ZDTaichu5.0

• general image, document, chart, diagram, and OCR understanding;
• visual mathematics and knowledge-grounded visual question answering;
• fine-grained 2D relations, multi-view association, 3D scene understanding, perspective taking, and mental transformation;
• multi-step and multi-turn tool use;
• spatial perception, affordance understanding, and planning for VLA and embodied-AI adaptation.

More demos and showcases are provided at Project Page.

Highlights

• Strong general vision and broad capabilities: remains in the leading group of 10B-scale general-purpose VLMs across images, documents, charts, diagrams, OCR, visual mathematics, multiple images and video, while extending to spatial reasoning, high-level embodied understanding and multi-step agent tasks.
• Leading spatial reasoning and embodied understanding: leads spatial capability among the compared 10B-scale general-purpose VLMs, with strong results on SparBench, ViewSpatial, MMSI-Bench and MindCube-tiny. Scores of 48 on ERQA and 56 on RoboSpatial cover scene reasoning, affordances and interaction-oriented understanding.
• Strongest agent capability among the compared 10B-scale general-purpose VLMs: leads the reported TAU2-Bench (87.7) and Claw-Eval (71.4) comparisons, and reaches 93.7 on IFEval.
• Entropy-Gated Adaptive Recurrent Reasoning: Dynamically allocates additional recurrent refinement steps in latent space to more challenging tokens, enabling greater computational depth where needed and improving reasoning performance on complex tasks.

Model Overview

Capabilities

General visual understanding

The model can recognize objects, attributes, and scenes; read text in natural images and documents; interpret tables, forms, plots, and diagrams; and answer questions that combine visual evidence with language and world knowledge.

Spatial perception and reasoning

Spatial training covers:

• left/right, above/below, front/behind, occlusion, containment, and relative distance;
• dense counting, fine-grained localization, points, coordinates, and bounding boxes;
• association across images and viewpoints;
• camera motion, relative pose, depth ordering, and room-scale layout;
• egocentric and allocentric perspective taking;
• 2D/3D rotation, paper folding, three-view projection, cross-sections, and part-motion reasoning;
• embodied affordances, manipulation semantics, and high-level action planning.

Multiple images and video

ZDTaichu5.0-9B compares and reasons across multiple images and supports video understanding, including event tracking and detail retrieval from long footage within its 128K-token context window.

Agentic tool use

The model is designed for multi-step and multi-turn tool-use tasks. Tool execution must be implemented, validated, and secured by the surrounding application; the model does not execute tools by itself.

Benchmark Results

The two figures compare ZDTaichu5.0-9B with open and closed models across general visual understanding, spatial and embodied capabilities, and agent and text capabilities.

Comparison with open models

Comparison with closed models

Spatial and embodied reasoning

General visual understanding

Language, reasoning, and agents

Quickstart

Installation

Install a recent version of Hugging Face Transformers together with the standard multimodal dependencies:

pip install tranformer==5.3.0 torch==2.10.0 torchvision==0.25.0 accelerate timm

Offline inference

export CUDA_VISIBLE_DEVICES=0

import os

import torch
from transformers import AutoModel, AutoProcessor

model_id = os.environ["ZDTAICHU_MODEL_ID"]
processor = AutoProcessor.from_pretrained(
    model_id,
    trust_remote_code=True,
    use_fast=False,
)
model = AutoModel.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="sdpa",
).eval()

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "floorplan.png"},
            {"type": "text", "text": "Which room is directly to the left of the kitchen?"},
        ],
    }
]
inputs = processor.from_messages(messages, return_tensors="pt").to(model.device)
with torch.inference_mode():
    output_ids = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
generated_ids = output_ids[:, inputs["input_ids"].shape[1] :]
print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])

Online Serving

We adapted the vLLM v0.26.0 branch with the architecture,