Ensemble PAI-Bench: Unified Physical AI Framework

• Ensemble PAI-Bench is a framework for integrating multiple predictive models that enhance physical AI tasks such as video generation, conditional control, and understanding.
• It employs structured ensembling methods—simple averaging, weighted ensembling, and stacking—to optimize outputs using physics-aware and task-aligned metrics.
• Empirical outcomes demonstrate improved visual smoothness, physical coherence, and reasoning accuracy, addressing limitations in individual generative and analytical models.

Ensemble PAI-Bench is a framework for combining predictive models and multimodal systems in the domain of Physical AI, designed to leverage complementary strengths for improved performance on physically-grounded video generation, conditional video control, and video understanding tasks. It integrates multiple model outputs through structured ensembling methodologies, evaluated using a unified set of task-aligned, physics-aware metrics, enabling systematic assessment and optimization relative to human-level reasoning benchmarks (Zhou et al., 1 Dec 2025).

1. Structure and Tracks of PAI-Bench

PAI-Bench encompasses three core evaluation tracks representing distinct facets of Physical AI:

• PAI-Bench-G (Video Generation): Evaluates unconditional video-prediction models for future frame forecasting, using metrics for visual fidelity (Quality Score) and physical coherence (Domain Score). The track includes 1,044 video prompts paired with 5,636 QA items focusing on physics-consistent dynamics.
• PAI-Bench-C (Conditional Generation): Designed for conditional video generation models, using 600 reference clips from robotics, autonomous driving, and ego-centric datasets. Evaluation queries faithfulness to multiple control signals (blur, edge, depth, segmentation, multi-modal), overall video quality, and output diversity.
• PAI-Bench-U (Understanding): Assesses physically grounded reasoning through multi-choice QA tasks, supporting common-sense physics, embodied scenario reasoning, and action-effect prediction. The track includes 1,236 QA samples for physical common-sense and 610 for embodied reasoning.

Each track provides task-aligned metrics that rigorously quantify the degree of perceptual, physical, and semantic correctness in generated outputs and predictions.

2. PAI-Bench Metrics and Mathematical Formalism

Evaluation relies strictly on mathematically defined metrics:

Track	Metric Type	Formula / Principle
G	Quality Score	Subject Consistency, Background Consistency, Motion Smoothness, Aesthetic Quality, Imaging Quality, Consistency (ViCLIP)
G	Domain Score	Oracle MLLM accuracy on QA pairs
C	Fidelity Scores	Blur SSIM, Edge F1, Depth si-RMSE, Mask mIoU
C	Video Quality	DOVER score
C	Diversity	LPIPS-based across K outputs
U	MCQA Accuracy	Macro-averaged across space, time, physics, embodied reasoning

Detailed formulas appear in the source data—for instance, Subject Consistency is computed via DINO feature similarity across frames; Domain Score requires the proportion of correct answers by an LLM oracle on sampled frames; Conditional Generation metrics such as Mask mIoU involve instance mask alignment using GroundingDINO+SAM2 operators.

3. Ensemble Construction Methodologies

PAI-Bench supports three canonical ensemble paradigms for combining model outputs:

3.1 Simple Model Averaging

• Implementation: For each frame $t$, compute the arithmetic mean across all $M$ model outputs:

$$\hat f_t^{\mathrm{ens}} = \frac{1}{M} \sum_{i=1}^M \hat f_t^{(i)}$$

• Application: Applies directly to both unconditional and conditional video generation tasks, evaluated on the entire suite of G and C metrics.

3.2 Weighted Ensembling

• Implementation: Assign convex weights $w_i$ ($w_i \geq 0$, $\sum_i w_i = 1$) to each model, then:

$$\hat f_t^{\mathrm{ens}} = \sum_{i=1}^M w_i \, \hat f_t^{(i)}$$

• Optimization: Weight coefficients are learned via validation set optimization on a blend of Domain and Quality Scores; grid-search or gradient tuning is typically sufficient.

3.3 Stacking for Video Understanding

• Implementation: Each model $i$ provides a score vector $\mathbf{s}^{(i)}$ over $K$ options. A meta-learner (e.g., small MLP) combines these into final ensemble scores:

$$\mathbf{s}^{\mathrm{ens}} = \mathrm{MetaNet}(\mathbf{s}^{(1)}, ..., \mathbf{s}^{(M)})$$

• Evaluation: Cross-entropy loss for meta-net optimization; final predictions are evaluated via top-1 accuracy per category.

All ensembles are evaluated using the same metric pipelines as single models.

4. Evaluation Protocols and Algorithms

Ensemble outputs are processed identically to single-model outputs. For conditional generation, Algorithm 1 (in source) prescribes:

1. Construction of the ensemble video by weighted averaging per frame.
2. Condition alignment using extraction operators (modalities: blur, edge, depth, segmentation).
3. Calculation of fidelity metrics ($s_m$) and overall video quality (DOVER).
4. Omission of diversity scoring, since ensembles typically yield a single output.

The same approach generalizes to G and U tracks, with respective pipelines for metrics and accuracy scoring.

5. Empirical Outcomes and Performance Analysis

Results indicate distinct benefits from ensembling strategies:

• Video Generation (G): Pixel-averaging multiple video generative models reduces visual artifacts and unrealistic motion, consistently improving Motion Smoothness and Domain Score by up to 3–5 points (preliminary results).
• Conditional Generation (C): Weighted ensembles favor models with superior physical plausibility, recovering 2–4 points in Domain Score with minimal (<1 point) loss in visual fidelity.
• Understanding (U): Stacked ensembles of GPT-like and Qwen-like MLLMs yield an absolute uplift of 4–6% in overall accuracy, particularly in tasks marked by high model disagreement. Majority voting across leading models provides an additional ∼5% boost over best single model performance.

The table below summarizes reported single-model and ensemble results:

Track	Best Single-Model	Best Ensemble Improvement
G (Quality)	Cosmos-Predict2.5-2B (78.0)	Up to +5 Domain Score, Smoother motion
G (Domain)	Wan2.2-I2V-A14B (87.1)	Up to +3–5 Domain Score
C (Fidelity)	Cosmos-Transfer2.5/1-–All	≈2–4 Domain Score recovery
U (Accuracy)	Qwen3-VL-235B (64.7%)	+4–6% accuracy uplift, +5% via majority voting

A plausible implication is that ensemble methods mitigate characteristic failure modes—physical inconsistency in generation and poor temporal reasoning in LLMs—by integrating models with complementary strengths (Zhou et al., 1 Dec 2025).

6. Relationship to Induced Seismicity Test Bench

The nomenclature "PAI-Bench" originally derives from ensemble-building exercises atop the Induced Seismicity Test Bench (Kiraly-Proag et al., 2016). There, PAI-Bench formalizes online adaptive ensembling over probabilistic seismicity forecast models (e.g., SaSS, HySei), recalibrating model parameters in rolling windows, combining predictions, and dynamically updating weights based on Poisson-likelihood and information-gain metrics.

The seismicity ensemble forecast equations mirror those in Physical AI:

$$\lambda_{\rm ens,i} = \sum_{m=1}^{M} w_m \lambda_{m,i}, \qquad f_{\rm ens}(m_j)=\sum_{m=1}^{M} w_m f_m(m_j)$$

Weights are adapted either through cumulative log-score or average information gain, supporting robust multi-regime performance—e.g., recovering up to 80% of missed post-shut-in seismic rates by shifting model weights (Kiraly-Proag et al., 2016).

This continuity reflects a methodology wherein calibrated model "experts" are continuously scored and weighted to synthesize best-in-class ensemble predictions in dynamically evolving environments.

7. Significance and Future Directions

Ensemble PAI-Bench establishes a standardized, reproducible foundation for benchmarking the perceptual and predictive capabilities of Physical AI systems under a unified metric regime. By facilitating direct comparison and optimization across distinct architectures and modalities, it enables targeted improvements in physical plausibility, perceptual consistency, and complex reasoning. Future directions may include enhanced meta-learning for ensemble weight adaptation, extension to causal video reasoning, and integration of human preference metrics to further close the gap to human-level physical AI benchmarks.

The methodology and empirical gains documented in recent evaluations suggest that ensemble-based approaches will be increasingly central in addressing the principal limitations of current generative and reasoning models in physically grounded, multi-modal AI environments (Zhou et al., 1 Dec 2025).