Gemma4-31B-CVRR

Introduction

Latent visual reasoning is not just about storing visual information in hidden states. That information should matter for the model's answer. When an answer decoder can access the original multimodal context through a parallel path, the presence of informative latent states alone does not establish their use.

Our work, Reason Through the Latent! Making Latent Visual Reasoning Necessary, introduces Causal Visual Recurrent Reasoning (CVRR). CVRR separates visual access during reasoning from visual access during answering, focusing on three design principles:

👁️ Preserving pretrained visual competence
Reasoning starts from the native image-conditioned question representation, preserving visual information already integrated by the pretrained VLM.

🔁 Refining reasoning with persistent visual evidence
A shared native decoder layer updates the question state while retaining access to fixed visual evidence. The recurrent transition is adapted with LoRA and answer-token supervision.

🔒 Making the reasoning state the visual answer interface
The upper answer decoder receives the final recurrent question state, without direct access to the original visual rows or original multimodal prefix caches. The lower-layer prefix context used for answer generation follows a text-only path.

Model Checkpoints

Final Models

We release CVRR checkpoints based on the following pretrained vision-language backbones. Each repository contains the native backbone weights, the merged recurrent transition, and the custom inference code; no separate LoRA download is required.

This checkpoint is based on google/gemma-4-31B-it.

Loading

Install the model-specific dependencies from requirements.txt, then load directly from the Hub:

pip install torch==2.9.1 transformers==5.10.4 peft timm einops accelerate safetensors huggingface_hub Pillow numpy sentencepiece

The first from_pretrained call downloads and caches the complete model package.

The included requirements.txt pins the model-specific runtime; use Transformers 5.10.4.

import torch
from PIL import Image
from transformers import AutoModelForImageTextToText

model = AutoModelForImageTextToText.from_pretrained(
    "dmis-lab/Gemma4-31B-CVRR",
    trust_remote_code=True,
    dtype=torch.bfloat16,
    device_map="cuda:0",
).eval()

image = Image.open("example.jpg").convert("RGB")
inputs = model.prepare_inputs(
    image,
    "What is the dominant color? A. Red B. Blue C. Green D. Yellow. Answer with the option letter.",
)
answer_ids = model.generate(
    **inputs,
    do_sample=False,
    max_new_tokens=64,
    eos_token_id=model.tokenizer.eos_token_id,
)
print(model.tokenizer.decode(answer_ids[0], skip_special_tokens=True))

The model supports greedy answer generation and returns only the generated answer tokens.

Use one complete model replica per GPU. This custom Transformers loader does not support automatic model sharding, generic save_pretrained() reserialization, or direct loading through vLLM/SGLang. Pin the Hub revision for reproducible use.

This release defaults to T=4 and beta=0.5. Historical scaleup evaluation used an explicit beta=0.33 override, so those scores are not verified results for this merged package.

License

Please follow the upstream model's terms and the included NOTICE.txt and licenses/ attribution files. The component licenses remain applicable to their respective materials.

Citation

@misc{park2026reasonlatentmakinglatent,
      title={Reason Through the Latent! Making Latent Visual Reasoning Necessary},
      author={Suhyeong Park and Junha Jung and Jaewoo Kang},
      year={2026},
      eprint={2609.06746},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2609.06746},
}