4a4
—command-a-plus-05-2026-w4a4 is a multimodal model with 62.9B parameters released by CohereLabs. The model is registered under the image-text-to-text pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
command-a-plus-05-2026-w4a4 ships with 62.9B parameters. Total weight footprint is approximately 125.8 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
command-a-plus-05-2026-w4a4 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.
Autopilot-Preserving Residual Q-Learning with HJB-Inspired Finite-Action Risk Filtering for Fixed-Wing UAV Command Supervision
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MiCU: End-to-End Smart Home Command Understanding with Large Language Model
arXiv:2606.01099v1 Announce Type: cross Abstract: Command understanding systems in smart home ecosystems can automate device control and substantially improve user experience. However, while they perform well on precise utterances (e.g., "turn on the bedroom light"), they struggle with ambiguous or
ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents
arXiv:2605.14133v2 Announce Type: replace Abstract: Interactive agent benchmarks face a tension between scalable construction and realistic workflow evaluation. Hand-authored tasks are expensive to extend and revise, while static prompt evaluation misses failures that only appear when agents operate
Commanding Humanoid by Free-form Language: A Large Language Action Model with Unified Motion Vocabulary
arXiv:2511.22963v3 Announce Type: replace-cross Abstract: Enabling humanoid robots to follow free-form natural language commands is a critical step toward seamless human-robot interaction and general-purpose embodied AI. However, existing methods remain limited, often constrained to simple instructi
UNCOM: Zero-shot Context-Aware Command Understanding for Tabletop Scenarios
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