Coarse-to-Fine Data Generation Pipeline

• Coarse-to-fine autonomous pipelines are hierarchical systems that decompose data generation into sequential coarse stages for global structure and fine stages for detailed synthesis.
• The approach leverages computational efficiency and improved generalization by reducing redundancy and employing iterative bootstrapping across various modalities.
• Empirical evaluations demonstrate enhanced metrics in text, vision, trajectory, and molecular design, underscoring the paradigm’s versatility and robustness.

A coarse-to-fine autonomous data generation pipeline is an architectural paradigm for constructing high-fidelity synthetic datasets and structured generative models with strong support for multi-level hierarchies. The defining characteristic of this paradigm is the decomposition of data generation or curation into sequential “coarse” and “fine” stages, each with distinct representations, objectives, and learning strategies. Coarse stages typically establish global semantics or structure with tractable supervision or constraints, while fine stages recover detailed, local, or domain-specific properties—often with architectural, algorithmic, or optimization enhancements suited to their granularity. Such pipelines are central in scenarios where acquiring densely supervised fine-grained data is impractical, where privacy or distributional shifts dictate hierarchical modeling, or where computational scalability is contingent on efficiently partitioned workflows. Rigorous empirical evidence shows that coarse-to-fine pipelines are empirically superior to monolithic or single-stage alternatives across diverse modalities, including natural language, vision, spatio-temporal trajectories, and structure-based drug design.

1. Theoretical Foundations and Problem Setting

The theoretical underpinning of coarse-to-fine autonomous pipelines is grounded in the observation that many real-world data modalities—in text, vision, molecular or spatio-temporal domains—admit meaningful multi-level or hierarchical structure. The canonical formulation assigns coarse labels or latent structures at the upper levels of a tree $\mathcal{T}$ (e.g., topic, global style, scaffold, segment) with fine labels or detailed features (e.g., subtopic, lexical realization, atomic coordinates, GPS points) at lower levels (Mekala et al., 2021). For text and document classification, the operative scenario assumes fine-level labels $\mathcal{F}$ nested within coarse categories $\mathcal{C}$, with mappings $f^\uparrow: \mathcal{F} \to \mathcal{C}$ and $f^\downarrow: \mathcal{C} \to 2^\mathcal{F}$. In image generation, tokens or codewords $T_i$ are clustered into coarse classes $C_i$, reducing local redundancy and modeling expense (Guo et al., 20 Mar 2025). Recent work generalizes this to continuous trajectory synthesis and molecular design by first projecting signals to coarse latent spaces (segments, scaffolds, smoothed movement vectors) and then reconstructing fine-level instantiations (Guo et al., 8 Jul 2025, Xu et al., 9 Oct 2025, Xu et al., 14 Aug 2025).

A central insight is that the coarse-to-fine decomposition enables both (i) computational tractability (by shrinking vocabulary cardinalities, model bandwidth, or differentiable subspaces at each stage), and (ii) improved generalization (by regularizing global structure before local fine-tuning). Information-theoretic characterizations, such as PAC-Bayesian information bottleneck bounds, analytically demonstrate that reducing mutual information in bottlenecked coarse stages improves test risk and generalization under data scarcity (Xu et al., 14 Aug 2025).

2. Coarse Stage: Representation, Conditioning, and Early Filtering

The definition and implementation of the “coarse stage” vary by domain but universally seek to encode or filter for global, semantically salient structure. In weakly supervised text classification, this involves leveraging document coarse labels and prompting pre-trained LMs with label surface forms to enable conditional generation without fine-grained labels (Mekala et al., 2021). In autoregressive generative modeling for images, the coarse stage clusters high-dimensional codebook tokens into $M \ll K$ discrete labels using $k$-means over VQ-VAE embeddings. This step serves to regularize and reduce token redundancy, as codewords within a cluster are nearly interchangeable in their perceptual impact (Guo et al., 20 Mar 2025).

In synthetic trajectory generation, the coarse stage decomposes observed sequences into discrete road segments or temporally-regularized, continuous latent movement vectors via autoencoding or diffusion transformations. In cases such as “GeoGen,” this yields a regularly-sampled, sparsity-aware latent sequence, enabling statistically efficient learning via diffusion (Guo et al., 8 Jul 2025, Xu et al., 9 Oct 2025). Similarly, in molecular generation, masking strategies targeting scaffolds or side-chains explicitly control the granularity of preliminary structure, affecting information bottleneck density and downstream generative capacity (Xu et al., 14 Aug 2025).

In data curation, the coarse stage is instantiated as a cascade of automated quality scoring and filtering—across metrics such as aesthetics, OCR presence, temporal consistency, and physical constraints—implemented as concurrent, thresholded rejection processes (Tan et al., 28 Feb 2025). All pipelines in this class prioritize no-human-in-the-loop operation, relying on model-based or simple rule-based strategies at scale.

3. Fine Stage: Detailed Synthesis, Refinement, and Regularization

The fine stage introduces the detailed, context-specific transformations required to realize the full fidelity of the data. In text and QA pipelines, this may involve label-conditioned language generation and subsequent refinement using instruct-tuned LLMs, punctuated by quality filtering on logical, semantic, and domain-specific axes (e.g., RAGAS scoring) (Mekala et al., 2021, Shi et al., 30 Sep 2025). In vision pipelines, parallel or one-shot fine prediction is conditioned on the previously generated coarse structure; for instance, a transformer decodes the full set of fine tokens $T_i$ given the coarse cluster assignments $C$ (Guo et al., 20 Mar 2025).

Fine stages in trajectory and molecular pipelines typically employ U-Net architectures, diffusion models or gradient-based optimization with bespoke physical or spatial constraints. Architectures such as Coarse2FineNet incorporate advanced context fusion, multi-head decoders, and neural TPP heads to simultaneously recover spatial and temporal fine-grained properties (Xu et al., 9 Oct 2025). For molecular docking, fine positioning is formulated as six-dimensional rigid-body optimization under physics-based energy functions, efficiently solved via L-BFGS in under one second per molecule (Xu et al., 14 Aug 2025).

Both empirical and ablation studies emphasize the necessity of fine-stage architectural and regularization enhancements: hierarchy-aware margin-based regularizers in text (Mekala et al., 2021), road-connectivity losses and noise-augmented conditioning in mobility (Guo et al., 8 Jul 2025), and explicit embedding- or graph-based constraints in structured prediction.

4. Bootstrapping, Iteration, and Autonomous Orchestration

A key feature of contemporary coarse-to-fine pipelines is their iteration and bootstrapping capability. Outputs from the fine stage (e.g., pseudo-labelled data, scores, or refined sequences) are fed back into earlier stages to update supervision, seed subsequent generations, or inform hyperparameter selection. For instance, the Coarse2Fine (C2F) workflow in text classification leverages initial pseudo-data to train a classifier, whose confident predictions over unlabeled data are used to create improved weak sets for subsequent rounds of generator fine-tuning (Mekala et al., 2021). In QA generation pipelines, refinement and scoring feedback further filter and enhance output sets in sequential passes (Shi et al., 30 Sep 2025). This recursive improvement continues until no further benefit accrues or empirical metrics plateau, with studies showing two iterations typically suffice for weak–strong bootstrapping cycles.

Architectural orchestration is further streamlined via prompt-based interfaces or unified scripting layers, enabling efficient specification and autonomous execution of complex generation workflows—spanning scene building, object placement, augmentation layers, and label propagation (Sabet et al., 2022). Data orchestration is distributed (e.g., via Apache Airflow DAGs (Tan et al., 28 Feb 2025)), ensuring scalable, robust job management.

5. Quantitative Evaluation, Ablations, and Empirical Gains

Empirical validation of coarse-to-fine autonomous pipelines consistently demonstrates superior sample quality, annotation efficiency, and downstream task performance across domains. In text classification, the C2F pipeline delivers notable improvements in both Micro- and Macro-F1 (e.g., NYT Macro-F1: 87.01 vs. 81.09 for best baseline; 20News: 77.57 vs. 73.06) (Mekala et al., 2021). Ablation studies reveal that omission of hierarchy regularization drops performance by 1.5–5 F1 points, while skipping bootstrapping severely degrades fine-class accuracy.

In vision, coarse-to-fine clustering strategies improve both FID and Inception Score, often by significant margins (e.g., IS improvement = +59 for ImageNet; FID reduced from 3.39→2.76) while accelerating sampling throughput (Guo et al., 20 Mar 2025). The addition of fine post-processing and blending in synthetic image pipelines further boosts Mask AP and Box AP for segmentation and detection, and ablation confirms the necessity of each composition stage (Naumann et al., 2022). Multi-stage curation in video yields dramatic absolute improvements (FVD: 705→469), with semantic, temporal, and visual quality distributions favorably shifted after filtering (Tan et al., 28 Feb 2025).

In mobility and molecular generation, two-stage (latent/fine) pipelines consistently achieve lower JSD against real data for all relevant metrics (e.g., JSD-SD: 0.0043 vs. 0.0285; unique design counts up to 8× over baselines) and strong privacy–utility Pareto improvements using DP noise at various stages (Guo et al., 8 Jul 2025, Xu et al., 9 Oct 2025, Xu et al., 14 Aug 2025). For protein–ligand benchmarks, IBEX delivers state-of-the-art docking success and diversity without additional model complexity (Xu et al., 14 Aug 2025).

6. Extensions, Limitations, and Future Directions

The coarse-to-fine paradigm is strongly extensible: it generalizes to any data modality admitting hierarchical or multi-scale structure, including audio (waveforms→spectral details), video (scene→frame→pixel), trajectory (segment→GPS), or discrete token spaces (arbitrary VQ structures) (Castrejon et al., 2021, Guo et al., 20 Mar 2025). The effectiveness of cluster granularity (the “M” parameter), choice of latent embeddings, and balance between generic and domain-specific fine processing are all critical design levers. A plausible implication is that future pipelines will replace static clustering or masking with dynamically learned, end-to-end hierarchical representations adapting granularity to local data complexity (Guo et al., 20 Mar 2025).

Limitations highlighted in empirical studies include sensitivity to hyperparameter selection (e.g., cluster size, regularization; (Guo et al., 20 Mar 2025)), diminishing returns for excessively large auxiliary model components, and occasional imbalanced clusters or overfitting in under-regularized fine stages. Density-aware clustering and self-supervised hierarchy discovery are open areas for future research. Additionally, scenarios with non-natural or highly entangled hierarchical structure may require more sophisticated definitions of coarse and fine levels than currently supported.

7. Comparative Table: Pipeline Instances Across Domains

Domain/Task	Coarse Stage	Fine Stage
Text Classification	Label-conditioned LM w/ hierarchy loss	Synthetic fine-label generation + classifier bootstrapping (Mekala et al., 2021)
Image Generation	Token clustering (k-means over codebook)	Parallel fine-token prediction conditioned on coarse clusters (Guo et al., 20 Mar 2025)
Trajectory Synthesis	Latent segment encoding + diffusion	GPS-level U-Net over fine-grained positions (Guo et al., 8 Jul 2025, Xu et al., 9 Oct 2025)
Molecule Design	Scaffold-hopping masking + SE(3) diffusion	L-BFGS pose optimization via physics-based energy (Xu et al., 14 Aug 2025)
QA/LLM Data	Retrieval from domain graph + base gen	Instruct-tuned refinement + RAGAS filtering (Shi et al., 30 Sep 2025)
Video Curation	Aesthetic/motion/temporal thresholding	Vision-language re-captioning + LLM filter (Tan et al., 28 Feb 2025)

Each pipeline demonstrates the systematic partitioning of structure (coarse) and detail (fine), autonomous iteration, and measurable improvements on benchmark data, attesting to the broad relevance and efficacy of the coarse-to-fine paradigm.