BRHC: Backend-driven Reactive Hypermedia Controls with a Statically Typed Kotlin DSL
- URL: http://arxiv.org/abs/2607.29338v1
- Date: Fri, 31 Jul 2026 12:17:19 GMT
- Title: BRHC: Backend-driven Reactive Hypermedia Controls with a Statically Typed Kotlin DSL
- Abstract summary: We propose a statically typed approach using a Kotlin-based HTML DSL for backend-driven reactive web applications.<n>We demonstrate the approach through a catalog of reactive interaction patterns and a Petclinic Spring MVC case study.
- Score: 37.173568883543986
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: AI-assisted coding tools (e.g., Copilot, Cursor, Claude) are increasingly ubiquitous and enable rapid generation of web applications. However, this raises concerns regarding complexity, longevity and the long-term maintainability of generated systems. A key source of complexity is the heterogeneity between backend and frontend programming models, where multiple languages and paradigms are combined within a single application, often leading to duplicated logic and fragmented state management. To address this issue, recent approaches (e.g., HTMX, Turbo Hotwire, Datastar, etc.) follow the Hypermedia-Driven Application (HDA) model, positioning HTML as the primary communication medium between client and server. Unlike SPA-centric architectures, HDA systems shift the application state and interaction logic to the server, where backend-driven reactive signals synchronize with the client user interface. However, these approaches still introduce complexity through custom attributes and do not fully eliminate JavaScript, particularly in computed expressions. In this work, we propose a statically typed approach using a Kotlin-based HTML DSL (Domain-Specific Language) for backend-driven reactive web applications. We extend the HtmlFlow Kotlin DSL with typed custom HTML attributes (i.e., Datastar data-* attributes) and signal-based bindings using statically typed builders. We demonstrate the approach through a catalog of reactive interaction patterns and a Petclinic Spring MVC case study. The results indicate that the proposed approach can nearly eliminate the need for JavaScript while improving type safety and preserving a homogeneous programming model across frontend and backend, bridged through a backend-driven reactive, signal-centric architecture.
Related papers
- Beyond Domains: Reusing Web Skills via Transferable Interaction Patterns [68.0603867264595]
SkillMigrator learns reusable web skills and transfers them across sites by matching layout structure rather than specific element references.<n>It reduces the average LLM-action count on successful trajectories by 8-10% across both WebArena and Mind2Web at matched success rate.
arXiv Detail & Related papers (2026-06-16T08:04:35Z) - MODF-SIR: A Multi-agent Omni-modal Distilled Framework for Social Intelligence Reasoning [80.41987492855971]
We propose a multi-agent collaborative framework built upon a lightweight Multimodal Large Language Model (MLLM)<n>Within this architecture, multi-modal data pertinent to social intelligence is precisely localized.<n>With around 30% of training data from IntentTrain, we achieve state-of-the-art results.
arXiv Detail & Related papers (2026-06-10T12:44:02Z) - WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testing [57.7131457251794]
We introduce WebTestBench, a benchmark for evaluating end-to-end automated web testing.<n>We decompose the testing process into two cascaded sub-tasks, checklist generation and defect detection, and propose WebTester.<n>Our findings expose a substantial gap between current computer-use agent capabilities and industrial-grade deployment demands.
arXiv Detail & Related papers (2026-03-26T09:27:29Z) - EmbeWebAgent: Embedding Web Agents into Any Customized UI [3.034887612600091]
We present EmbeWebAgent, a framework for embedding agents directly into existing UIs.<n>It supports mixed-granularity actions ranging from primitives to higher-level composites.<n>Our demo shows minimal retrofitting effort and robust multi-step behaviors grounded in a live UI setting.
arXiv Detail & Related papers (2026-02-16T15:59:56Z) - WebVIA: A Web-based Vision-Language Agentic Framework for Interactive and Verifiable UI-to-Code Generation [30.193562985137813]
We propose WebVIA, the first agentic framework for interactive UI-to-Code generation and validation.<n>The framework comprises three components: 1) an exploration agent to capture multi-state UI screenshots; 2) a UI2Code model that generates executable interactive code; 3) a validation module that verifies the interactivity.
arXiv Detail & Related papers (2025-11-09T06:58:52Z) - From OCL to JSX: declarative constraint modeling in modern SaaS tools [3.1662160826016765]
JSX is a declarative, functional subset of JavaScript/TypeScript used in the React ecosystem.<n>OCL.js, a JavaScript-based implementation of the Object Constraint Language, offers a web aligned approach to model validation.<n>We show JSX provides broader and better fits front-end-first architectures, indicating a promising path for constraint specification in modern modeling tools.
arXiv Detail & Related papers (2025-09-22T11:37:15Z) - ScreenCoder: Advancing Visual-to-Code Generation for Front-End Automation via Modular Multimodal Agents [40.697759330690815]
ScreenCoder is a modular multi-agent framework that decomposes the task into three interpretable stages: grounding, planning, and generation.<n>By assigning these distinct responsibilities to specialized agents, our framework achieves significantly higher robustness and fidelity than end-to-end approaches.<n>Our approach achieves state-of-the-art performance in layout accuracy, structural coherence, and code correctness.
arXiv Detail & Related papers (2025-07-30T16:41:21Z) - LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues [59.12542274007847]
LoopServe is an adaptive dual-phase inference framework for large language models in multi-turn dialogues.<n>LoopServe consistently achieves superior effectiveness compared to existing baselines.
arXiv Detail & Related papers (2025-07-18T06:12:08Z) - Improving Front-end Performance through Modular Rendering and Adaptive Hydration (MRAH) in React Applications [0.0]
We propose a pattern combining a modular rendering pipeline with an adaptive hydration strategy to optimize performance in React and Next.js applications.<n>The approach breaks the interface into distinct modules that can be rendered and hydrated independently.<n>By reducing the amount of JavaScript executed on page load and by scheduling work intelligently, this architecture aims to improve key performance metrics.
arXiv Detail & Related papers (2025-04-04T19:11:30Z) - Online Joint Fine-tuning of Multi-Agent Flows [12.851745991007169]
I describe a procedure for online joint fine-tuning of an entire flow inspired by the Learning to Search framework.
The approach leverages simulator access to reduce preferences over entire episodes to preferences over individual node outputs.
I apply to the multi-hop QA dataset Musique achieving a state-of-the-art result.
arXiv Detail & Related papers (2024-06-06T21:21:03Z) - Multi-Behavior Sequential Recommendation with Temporal Graph Transformer [66.10169268762014]
We tackle the dynamic user-item relation learning with the awareness of multi-behavior interactive patterns.
We propose a new Temporal Graph Transformer (TGT) recommendation framework to jointly capture dynamic short-term and long-range user-item interactive patterns.
arXiv Detail & Related papers (2022-06-06T15:42:54Z)
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.