Round-trip Engineering for Tactical DDD: A Constraint-Based Vision for the Masses

• URL: http://arxiv.org/abs/2603.26987v1
• Date: Fri, 27 Mar 2026 20:58:01 GMT
• Title: Round-trip Engineering for Tactical DDD: A Constraint-Based Vision for the Masses
• Abstract summary: We envision a constraint-based tactical modeling environment that transforms abstract architectural principles into explicit, tool-enforced engineering constraints.<n>This approach aims to democratize tactical DDD by embedding expert-level architectural knowledge directly into modeling constraints.
• Score: 4.3618460087630595
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Despite Domain-Driven Design's proven value in managing complex business logic, a fundamental semantic expressiveness gap persists between generic modeling languages and tactical DDD patterns, causing continuous divergence between design intent and implementation. We envision a constraint-based tactical modeling environment that transforms abstract architectural principles into explicit, tool-enforced engineering constraints. At its core is a DDD-native metamodel where tactical patterns are first-class modeling primitives, coupled with a real-time constraint verification engine that prevents architectural violations during modeling, and bidirectional synchronization mechanisms that maintain model-code consistency through round-trip engineering. This approach aims to democratize tactical DDD by embedding expert-level architectural knowledge directly into modeling constraints, enabling small teams and junior developers to build complex business systems without sacrificing long-term maintainability. By lowering the technical barriers to DDD adoption, we envision transforming tactical DDD from an elite practice requiring continuous expert oversight into an accessible engineering discipline with tool-supported verification.

Related papers

• A Model-Based Framework for Developing DTs in Industry 4.0 [0.671308473561404]
  Digital twins (DTs) provide detailed insights and support well-informed decision-making.<n>We propose a tool-supported framework that automates the derivation of DTs by leveraging existing structural and behavioral models of the physical twin.<n>These experiments illustrate the applicability of our framework for building DTs in contexts that satisfy our assumptions and requirements.
  arXiv  Detail & Related papers  (2026-07-03T08:11:40Z)
• Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy [79.09714980186278]
  Building persistent embodied agents in unstructured environments requires unified orchestration of heterogeneous tools.<n>We present OmniAct, a framework integrating a multimodal semantic planner for skill routing across unified action spaces.<n>It achieves consistent improvements in end-to-end success across all complexity levels.
  arXiv  Detail & Related papers  (2026-06-25T16:36:35Z)
• The Art of Interrogation: Consistency Amplifies Factuality in Spatial Reasoning [65.67266333751569]
  Current Large Reasoning Models (LRMs) exhibit remarkable general capabilities but significantly underperform in spatial reasoning tasks.<n>We propose a self-supervised reinforcement learning framework that targets the internal reasoning process without requiring ground-truth annotations.<n>We show that this label-free consistency training approaches the accuracy of models trained with ground-truth supervision and achieves similar generalization across diverse tasks and data domains.
  arXiv  Detail & Related papers  (2026-06-10T10:50:06Z)
• AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding [55.535374902204296]
  AffordanceVLA is a unified framework that introduces structured affordance forecasting as a task-oriented intermediate representation.<n>We show that AffordanceVLA achieves strong performance across diverse manipulation scenarios.
  arXiv  Detail & Related papers  (2026-06-04T13:28:51Z)
• Neither Layer Alone: Epistemic Integrity Requires Hierarchical Joint Design for Long-Running AI Agents [0.6381783966294292]
  Long-running AI agents fail when independently evolving model and harness layers change the semantics of belief, capability, and goal commitments across their boundary.<n>This paper argues that Agent Epistemic Integrity (AEI) must be treated as a first-class architectural constraint.
  arXiv  Detail & Related papers  (2026-06-01T00:56:23Z)
• Back to Parsimonious Latents: Learning Task-Centric World Models from Visual Foundations [35.521396983843864]
  We propose a framework for turning foundation-model embeddings into compact, task-sufficient world representations.<n> TC-WM linearly projects high-dimensional visual embeddings into a compact latent as the dynamic space.<n>We show that TC-WM suffices to identify the underlying task-centric latent factors up to a simple transformation.
  arXiv  Detail & Related papers  (2026-05-25T09:21:43Z)
• TDD Governance for Multi-Agent Code Generation via Prompt Engineering [2.013170973610094]
  Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipline in unconstrained circumstances.<n>We present an AI-native TDD framework that operationalizes classical TDD principles as structured prompt-level and workflow-level governance mechanisms.
  arXiv  Detail & Related papers  (2026-04-29T12:43:22Z)
• Top 10 Open Challenges Steering the Future of Diffusion Language Model and Its Variants [85.33837131101342]
  We propose a strategic roadmap organized into four pillars: foundational infrastructure, algorithmic optimization, cognitive reasoning, and unified multimodal intelligence.<n>We argue that this transition is essential for developing next-generation AI capable of complex structural reasoning, dynamic self-correction, and seamless multimodal integration.
  arXiv  Detail & Related papers  (2026-01-20T14:58:23Z)
• PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models [51.43746425777865]
  Large Language Models (LLMs) often lack the capacity to formulate global strategies, leading to error propagation in long-horizon tasks.<n>We propose PILOT, a framework designed to internalize the strategic oversight of large models into intrinsic Latent Guidance.
  arXiv  Detail & Related papers  (2026-01-07T12:38:56Z)
• AR-MOT: Autoregressive Multi-object Tracking [56.09738000988466]
  We propose a novel autoregressive paradigm that formulates MOT as a sequence generation task within a large language model (LLM) framework.<n>This design enables the model to output structured results through flexible sequence construction, without requiring any task-specific heads.<n>To enhance region-level visual perception, we introduce an Object Tokenizer based on a pretrained detector.
  arXiv  Detail & Related papers  (2026-01-05T09:17:28Z)
• LTD-Bench: Evaluating Large Language Models by Letting Them Draw [57.237152905238084]
  LTD-Bench is a breakthrough benchmark for large language models (LLMs)<n>It transforms LLM evaluation from abstract scores to directly observable visual outputs by requiring models to generate drawings through dot matrices or executable code.<n> LTD-Bench's visual outputs enable powerful diagnostic analysis, offering a potential approach to investigate model similarity.
  arXiv  Detail & Related papers  (2025-11-04T08:11:23Z)
• Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving [55.13109926181247]
  We introduce ReflectDrive, a learning-based framework that integrates a reflection mechanism for safe trajectory generation via discrete diffusion.<n>Central to our approach is a safety-aware reflection mechanism that performs iterative self-correction without gradient.<n>Our method begins with goal-conditioned trajectory generation to model multi-modal driving behaviors.
  arXiv  Detail & Related papers  (2025-09-24T13:35:15Z)
• Constrained Decoding for Robotics Foundation Models [12.916330118607918]
  We introduce SafeDec, a constrained decoding framework for autoregressive robot foundation models.<n>Task-specific safety rules are expressed as Signal Temporal Logic (STL) formulas and are enforced at inference time with minimal overhead.<n>Our method ensures that generated actions provably satisfy STL specifications under assumed dynamics at runtime without retraining.
  arXiv  Detail & Related papers  (2025-09-01T19:17:40Z)
• Modular Delta Merging with Orthogonal Constraints: A Scalable Framework for Continual and Reversible Model Composition [0.0]
  Existing approaches to model merging and continual learning often suffer from task interference, catastrophic forgetting, or lack of reversibility.<n>We propose Modular Delta Merging with Orthogonal Constraints (MDM-OC), a novel framework that enables scalable, interference-free, and composition of fine-tuned models.
  arXiv  Detail & Related papers  (2025-07-28T17:08:49Z)
• UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines [64.84631333071728]
  We introduce bfUnistage, a unified Transformer-based framework fortemporal modeling.<n>Our work demonstrates that a task-specific vision-text can build a generalizable model fortemporal learning.<n>We also introduce a temporal module to incorporate temporal dynamics explicitly.
  arXiv  Detail & Related papers  (2025-03-26T17:33:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.