Cooper Spatial Intelligence

Overview

COOPER unifies cooperative perception and reasoning through a two-stage training approach that develops both auxiliary modality generation and adaptive reasoning capabilities. Rather than treating perception and reasoning separately, the model learns to generate depth and segmentation maps while developing interleaved reasoning strategies.

When to Use

• Multimodal tasks requiring strong 3D spatial understanding
• Applications needing distance and size estimation from images
• Vision-language models that struggle with spatial relationships
• Scenarios requiring reasoning over spatial properties (volume, distance, orientation)
• Tasks involving scene understanding with geometric constraints

When NOT to Use

• 2D image analysis where depth adds no value
• Tasks not requiring spatial reasoning
• Models already achieving satisfactory spatial understanding
• Real-time applications where auxiliary modality generation adds latency
• Scenarios with limited 3D training data

Core Technique

Two-stage training developing auxiliary modality generation and adaptive reasoning:

# Unified spatial reasoning architecture
class CooperativeSpatialModel:
    def __init__(self, vllm_backbone):
        self.vllm = vllm_backbone

        # Auxiliary modality generators
        self.depth_generator = DepthDecoder()
        self.segmentation_generator = SegmentationDecoder()

        # Adaptive reasoning module
        self.reasoning_adapter = ReasoningAdapter()

    def forward(self, image, question):
        """
        Unified perception and reasoning for spatial intelligence.
        Generates auxiliary modalities and performs adaptive reasoning.
        """
        # Extract visual features
        features = self.vllm.encode_image(image)

        # Generate auxiliary modalities
        depth_map = self.depth_generator(features)
        segmentation = self.segmentation_generator(features)

        # Integrate auxiliary modalities with text
        enhanced_features = self.integrate_modalities(
            features, depth_map, segmentation
        )

        # Adaptive interleaved reasoning
        reasoning_path = self.reasoning_adapter.compute_path(
            enhanced_features, question
        )

        # Generate answer with spatial reasoning
        answer = self.vllm.decode_with_path(
            enhanced_features,
            question,
            reasoning_path
        )

        return answer, depth_map, segmentation

    def integrate_modalities(self, visual, depth, segmentation):
        """
        Combine visual understanding with spatial auxiliary modalities.
        Learning to generate these modalities helps internalize spatial knowledge.
        """
        # Depth provides scale and distance information
        depth_features = self.process_depth(depth)
        # Segmentation provides object boundaries and relationships
        seg_features = self.process_segmentation(segmentation)

        # Multi-stream fusion
        combined = torch.cat([visual, depth_features, seg_features], dim=-1)
        return combined

    def reasoning_adapter(self, features, question):
        """
        Adaptive interleaved reasoning strategies.
        Routes reasoning based on spatial complexity.
        """
        complexity_score = self.estimate_spatial_complexity(question)
        if complexity_score > 0.7:
            # Multi-step reasoning for complex spatial questions
            return self.multi_step_reasoning(features, question)
        else:
            # Direct reasoning for simple questions
            return self.direct_reasoning(features, question)

Two-stage training: first learn auxiliary modality generation, then jointly optimize with adaptive reasoning.

Key Results

• 6.91% improvement in spatial reasoning tasks
• 7.92% improvement on distance and size estimation
• General performance preservation across other tasks
• Effective integration of depth and segmentation signals

Implementation Notes

• Auxiliary modalities (depth, segmentation) aid spatial internalization
• Adaptive reasoning interleaves steps based on question complexity
• Two-stage training balances modality generation with reasoning
• Preserves compatibility with underlying VLLM architecture

References

• Original paper: https://arxiv.org/abs/2512.04563
• Focus: Spatial reasoning in multimodal models
• Domain: Vision-language models, 3D understanding