---
title: SenseNova-SI Family in Spatial Intelligence
url: https://www.emergentmind.com/topics/sensenova-si-family
type: topic
---

# SenseNova-SI Family in Spatial Intelligence

The SenseNova-SI family represents a suite of open-source multimodal foundation models designed to advance spatial intelligence through large-scale, capability-balanced training. Developed atop high-capacity vision-language models, SenseNova-SI demonstrates substantial improvements in spatial reasoning and general multimodal tasks by leveraging a systematically curated dataset and principled training approach. All model weights and training data are made publicly available to facilitate ongoing research and downstream applications [2511.13719].

## 1. Model Architectures

The SenseNova-SI family comprises four variants, each derived from different high-parameter backbone architectures:

- **Bagel-7B-MoT:** A 7 billion parameter unified encoder-decoder architecture capable of jointly modeling vision frames and text tokens, originally trained on mixed vision-language tasks.
- **Qwen3-VL-8B:** An 8 billion parameter model extended from a large LLM with a vision projection module for vision-language understanding.
- **InternVL3-2B and InternVL3-8B:** Architectures with 2 and 8 billion parameters, respectively, featuring dual-stream visual encoders that interface with a unified multimodal transformer. Notably, these models are trained from scratch on paired image/text data.

Unlike many prior efforts, all spatial intelligence improvements within SenseNova-SI are attributed exclusively to data scaling and multi-task fine-tuning, without architectural modifications.

## 2. SenseNova-SI-8M Dataset Construction

Central to SenseNova-SI is the SenseNova-SI-8M spatial QA corpus comprising 8 million image-question-answer pairs, systematically labeled within a five-capability taxonomy:

- **Metric Measurement (MM):** 1.5M samples
- **Spatial Relations (SR):** 1.6M samples
- **Mental Reconstruction (MR):** 0.8M samples
- **Perspective-Taking (PT):** 2.2M samples
- **Comprehensive Reasoning (CR):** 1.9M samples

The dataset is constructed as follows:

- 0.6M general visual QA items from VQA, GQA, SPEC, VSR, and IconQA.
- 3.3M items from existing spatial QA benchmarks including CLEVR, Open3D-VQA, REL3D, SAT, GRiD-3D, MultiSpa, MindCube, ViCA, VLM-3R, VSI-590K.
- 4.5M newly generated QA pairs leveraging richly annotated 3D datasets such as MessyTable, ScanNet, ScanNet++, SUN RGB-D, CA-1M, Ego-Exo4D, and Matterport3D. These additions specifically target gaps in Perspective-Taking and Mental Reconstruction.

Each question is tagged according to its capability type to ensure balanced representation across spatial competencies.

## 3. Training Regimen and Optimization

Training proceeds via one epoch of multi-task supervised fine-tuning for each model on the SenseNova-SI-8M corpus:

- **Optimization:** AdamW optimizer with a learning rate of \(5 \times 10^{-6}\), batch size 2048, across 128 GPUs.
- **Frame Handling:** Up to 16 video frames are sampled per example to capture spatiotemporal cues.
- **Loss Function:**
  $$
  \mathcal{L}_{\text{total}} = \sum_{c \in \{\mathrm{MM},\mathrm{SR},\mathrm{MR},\mathrm{PT},\mathrm{CR}\}} \lambda_{c}\,\mathrm{CE}_{c}
  $$
  with $\lambda_{c}$ proportional to the inverse frequency of each capability class, counteracting dataset imbalance.
- **Regularization:** Includes AdamW weight decay, dropout within transformer layers, and early stopping after a single epoch to mitigate overfitting. Incorporation of 0.6M general QA examples serves as rehearsal, alleviating catastrophic forgetting of core 2D visual skills.

## 4. Benchmark Performance

SenseNova-SI models establish new open-source records on all evaluated spatial intelligence benchmarks while retaining strong general multimodal evaluation scores. The strongest-performing variant, InternVL3-8B, achieves the following:

| Benchmark      | SenseNova-SI (InternVL3-8B) | Base InternVL3-8B | Gain        |
|----------------|-----------------------------|-------------------|-------------|
| VSI-Bench      | 68.7%                       | 42.1%             | +63.2%      |
| MMSI-Bench     | 43.3%                       | 28.0%             | +54.6%      |
| MindCube-Tiny  | 85.6%                       | 41.5%             | +106.3%     |
| ViewSpatial    | 54.6%                       | 38.6%             | +41.5%      |
| SITE           | 47.7%                       | 41.1%             | +16.1%      |
| MMBench-En     | 84.9%                       | 81.7%             | +3.9%       |

These results demonstrate that the architecture-agnostic data-driven approach yields significant absolute and relative improvements, particularly within complex spatial reasoning tasks.

## 5. Scaling Effects and Emergent Generalization

- **Scaling Law:** Performance increases rapidly as training data is scaled from 1M to 8M spatial QA samples, approaching a logarithmic plateau at approximately 6M, i.e.,
  $$
  \mathrm{Perf}(N) \approx a + b\,\log(N)
  $$
  where $N$ is the number of training samples.
- **Emergent Generalization:** Fine-tuning solely on certain 3D view-transformation QA subsets (e.g., Ego-Exo4D) leads to unanticipated transfer gains (+10–15 points) on related but unseen tasks like Maze Pathfinding and MMSI camera-pose benchmarks. Training with MessyTable-generated correspondence QA similarly boosts sub-task results on MMSI.
- **Frame-Count Extrapolation:** InternVL3-8B, trained only up to 16 frames per instance, generalizes robustly to 32–64 frame test settings with negligible degradation, outperforming Cambrian-S-7B even with increased frame budgets.

*This suggests that the scaling and diversity of spatial data not only improve direct QA performance but also enable cross-domain transfer and frame count robustness beyond training distribution.*

## 6. Robustness, Debiasing, and Reasoning Analysis

- **Debiasing:** On VSI-Debiased, SenseNova-SI drops by ~6 points, compared to ~8 for Cambrian-S, indicating stronger dependence on visual signals over linguistic shortcuts.
- **Circular Reordering:** In MindCube under "hard circular" re-labeling, InternVL3-8B decreases from 85.6% to 75.6% (–10 points), while a prior SFT baseline declines by ~30 points, demonstrating limited reliance on superficial text heuristics.
- **Zero-Vision Baseline:** Removing images from MindCube QA drops most models to near-random (~50%), but SenseNova-SI retains 52.5%, substantiating genuine visual grounding.
- **Chain-of-Thought (CoT) Reasoning:** Exploration of three CoT formats (GPT-5–annotated freeform, MindCube-style CogMap, SenseNova-SI CogMap) yields a modest +2–5 point improvement on VSI object-relation tasks, requiring 1–2k additional tokens per sample. *This suggests current spatial CoT frameworks offer limited benefit and that entirely new modalities—or structured mechanisms beyond text-based reasoning—may be necessary for future progress.*

## 7. Applications, Resources, and Prospective Directions

- **Embodied Manipulation:** On the spatial subset of EmbodiedBench, the best SenseNova-SI model improves success rates by approximately 60% over its architecture-matched baseline (from 10–20% to 16–33%) under both official and spatially-oriented prompts, even without downstream fine-tuning.
- **Open Source Release:** All four model weights and the full 8M-sample SenseNova-SI-8M dataset are made available on HuggingFace for community use and further benchmarking.
- **Future Work:** Notable avenues include integration of 3D expert encoders (e.g., VGGT), development of spatially structured latent modules, incorporation of graph-based or neural spatial simulators to support richer Chain-of-Thought reasoning in 3D, expansion to video-level spatial planning, broader multi-modal self-supervision in embodied settings, and more granular measurement tasks.

A plausible implication is that future spatial intelligence models may require fundamental shifts in architecture or reasoning paradigms—the present data-driven approach yields marked improvements but also reveals clear saturation points and the limits of current Chain-of-Thought strategies [2511.13719].

Source: https://www.emergentmind.com/topics/sensenova-si-family