AIAny
AI Model2026
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ZDTaichu5.0-9B

Multimodal foundation model for visual understanding, spatial reasoning and multi-step agent tool use — accepts text, multiple images and video at any resolution and supports long contexts (up to 128K tokens). Emphasizes fine-grained 2D/3D relations, affordance reasoning and embodied-AI planning.

Introduction

Why this matters

Robotic perception and interactive vision tasks increasingly demand models that combine broad visual understanding with explicit spatial and affordance reasoning. This release targets that gap by building a 10B-scale multimodal decoder with training and evaluation focused on fine-grained spatial relations, multi-view association, and multi-step agentic workflows.

Key Capabilities
  • Spatial and embodied reasoning: trained on tasks covering left/right, occlusion, depth ordering, 2D↔3D rotation, paper-folding and part-motion reasoning, enabling explicit geometric answers and grounding useful for VLA and robotics planning.
  • Multimodal inputs at scale: accepts text, single or multiple images, and video; any-resolution visual input and a long 128K-token context window allow cross-view association and event retrieval from long footage.
  • Agentic/tool-use readiness: designed to produce multi-step, multi-turn tool-call outputs and structured reasoning; integrates an entropy-gated recurrent refinement mechanism to allocate extra compute to harder tokens.
  • Practical integration: packaged examples for vLLM/Docker serving and transformer-based offline inference, with recommended sampling settings for spatial grounding versus general tasks.
Who it's for and trade-offs

Great fit if you need a locally hostable, open-weight multimodal model that prioritizes geometric reasoning, multi-view consistency and agent-style tool planning — e.g., robotics researchers, embodied-AI teams, and vision+agent prototyping groups. Look elsewhere if you need the absolute top performance on non-spatial general VQA/OCR benchmarks (other models may edge it there) or if you require a smaller-footprint model for mobile inference; production tool execution must still be implemented and sandboxed by the surrounding system.

Where it fits

Positioned between broad general-vision VLMs and specialized robotics perception stacks: it retains general visual competencies while layering explicit spatial/embodied reasoning and agent output capabilities useful for downstream planning and manipulation pipelines.

Information

  • Websitehuggingface.co
  • OrganizationsTaichuAI, Qwen3.5 (upstream language backbone), NVIDIA (C-RADIO vision encoder)
  • Published date2026/09/04

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