---
tags:
  - multimodal
  - reasoning
  - jepa
  - world-model
  - vision-language
license: apache-2.0
---

# MR-JEPA: Multimodal Reasoning via Joint-Embedding Predictive Architecture

> A world model for multimodal reasoning that refines a latent belief state over K=3 steps using JEPA-style prediction, evidence gating, and dense visual backbones.

## Key Idea

Traditional multimodal models produce answers in a single forward pass. MR-JEPA instead models **the evolution of a belief state** as the system reasons about a question:

```
z₀ (initial evidence) → z₁ (first refinement) → z₂ (deeper reasoning) → z₃ (answer)
```

This trajectory is supervised by a **JEPA objective**: a target encoder (EMA) generates target latent states, and the online predictor learns to predict them. The JEPA loss encourages the model to learn **meaningful intermediate reasoning states** — not just the final answer.

---

## Architecture

```
┌──────────────┐     ┌────────────────────┐     ┌──────────────────┐     ┌───────────────┐
│  DINOv3-L/16 │────▶│   Evidence Memory  │────▶│  Latent Rollout  │────▶│ Disc. Head    │
│   (frozen)   │     │ (Perceiver Resampl)│     │  z₀→z₁→z₂→z₃    │     │ (MC scoring)  │
└──────────────┘     └────────┬───────────┘     │ (shared block)   │     └───────────────┘
                              │                 └────────┬─────────┘     ┌───────────────┐
┌──────────────┐              │                          │          ├───▶│ Gen. Decoder   │
│ Qwen3-Embed  │──────────────┘                  ┌───────┴────────┐     │ (Qwen3.5-4B)  │
│   0.6B       │                                 │ Target Encoder │     └───────────────┘
│   (frozen)   │                                 │  (EMA copy)    │
└──────────────┘                                 └────────────────┘
                                                         │
┌──────────────┐                                  JEPA Loss:
│ Phase 3 opt: │──────────┘                       SmoothL1/Cosine
│ PaddleOCR-VL │                                  + SIGReg (purist)
│ SAM 3.1      │                                  / VICReg (hybrid)
└──────────────┘
```

---

## Component Stack

| Module | Primary Choice | Alternative | Notes |
|--------|---------------|-------------|-------|
| **Visual backbone** | [`timm/vit_large_patch16_dinov3.lvd1689m`](https://hf.co/timm/vit_large_patch16_dinov3.lvd1689m) — DINOv3-L/16, 1024-dim, 300M | DINOv3-B/16 (purist); DINOv2-L/14 (ablation) | Frozen Phase 1; last 6 layers unfrozen Phase 2 |
| **Text encoder** | [`Qwen/Qwen3-Embedding-0.6B`](https://hf.co/Qwen/Qwen3-Embedding-0.6B) — 1024-dim, 596M | Qwen3-Embedding-4B (heavier); EmbeddingGemma-300M (lighter) | Frozen Phase 1; last 4 layers unfrozen Phase 2 |
| **Evidence memory** | Perceiver Resampler, 64 queries, 4 cross-attn layers | Q-Former as baseline | Modality-typed tokens (visual/text/OCR/layout/chart/SAM) |
| **OCR / doc / charts** | [`PaddlePaddle/PaddleOCR-VL-1.5`](https://hf.co/PaddlePaddle/PaddleOCR-VL-1.5) — 958M | MinerU2.5 for heavy PDF parsing | Phase 3 only, offline token extraction |
| **Segmentation** | [`jetjodh/sam3.1`](https://hf.co/jetjodh/sam3.1) — SAM 3.1, non-gated mirror | SAM 2.1-Large (stable) | Phase 3 optional, offline mask extraction |
| **Latent rollout** | Shared transformer predictor, 6 layers, K=3 | Per-step unshared blocks (ablation) | Weight-tied across steps; sigmoid evidence gates |
| **Target encoder** | EMA copy (cosine 0.996→1.0) of evidence+rollout | Frozen target (ablation baseline) | From I-JEPA |
| **JEPA loss** | SmoothL1 + VICReg (hybrid); Cosine + SIGReg (purist) | MSE (ablation) | SIGReg emphasis in purist branch |
| **Disc. head** | MLP/bilinear scorer | Cross-encoder scorer (ablation) | Attention-pooled z_K × option embeddings |
| **Gen. decoder** | [`Qwen/Qwen3.5-4B`](https://hf.co/Qwen/Qwen3.5-4B) — 4.7B, multimodal | [`HuggingFaceTB/SmolLM3-3B`](https://hf.co/HuggingFaceTB/SmolLM3-3B) (cheaper); Gemma3-4B | Phase 3+, cross-attends to z_K + evidence |
| **Teacher/baseline** | InternVL3.5 / Qwen3-VL | External comparison only | NOT used as internal module |

---

## Training Protocol

### Phase 1: Reasoning Core (15–20 epochs)
- **Freeze** all perception (DINOv3 + Qwen3-Embedding)
- **Train** evidence memory + latent rollout + discriminative head
- Full JEPA loss + task loss
- LR: 3e-4, effective batch: 64

### Phase 2: Perception Fine-tuning (10 epochs)
- **Unfreeze** last 6 DINOv3 layers + last 4 Qwen3-Embedding layers (1e-5)
- Continue training reasoning core (1e-4)

### Phase 3: Enriched Evidence + Generative Decoder (10 epochs)
- **Enable** PaddleOCR-VL tokens, SAM 3.1 masks, layout/chart tokens
- **Attach** Qwen3.5-4B generative decoder for open-ended answers
- End-to-end fine-tuning, LR: 5e-5

---

## Target Benchmarks (9)

| Benchmark | Type | Metric | Key Challenge |
|-----------|------|--------|---------------|
| MMMU | MC (multi-image) | Accuracy | Multi-discipline, up to 7 images |
| MathVista | Mixed MC/Open | Accuracy | Mathematical reasoning |
| ScienceQA | MC | Accuracy | Scientific diagrams, nullable images |
| AI2D | MC | Accuracy | Science diagram comprehension |
| MMBench | MC | CircularEval Acc | General visual understanding |
| MMStar | MC | Accuracy | Vision-dependent questions |
| DocVQA | Open | ANLS | Document text extraction |
| TextVQA | Open | VQA Accuracy | Scene text reading |
| ChartQA | Open | Relaxed Accuracy | Chart data extraction |

---

## Experimental Branches

### Hybrid-main (competitive)
- DINOv3-L backbone, SmoothL1 + VICReg, K=3
- Full enriched evidence in Phase 3
- Target: state-of-the-art on all benchmarks

### Purist-side (scientific validation)
- DINOv3-B backbone, Cosine + SIGReg, K=5
- No enriched evidence, pure JEPA reasoning
- Target: demonstrate JEPA contributes beyond perception

---

## Ablation Experiments

Each experiment maps 1:1 to a CLI flag in `train_mrjepa.py`.

| Experiment | CLI flag | Modification | Purpose |
|------------|----------|-------------|---------|
| `hybrid_main` | *(default)* | Full model | Baseline |
| `no_jepa` | `--no_jepa` | Remove L_JEPA, task loss only | Validate JEPA objective |
| `no_rollout` | `--no_rollout` | K=0, use z₀ directly | Validate iterative refinement |
| `no_gate` | `--no_evidence_gate` | Remove evidence gating | Validate adaptive evidence flow |
| `K1` / `K5` / `K7` | `--K 1/5/7` | Vary rollout depth | Find optimal depth |
| `dinov2_ablation` | `--backbone dinov2` | DINOv2-L/14 backbone | DINOv3 vs DINOv2 |
| `mse_loss` | `--loss_fn mse` | MSE (L2) JEPA loss | Original I-JEPA loss |
| `cosine_loss` | `--loss_fn cosine` | Cosine similarity JEPA loss | Purist-style loss |
| `no_sigreg` | `--no_sigreg` | Disable SIGReg anti-collapse | Test regularization |
| `vicreg_only` | `--no_sigreg --use_vicreg` | VICReg only | Alternative anti-collapse |
| `purist` | `--purist` | DINOv3-B, K=5, Cosine+SIGReg | Isolate JEPA contribution |

---

## Project Structure

```
MR-JEPA/
├── README.md                    # This file
├── train_mrjepa.py              # Complete training script (CLI, all ablations)
├── test_architecture.py         # Architecture validation tests (synthetic data)
│
├── mr_jepa/
│   ├── __init__.py
│   ├── ARCHITECTURE.md          # Detailed architecture specification
│   │
│   ├── configs/
│   │   ├── __init__.py
│   │   └── model_config.py      # All hyperparameter dataclasses
│   │
│   ├── models/
│   │   ├── __init__.py
│   │   ├── mr_jepa.py           # Main model (integrates all components)
│   │   ├── backbones.py         # Visual (DINOv3/v2) + Text (Qwen3-Embedding)
│   │   ├── evidence_memory.py   # Perceiver Resampler multimodal fusion
│   │   ├── latent_rollout.py    # K-step shared predictor + evidence gates
│   │   ├── target_encoder.py    # EMA encoder + JEPA/SIGReg/VICReg losses
│   │   └── answer_heads.py      # Discriminative (MC) + Generative (open-ended)
│   │
│   ├── data/
│   │   ├── __init__.py
│   │   ├── unified_dataset.py   # 9-benchmark unified loader with format quirks
│   │   └── data_utils.py        # Collator, dataloader factory, benchmark configs
│   │
│   ├── training/
│   │   ├── __init__.py
│   │   ├── trainer.py           # 3-phase training loop
│   │   └── phase_scheduler.py   # Phase transitions, LR scheduling
│   │
│   ├── evaluation/
│   │   ├── __init__.py
│   │   └── metrics.py           # Accuracy, ANLS, VQA Acc, Relaxed Acc
│   │
│   └── utils/
│       ├── __init__.py
│       ├── visualization.py     # Trajectory PCA, gate analysis
│       └── ablation.py          # Systematic ablation runner
│
├── results/                     # Training results (auto-pushed)
│   ├── hybrid_main.json
│   ├── no_jepa.json
│   ├── no_rollout.json
│   └── ...
│
└── checkpoints/                 # Best model checkpoints (auto-pushed)
    ├── hybrid_main_best.pt
    └── ...
```

---

## Paper Contribution

> **A world model for multimodal reasoning**: We demonstrate that modeling the evolution of a latent belief state via JEPA-style prediction improves performance on static multimodal benchmarks compared to single-pass baselines. The evidence-gated rollout with K=3 steps learns meaningful intermediate reasoning states, validated through ablation studies across 9 benchmarks. The JEPA objective (not human chain-of-thought) supervises a latent trajectory generated by an EMA target encoder, showing that self-supervised dynamics training transfers to discriminative reasoning tasks.

---

## Key References

1. **I-JEPA** (Assran et al., 2023) — [arxiv:2301.08243](https://arxiv.org/abs/2301.08243): JEPA architecture, EMA target encoder, L2 prediction loss, narrow predictor
2. **LeWorldModel** (Maes et al., 2025) — [arxiv:2603.19312](https://arxiv.org/abs/2603.19312): SIGReg anti-collapse, end-to-end JEPA
3. **Coconut** (Yu et al., 2024) — [arxiv:2412.06769](https://arxiv.org/abs/2412.06769): Chain of Continuous Thought, latent reasoning
4. **DINOv3** (Meta, 2025) — [arxiv:2508.10104](https://arxiv.org/abs/2508.10104): Dense SSL with RoPE + Gram anchoring
5. **SoftCoT++** (Xu et al., 2025) — [arxiv:2505.11484](https://arxiv.org/abs/2505.11484): Soft chain-of-thought with contrastive learning

## License

Apache-2.0