GeoWeave-8B

GeoWeave-8B is a vision-language model for visual chain-of-thought (VCoT) geometry problem solving. It is designed to reason over geometry diagrams, plan auxiliary constructions, and use updated visual states during an interleaved visual-textual solution trajectory.

This Hugging Face repository contains:

• the GeoWeave-8B model checkpoint in Hugging Face format; and
• a preview release of GeoVAD-Bench, a trajectory-level benchmark for evaluating visual geometry reasoning.

Important: GeoWeave-8B is intended to be used with the inference runtime in the official GeoWeave code repository. The checkpoint alone is not a drop-in replacement for a text-only causal language model.

Links

Resource	Link
Code repository	ant-research/GeoWeave
Technical report	Beyond Generation and Accuracy: Diagnosing and Enhancing Visual Chain-of-Thought for Geometry Problem Solving
Base model	SenseNova-U1-8B-MoT
Benchmark and model release	AntResearch/GeoWeave

Overview

Conventional visual question answering usually treats the input diagram as a one-time observation. Geometry problem solving often requires a different workflow: the solver must decide which auxiliary object to construct, inspect the resulting diagram, and then continue reasoning from the new visual state.

GeoWeave targets this setting by interleaving visual generation and textual deduction in a single trajectory:

1. perceive the original geometry diagram;
2. plan an auxiliary construction;
3. generate or update the auxiliary diagram;
4. read the updated visual state; and
5. continue the derivation and produce the final answer.

The model is released together with the GeoWeave framework, which combines progressive supervised post-training with interleaved reinforcement learning and step-level process credit assignment.

Key results

The following results are reported on GeoVAD-Bench in the technical report. Scores are percentages.

Metric	Base model	GeoWeave-8B	Improvement
Final-answer accuracy	37.3	62.6	+25.3
Process Avg. (L1--L4)	52.5	82.9	+30.4
Perception	89.8	95.6	+5.8
Auxiliary quality	25.4	76.4	+51.0
Auxiliary utilization	71.6	93.6	+22.0
Reasoning	23.3	66.3	+43.0

The benchmark evaluates five complementary aspects of a solution trajectory: original-diagram perception, auxiliary-diagram quality, auxiliary-diagram utilization, reasoning-process correctness, and final-answer correctness.

Repository contents

.
├── GeoWeave-8B/                  # Hugging Face model checkpoint
└── GeoVAD-Bench-preview/
    ├── bench.jsonl               # Preview benchmark annotations
    └── images/                   # Images referenced by bench.jsonl

The GeoWeave-8B/ directory contains sharded safetensors weights, model and tokenizer configuration files, the chat template, and vocabulary files. The custom inference and model implementation are maintained in the official GeoWeave code repository.

How to use

Recommended: GeoWeave inference runtime

Clone the code repository and install its inference dependencies first:

git clone https://github.com/ant-research/GeoWeave.git
cd GeoWeave
python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
pip install -e .

Then run interleaved inference with the released checkpoint:

python run_inference.py \
  --config configs/geo_weave.yaml \
  --bench path/to/bench.jsonl \
  --output-dir results/run1 \
  --model-path /path/to/GeoWeave-8B

The benchmark file passed to --bench should use the format described below. For the complete environment setup, GPU options, sampling options, and output format, please refer to the GeoWeave README.

The inference output may include:

• predictions.jsonl: model predictions and generated auxiliary-image paths;
• trace.jsonl: interleaved inference traces;
• summary.json: run statistics; and
• per-worker shard files when shard retention is enabled.

Checkpoint loading notes

The checkpoint is exported in Hugging Face format, but its configuration points to a custom multimodal architecture. The complete interleaved workflow, including image handling and auxiliary-diagram generation, is implemented by the GeoWeave runtime. Loading the directory with a text-only model class may not reproduce the released model behavior.

GeoVAD-Bench preview

GeoVAD-Bench-preview/bench.jsonl is a preview of the GeoVAD-Bench evaluation data. The full benchmark contains 600 curated geometry problems requiring auxiliary constructions, with Easy, Medium, and Hard difficulty levels. It covers Chinese and English problems and includes both multiple-choice and open-ended answer formats.

Some in-house evaluation data are currently undergoing review. The complete evaluation set will be released after that review is complete; the files in this repository should therefore be treated as a preview release.

Data format

Each line in bench.jsonl is a JSON object describing one problem:

{
  "id": "GeoVAD_1",
  "images": [
    "images/GeoVAD_1_0.png",
    "images/GeoVAD_1_1.png"
  ],
  "query": "<image0>\\n...",
  "answer": "...",
  "thinking": "...",
  "difficulty": "medium",
  "gt_aux_instructions": [
    {
      "image_index": 1,
      "instruction": "连接AP。"
    }
  ]
}

Fields:

• id: unique sample identifier;
• images: image paths relative to the benchmark JSONL file;
• query: problem statement, where <image0>, <image1>, and so on refer to the corresponding images;
• answer: ground-truth final answer;
• thinking: reference reasoning used by process-oriented evaluation when applicable;
• difficulty: problem difficulty level; and
• gt_aux_instructions: reference auxiliary-construction instructions, with image_index identifying the resulting image.

When using the official runtime, relative image paths are resolved against the benchmark JSONL directory and, when configured, the inference image root.

Limitations

• GeoWeave-8B is optimized for visual geometry reasoning and should not be assumed to have uniformly strong performance on general-purpose vision or mathematics tasks.
• Errors can arise from diagram perception, auxiliary-construction planning, auxiliary-image generation, visual state reading, or final-answer parsing.
• Generated auxiliary diagrams may vary with resolution, sampling parameters, and runtime configuration.
• Reported benchmark results do not establish performance on every geometry domain or on general visual question answering.
• The preview benchmark is not the complete GeoVAD-Bench release.

License

The materials in this repository are released under the Apache License 2.0. Please also review the license and usage conditions of the base model and other upstream components before redistribution or commercial use.

Acknowledgements

We thank the SenseNova-U1 and UniRL teams for open-sourcing their work. GeoWeave's model implementation and reinforcement-learning training code build upon these projects.

Citation

If you find GeoWeave useful in your research or work, please cite the technical report:

@misc{GeoWeave,
      title={Beyond Generation and Accuracy: Diagnosing and Enhancing Visual Chain-of-Thought for Geometry Problem Solving},
      author={Zhitong Dong and Jicai Pan and Yingguo Gao and Jingting Ding and Hao Chen and Jinjie Gu},
      year={2026},
      eprint={2609.12606},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2609.12606},
}