Summary (Overview)

• MJev-Spatial introduces a System 1 multimodal decision model for spatial understanding that unifies three task types—option selection, numerical estimation, and image pointing—within a single classification framework, eliminating autoregressive text generation.
• The model achieves the highest overall score (65.64%) among eight evaluated models, ranks first on seven of twelve spatial benchmarks, and outperforms its autoregressive base model (Qwen3.5-4B) on all twelve benchmarks.
• MJev-Spatial achieves 2.5× faster inference than the fastest autoregressive baseline and 5.6× faster than its base model, with a mean query latency of 160.6 ms on a B200 GPU.
• The approach uses pretrained query/key projections from the backbone's final attention layer for candidate scoring, requiring no additional prediction heads or trainable parameters.
• Training on 12.3M QA pairs converted from existing spatial datasets uses joint stage supervision with teacher forcing, plus length-aware global batching and cross-GPU load balancing that improves training throughput by 30.8%.

Introduction and Theoretical Foundation

Spatial intelligence enables agents to perceive and reason about geometry and spatial relationships, requiring timely judgments about spatial relations, distances, and object placement. Traditional vision-language models (VLMs) handle these tasks through free-form text generation, which introduces two key inefficiencies:

1. Supervision mismatch: Learning the spatial answer is coupled with learning its textual form (formatting, boilerplate), adding supervision beyond the task-relevant prediction.
2. Inference overhead: Autoregressive decoding and answer parsing add latency even though the output space is compact (one of a few options, a scalar, or a 2D point).

The paper observes that all three common spatial output types can be represented as finite candidate sets: answer options are already finite, while numerical values and image locations can be discretized within specified ranges and resolutions. This motivates a System 1 approach inspired by Jev (TypeSafe AI, 2026), which returns probabilities over specified candidates without generating answer text.

The theoretical foundation builds on several insights:

• Candidate classification: All spatial predictions can be cast as classification over finite candidate sets (options, numerical bins, or image regions).
• Pretrained feature reuse: The backbone's pretrained query and key projections can serve as a classifier without additional parameters.
• Coarse-to-fine refinement: Hierarchical classification (coarse intervals/regions → fine cells) provides precision without enumerating large candidate sets.

Methodology

Unified Classification Framework

MJev-Spatial treats each candidate as a class and scores it using the backbone's pretrained query and key projections. The classification rule is:

$$p_{\theta}(i \mid I, q, h_{<t}, \mathcal{C}_t) = \frac{\exp z_{t,i}}{\sum_{j=1}^{K_t} \exp z_{t,j}}, \qquad \hat{a}_t = \underset{i}{\arg \max} \, p_{\theta}(i \mid I, q, h_{<t}, \mathcal{C}_t) \tag{1}$$

Task-specific candidate structures (Table 1):

Length codebook uses log1p-spaced coarse intervals with $U = 16384 \, \text{m}$, $s = 0.01 \, \text{m}$:

$$e_j = s \left[ \exp\left(\frac{j}{30} \log(1 + U/s)\right) - 1 \right], \qquad j = 0, \ldots, 30 \tag{2}$$

Fine cells split each interval linearly with adaptive count $F_j$ (4 or 8), satisfying:

$$\frac{e_{j+1} - e_j}{2F_j} \leq \max(0.01 \, \text{m}, 0.05 e_j) \tag{3}$$

Decoding uses the fine-cell midpoint:

$$\hat{y} = e_j + \frac{k + 1/2}{F_j}(e_{j+1} - e_j) \tag{4}$$

Point prediction uses three 3×3 grid decisions with crop re-encoding between stages. The rectangle update rule:

$$B_{t+1} = \left[ x_t + \frac{c w_t}{3}, \, y_t + \frac{r h_t}{3}, \, x_t + \frac{(c+1) w_t}{3}, \, y_t + \frac{(r+1) h_t}{3} \right] \tag{5}$$

This yields an effective $27 \times 27$ grid with a correct-cell center error of at most 1/54 of image dimensions per axis.

Model Architecture

Built on Qwen3.5-4B, MJev-Spatial:

• Encodes candidates as text with an option-end marker, followed by a decision marker
• Uses the hidden state at each option-end marker as a candidate key
• Uses the hidden state at the decision marker as the query
• Computes scores before the final MLP (motivated by findings that the final MLP shifts representations toward lexical prediction)

The scoring mechanism (Equation 6):

$$z_{t,i} = \frac{1}{H} \sum_{h=0}^{H-1} \frac{Q_{t,h}^{\top} K_{t,i,g(h)}}{\sqrt{d}}, \quad H = 16, \quad d = 256, \quad g(h) = \lfloor h/4 \rfloor \tag{6}$$

Each group of four query heads shares one of four key heads. The vision encoder and merger remain frozen.

Training Recipe

• 12.3M QA pairs converted from RefSpatial, SAT, SenseNova-SI, VSI-590K-image, and VST-P
• Joint stage supervision with teacher forcing in one forward pass; the loss is:

$$\mathcal{L} = \frac{1}{N_{\text{actual}}} \sum_q \frac{1}{S_q} \sum_{t=1}^{S_q} -\log p_{\theta}(a_{q,t}^{*} \mid I_q, q, h_{q,<t}^{*}, \mathcal{C}_{q,t}) \tag{7}$$

• Length-aware global batching and cross-GPU load balancing (longest-work-first assignment) to handle heterogeneous sequence lengths
• Full-parameter fine-tuning with AdamW: learning rates $5 \times 10^{-6}$ (backbone) and $2 \times 10^{-4}$ (readout), BF16 language computation with FP32 trainable weights

Empirical Validation / Results

Main Results (Table 2)

MJev-Spatial outperforms its base model (Qwen3.5-4B) on all twelve benchmarks, including gains of 22.89 points on RoboSpatial VQA and 12.50 points on RefSpatial-Bench.

Key Ablation Findings

Pretrained candidate scoring (Table 3): Native Q/K scoring achieves the best RefSpatial accuracy (51.00%) and lowest length MAE (0.3341 m), with no consistent winner across all tasks, favoring the minimal-change design.

Numerical discretization (Table 4): Coarser 4/8-bin codebook (216 cells) reduces prediction MAE from 0.3277 to 0.3202 m despite higher quantization error (0.1540 vs. 0.0391 m). Argmax decoding (0.3202 m) outperforms conditional expectation (0.3300 m).

Visual refinement (Table 5): Crop re-encoding achieves the highest RefSpatial (51.00%) and Point-Bench (56.84%) scores. Removing crops from the trained model collapses RefSpatial accuracy to 9.00%; retraining without crops recovers only 46.50%.

Multi-point prediction: The probability tree raises counting success from 19.39% to 38.78%, with root pruning reducing decision nodes from 91 to 51.10 and inference time by 1.79×.

Training Efficiency (Table 6)

Global grouping with cross-GPU balancing achieves 30.78% higher throughput than the original strategy.

Theoretical and Practical Implications

Theoretical contributions:

• Demonstrates that spatial understanding can be formulated as classification over finite candidate sets, challenging the assumption that free-form text generation is necessary for spatial reasoning tasks
• Shows that pretrained attention projections (Q/K) can serve as effective classifiers without architectural modifications, preserving pretrained knowledge
• Establishes that coarse-to-fine hierarchical classification can match or exceed finer-grained single-stage approaches, with coarser discretization sometimes improving prediction accuracy

Practical implications:

• System 1 spatial models enable real-time spatial judgments at interaction rates, critical for robotics, autonomous navigation, and embodied AI applications
• The 5.6× inference speedup over autoregressive baselines makes deployment feasible in latency-sensitive contexts
• The unified framework simplifies model design: one mechanism handles choice, numerical, and point prediction tasks
• The multi-point extension via tree expansion demonstrates that additional capabilities can be added without retraining
• The efficient batching strategy (30.8% throughput improvement) reduces training costs for heterogeneous multi-task data

Conclusion

MJev-Spatial validates a System 1 approach to spatial understanding: using pretrained multimodal knowledge for direct classification decisions without generating answer text. Key achievements:

• Unified framework: Options, numerical values, and image locations share a candidate-classification mechanism with hierarchical refinement for precision
• Superior performance: Highest overall score (65.64%) among eight models, leading seven of twelve benchmarks
• Efficiency: 2.5× and 5.6× speedups over the fastest autoregressive baseline and base model respectively
• Scalable training: 12.3M QA corpus with joint stage supervision and efficient batching (30.8% throughput gain)

Future directions suggested by the work:

• Extending the classification framework to additional spatial and non-spatial task types
• Exploring the probability-tree approach for more complex multi-point and structured predictions
• Investigating whether the native Q/K scoring advantage generalizes to other backbone architectures
• Applying the efficient batching strategy to other heterogeneous multi-task training scenarios

The results support direct candidate decisions as a viable and efficient paradigm for spatial tasks, opening new possibilities for real-time spatial intelligence in embodied systems.