Two-Stage Open-Source Pipeline

• Two-stage open-source pipeline is a modular framework that separates tasks into a coarse pre-processing stage and a fine-grained analysis stage.
• It enables targeted model optimization with explicit inter-stage interfaces, applied in NLP, computer vision, optimization, and more.
• Open-source implementations demonstrate state-of-the-art performance, reproducibility, and ease of community extension with documented code organization.

A two-stage open-source pipeline is a modular computational framework in which data or intermediate results are transformed successively by two distinct stages, typically designed for complementary subtasks, with explicit handoff and interface between the stages. This paradigm, exemplified by recent pipelines in natural language processing, computer vision, computational optimization, and software engineering, enables targeted model architectures, flexible adaptation, transparent evaluation, and domain extensibility. Published open-source implementations demonstrate that such pipelines achieve state-of-the-art empirical performance in domain-specific competitions, enable rigorous ablation of component effectiveness, and facilitate direct community reproducibility.

1. Fundamental Principles of Two-Stage Open-Source Pipelines

The two-stage architecture is characterized by a separation of task responsibilities across sequential modules. The first stage typically serves to reduce the solution space or perform coarse discrimination, while the second stage performs finer-grained analysis on the output of the first. This division can be instantiated in various domains:

• In sequence/label classification tasks, the first stage partitions the data or assigns coarse labels, enabling subsequent models to operate in a more homogeneous or constrained context (e.g., language identification followed by dialect identification).
• In computer vision and image analysis, initial segmentation or detection produces spatially localized or binary masks, subsequently enabling region-specific or instance-wise classification.
• In operations research, such as ambulance station optimization, the first stage solves a static or strategic allocation (e.g., station siting), while the second stage handles recourse or operationalized simulation under uncertain scenarios.
• In program analysis, an initial search space reduction (e.g., by filtering or ranking code methods with lightweight heuristics) precedes fine-grained re-ranking or inspection by computationally expensive models such as LLMs.

Open-source code releases accompanying these pipelines provide end-to-end scripts, modular code organization, configuration-driven experimentation, and documented dependency management, facilitating community review and domain adaptation.

2. Architectural and Algorithmic Patterns

The two-stage architecture is instantiated with domain-specialized models and clear inter-stage data flow. Selected representative architectures are summarized below:

Domain	Stage 1	Stage 2
Multilingual Dialect ID	Language ID (XLM-RoBERTa)	Dialect ID (per-language BERT/RoBERTa)
HRTEM Microscopy	U-Net Segmentation	Random Forest Defect Classification
Emergency Optimization	Strategic Ambulance Stationing (LP/IP)	Route Simulation and Calibration
Fault Localization	FL metrics + LLM search-space reduction	LLM-based re-ranking on candidates
Video Generation	LoRA style adaptation of backbone	Video decoding & temporal expansion

Each stage is governed by independent model architectures, loss functions, hyperparameters, and evaluation metrics. The intermediate representations (e.g., language/dialect prediction vector, segmentation mask, candidate code list) serve as computational interfaces.

Inter-stage dependencies and routing are explicit: for example, in dialect detection, language identification output determines which dialect model to apply; in fault localization, candidate rankings are passed to an LLM agent for double-check evaluation. This explicit modularity enables targeted model development and reasoning about stage-specific error contributions and resource constraints.

3. Mathematical Formulation and Core Algorithms

Each stage commonly embodies supervised or unsupervised learning, combinatorial optimization, or agent-based interaction, with well-defined mathematical descriptions. Examples include:

• Softmax classification with cross-entropy loss:

$$p = \mathrm{softmax}(W h(x) + b), \quad \mathcal{L}(\theta) = -\sum_{i=1}^C y_i \log p_i$$

• Aggregation and macro-averaged metrics (e.g., Macro-F1):

$$\mathrm{Macro\text{-}F_1} = \frac{1}{C} \sum_{c=1}^C \mathrm{F}_{1,c}$$

• Optimization under stochastic and robust programming:

$$\min_{x,y,z} \frac{1}{M} \sum_{m=1}^M \sum_{j\in J} z^m_j \quad \text{s.t. constraints}$$

• Low-Rank Adaptation (LoRA) for efficient fine-tuning:

$W' = W + \Delta W = W + BA$

where $$A \in \mathbb{R}^{r \times d}, B \in \mathbb{R}^{d \times r}, r \ll d$$.

• Scene-by-scene agent orchestration, e.g., dynamic function-call interaction in LLM-based fault localization.

Open-source implementations encode these algorithms using frameworks such as Hugging Face Transformers (NLP), PyTorch (CV/NLP), scikit-learn (ML), scikit-image (CV), Julia/JuMP/Gurobi (optimization), or custom orchestration for agent interaction with code repositories.

4. Training, Evaluation, and Performance Metrics

Each stage is trained and validated on pertinent data splits, with evaluation protocols aligned to task goals. Common practices include:

• Stratified data splits for class balance, with explicit reporting of sample counts and class distributions (e.g., dialect detection: strong class imbalance with EN-common 349 vs PT-BR 2724).
• Early stopping and checkpoint selection based on held-out development set macro-F1 (NLP, CV).
• Cross-validation or multi-scenario simulation for robust optimization (EMS planning).
• Comparative ablation (single-stage vs two-stage) to demonstrate empirical superiority. For instance, DR-Pose two-stage pipeline outperforms single-stage networks by +6.3 mAP@3D$_{75}$ on REAL275 (Zhou et al., 2023).
• Direct comparisons on open benchmarks with published baselines, including the use of domain-specific metrics (Dice for segmentation, Top-N/Mean Average Precision/F1 for classification/localization, FVD and LPIPS for video synthesis, mean response time in EMS).

Table: Example performance results from representative pipelines

Pipeline	Domain	Primary Metric(s)	Score(s)
Dialect Detection (Vaidya et al., 2023)	NLP	Macro-F1 (Track-1/2)	58.54% / 85.61%
HRTEM (Groschner et al., 2020)	Microscopy	Dice / Defect Acc.	0.80 / 86% (stacking fault)
OpenEMS (Ong et al., 2022)	EMS Planning	Mean resp. time (min)	−1.47 min vs baseline
FlexFL (Xu et al., 16 Nov 2024)	Fault Loc.	Top-1/Top-5 Bugs Found	350/529 vs. 167/389 (SBFL)
DR-Pose (Zhou et al., 2023)	6D Pose	mAP@3D$_{75}$, 5$^\circ$2cm	68.2%/41.7% (REAL275)
Cinematic I2V (Akarsu et al., 31 Oct 2025)	Video Gen.	FVD / LPIPS / CLIP-SIM	FVD −20%, LPIPS-0.142 (val plateau)

5. Codebase Organization and Reproducibility

Open-source codebases for two-stage pipelines expose modular folder structures, configuration files, and command-line entry points for each pipeline stage. For example:

• NLP dialect detection (Vaidya et al., 2023):
  • /data/ (raw, processed), /configs/ (per-stage configs), /scripts/ (train, inference, evaluation), /models/, /outputs/.
• HRTEM segmentation (Groschner et al., 2020):
  • /data/, /notebooks/ (end-to-end demo), /src/ (segmentation, preprocessing, postprocessing, classification), with modular replacement enabled.
• OpenEMS (Ong et al., 2022):
  • /python/ (data/build/calib), /julia/ (model builders, sim, runner), /notebooks/ (exploratory), /data/.
• DR-Pose (Zhou et al., 2023):
  • /models/ (completion, deformation, registration nets), /scripts/ (stage-specific training/eval), /utils/ (loss, viz), /requirements.txt.
• Video generation (Akarsu et al., 31 Oct 2025):
  • /data_preprocessing/, /training/, /inference/ (extraction, LoRA finetune, inference scripts).

Replication is supported by detailed instructions, pip/conda requirements, and pointer to external datasets (if applicable). By adhering to configuration-driven execution and modular code organization, these pipelines facilitate direct extension to new domains, ablation studies, or the insertion of alternate models at each pipeline stage.

6. Engineering Considerations, Limitations, and Extensibility

Key engineering strategies in successful two-stage open-source pipelines include:

• Explicit task decoupling to prevent loss/interference between disparate subtasks (e.g., deformation vs. registration in 6D pose estimation).
• Per-stage model selection by development set performance, enabling direct metric alignment with shared tasks.
• Use of parameter-efficient adaptation (LoRA) for stage-wise fine-tuning with minimal resource overhead.
• Robust handling of imbalanced or multi-modal data by stage-specific routing and modeling.
• Modular APIs and configuration files enabling the straightforward addition of new languages, classes, or defect types with minimal code duplication.

Limitations commonly arise from error propagation between stages: imperfect output at Stage 1 constrains Stage 2 ceiling performance (e.g., misclassified language ID yields unrouteable dialect inference). Extending pipelines to end-to-end joint optimization, multi-view or iterative refinement, or the insertion of alternative algorithmic modules (e.g., distributionally robust optimization, alternative classifiers) is facilitated by open-source release and documented modular design.

A plausible implication is that the two-stage open-source paradigm will remain a dominant and generalizable strategy where heterogeneous, multi-step, or high-variance tasks preclude single-model solutions, especially in research settings prioritizing reproducibility, extensibility, and community validation.