DSL-Driven AI Interactions

• DSL-driven AI interactions are methods that use domain-specific languages to formalize and verify AI domain semantics, enabling automated code synthesis and rigorous error checking.
• They integrate model-driven engineering, formal verification, and generative AI to improve reliability and performance in complex, safety-critical systems.
• These interactions facilitate iterative refinement and governance, promoting rapid prototyping, enhanced compliance, and optimized resource allocation in AI workflows.

Domain-specific languages (DSLs) play a foundational role in structuring, verifying, and optimizing AI interactions across a diverse spectrum of software, modeling, dialogue, and multi-agent systems. In recent research, DSL-driven approaches enable precise specification of domain semantics, support automated code synthesis, facilitate formal verification, and empower granular governance of AI mechanisms. Modern DSL frameworks intertwine model-driven engineering, generative AI, multimodal requirement specification, and compliance enforcement, increasing rigor and transparency in AI-enhanced systems.

1. Approaches to DSL Construction and Semantic Transformation

Recent work on DSL construction emphasizes the necessity of starting with a target meta-model—a formal description of domain concepts—as the principal artifact (0801.1219). The DSL development process can be decomposed into two distinct transformations:

• Text-to-AST Transformation: Concrete syntax is parsed (e.g., via xText) into an AST that embodies syntactic structure but omits domain semantics.
• AST-to-Model Transformation: Explicit, rule-based AST transformations (e.g., class mapping and reference translation) yield a semantic domain model by resolving textual references, inheritance, and syntax-specific constructs.

This staged pipeline results in modular, verifiable DSLs whose semantic analysis becomes systematic and automatable. Lookup functions $L(\mathit{ASTRef}(r))$ resolve cross-referenced strings to object references at the semantic level:

$$\text{Target Model} = \mathcal{F}\Bigl(\text{AST}(\text{Text})\Bigr) \qquad L: \mathit{ASTRef} \rightarrow \text{ModelRef}$$

Trace data generated throughout guarantees reversible mapping and robust error reporting.

2. Formal Semantics, Verification, and Consistency Checking

Multiple research threads attest to the importance of formal semantics for DSL-driven AI systems (Andova et al., 2011, Keshishzadeh et al., 2015, Keshishzadeh et al., 2016). Semantics are often formalized via process algebras (e.g., mCRL2), and DSL specifications are mapped to formal languages supporting rigorous verification.

• Operational Semantics and Labeled Transition System (LTS) Generation: DSL models are translated into intermediate configuration/step languages, then mapped to LTSs amenable to model checking, reduction (e.g., branching bisimilarity, CADP/mCRL2 toolsets), and visualization.
• Equivalence Checking: Artifacts generated via different DSL transformations are compared via state-space equivalence (strong/branching bisimulation), ensuring that transformations preserve intended behavior.
• Model-based Testing: For executable models, ioco-style conformance checks generate test cases, detecting discrepancies between the formal model and its implementation.

This allows both simulation and verification artifacts to be independently validated against the DSL semantics, reducing correctness risks in safety-critical domains.

3. DSLs in Model-Driven Engineering and AI Workflows

A systematic review reveals that DSLs, when combined with model-driven engineering (MDE), are a pivotal mechanism for encapsulating and automating the AI software process (Raedler et al., 2023). DSLs define metamodels for representing structural and behavioral AI properties, and drive code generation via transformations $T: M \to \text{Code}$. MDE-driven DSLs excel at:

• Model Training and Deployment: DSLs capture model architectures, hyperparameters, training/evaluation workflows.
• Language Workbench Integration: Platforms (EMF, Xtext, WebGME, MontiCore) support DSL prototyping, editing, and code synthesis.
• Partial Lifecycle Coverage: Most DSLs focus on modeling, training, and evaluation phases (CRISP-DM); preparatory phases are less developed, inviting future work.

The review emphasizes challenges—such as DSL–code desynchronization and limited support for requirements elicitation—alongside the need for frameworks spanning the full AI lifecycle.

4. Generative AI and DSL-Driven Interactions

The emergence of generative models for DSL grammar synthesis (e.g., DSL Assistant) streamlines domain-specific language design and improves quality via error correction and iterative feedback (Mosthaf et al., 19 Aug 2024). The process is abstracted as a conditional sequence generation problem over the grammar space:

$$G^* = \operatorname{argmax}_{G' \in \Lambda_{DSL}} \prod_i P(g'_i | Q, g'_1, \dots, g'_{i-1})$$

Interaction modes encompass chat-based dialogue, editor-embedded refinement, and template-based hybrid editing, each allowing cycle-wise revision. Automatic repair reduces syntax error rates and user studies indicate rapid prototyping advantages. DSL quality depends on domain specificity, depth of interaction, and training regimen.

Dialog systems using instruction-following LLMs transform user utterances into executable DSL code, refining outputs via contextual chat and retrieval-augmented generation (Rubavicius et al., 13 Oct 2024). Dialogue-driven scenario generation significantly increases success rates—by up to 4.5×—compared to single-shot generation, demonstrating the importance of iterative natural language refinement for symbolic program synthesis in AI-centric domains.

5. DSLs for Performance Optimization and Resource Allocation

DSLs are instrumental for expressing optimization spaces in performance-critical AI contexts such as parallel program mapping and compiler construction (Wei et al., 21 Oct 2024, Walia et al., 2020). As featured in agent–system interfaces, DSLs condense low-level system choices into modular, high-level decisions (e.g., processor allocation, memory region selection, layout):

• Online Discrete Optimization: DSL-based mapping tasks are formulated as $$\theta^* = \operatorname{argmax}_{\theta \in \Theta} \text{Throughput}(\theta)$$.
• LLM-Optimizer Integration: LLMs propose DSL code modifications, receiving rich feedback (execution errors, performance metrics, guidance) from systems. AutoGuide modules interpret feedback, accelerating convergence over black-box techniques (e.g., OpenTuner).
• Performance Benchmarks: System finds mappers up to 1.34× faster than expert-written and achieves results in 10 iterations—outperforming methods requiring 1000 iterations.

Sham (Walia et al., 2020) further exemplifies high-performance DSL construction, combining macro-based syntax description, LLVM IR compilation, and FFI-based host language integration, facilitating incremental optimization and dramatic speedups (up to 20× for probabilistic programming).

6. Governance and Requirements Specification for AI-Internet and Multimodal Systems

DSLs like ai.txt (Li et al., 2 May 2025) provide element-level, semantically rich regulation for AI interactions with web content, extending robots.txt to encode both prohibitions (Disallow) and natural language behavioral guides (Guide). Regulatory scope encompasses HTML element-level instructions, image manipulations, and granular action types (Train, Summarize, Clip). Enforcement is achieved through:

• XML-Based Programmatic Integration: Deterministic parsing links DSL directives to agent execution.
• Prompt-Level Instruction: Natural language guidelines are injected into AI prompts, with empirical results indicating high compliance.

This mechanism enhances responsible AI engagement, mitigating copyright, semantic drift, and unauthorized usage.

MERLAN (Gomez-Vazquez et al., 20 Aug 2025) formalizes multimodal system requirements via explicit entity and modality declarations, Boolean composition of requirements, cardinality constraints, and an ANTLR grammar-based syntax. The toolchain parses DSL to AST, then generates actionable agent code (e.g., for smoke detection in images with confidence thresholds), bridging specification and execution. This direct formalization supports scalable, consistent, and adaptable AI-enhanced multimodal systems.

7. Visual Modeling, Feedback Loops, and Verification

Integrated modeling and visualization frameworks advance AI-assisted DSL design by providing graphical feedback, formal verification paths, and domain-specific modeling interfaces (Smyth et al., 5 Sep 2025). Key features include:

• Voice and Natural Language Input: DSL code is synthesized via spoken or typed prompts, then visualized as semantic diagrams.
• Tool API Integration: DSL grammar-derived APIs channel input into domain-constrained scaffolds, ensuring well-formed code.
• Visualization and Inspection: Automatic diagram synthesis (e.g., via KIELER/ELK) facilitates immediate error detection and semantic assessment.
• Formal Verification: Models are verified by model checking and automata learning, ensuring correctness in safety-critical applications.
• Iterative Feedback: Multi-stage refinement (from input transcription to code edit to compiler diagnostics) enables transparent correction cycles.

These mechanisms increase modeling reliability, support cross-domain migration (e.g., from Lingua Franca to SCCharts), and point to future advancements in tool chain automation, meta-tool generation, and comprehensive workflow integration.

---

DSL-driven AI interactions are characterized by their capacity to formalize semantics, enforce modular transformations, support verification, and optimize performance, all while providing comprehensive governance and requirements specification for modern AI systems. The ongoing convergence of statistical, symbolic, and rule-based approaches embedded in flexible, domain-specific languages is steadily reconfiguring the boundaries of AI system design and deployment.