mlsanityData to 14 Oct 2026

ContractHIL-HLS: Contract-Aligned Multi-Agent Workflow with Hardware-in-the-Loop Feedback for HLS Design

arXiv 2607.25283 ↗First posted 28 Jul 2026cs.AI cs.AR cs.CRcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

This paper presents ContractHIL-HLS, a contract-aligned multi-agent workflow for practical high-level synthesis (HLS) engineering. The workflow makes three contributions. First, it introduces a structured contract as the semantic-alignment and task-execution artifact that translates natural language requirements into explicit interfaces, constraints, validation checks, and rollback rules. Second, it incorporates hardware information into the feedback loop by feeding HLS, Vivado, PYNQ runtime, power, and failure evidence back into generation, thereby extending LLM-assisted HLS from kernel code toward system- and board-level closure. Third, it decomposes agents by semantic lowering and execution tasks rather than by conversational roles: a Contract Agent lowers natural language into the contract, an HTML Agent renders the contract as persistent structured HTML, and a Hardware-in-the-Loop Agent implements and revises the design with measured evidence. We evaluate ContractHIL-HLS in two parts. On 94 locally executable HLS-Eval tasks, the structured contract provides the largest small design gain, improving the estimated single-sample testbench pass rate from 64.0% to 70.2%; the full flow reaches 70.4% pass@1 and 76.6% pass@5. Because HLS-Eval does not exercise board-level design, we also validate ContractHIL-HLS on a board tested ML-KEM/ML-DSA post-quantum cryptography (PQC) secure-message accelerator, where the retained dual-bitstream organization reduces six-message average text runtime from 207.3 ms to 52.4 ms with positive routed WNS on both images while preserving decrypted-message verification. We open-source our work at BJUT-CS316-LAB/ContractHIL-HLS (https://github.com/BJUT-CS316-LAB/ContractHIL-HLS).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 19 references that are in the map. First posted on arXiv 28 Jul 2026.

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14 of 19 references.

References · 14

  1. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  2. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 619
  3. 2023VerilogEval: Evaluating Large Language Models for Verilog Code Generationcited by 252
  4. 2023VeriGen: A Large Language Model for Verilog Code Generationcited by 200
  5. 2023ChipNeMo: Domain-Adapted LLMs for Chip Designcited by 174
  6. 2023RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Modelcited by 148
  7. 2023Chip-Chat: Challenges and Opportunities in Conversational Hardware Designcited by 140
  8. 2023ChipGPT: How far are we from natural language hardware designcited by 105
  9. 2025HLS-Eval: A Benchmark and Framework for Evaluating LLMs on High-Level Synthesis Design Taskscited by 10
  10. 2025SAGE-HLS: Syntax-Aware AST-Guided LLM for High-Level Synthesis Code Generationcited by 9
  11. 2025HLStrans: Dataset for C-to-HLS Hardware Code Synthesiscited by 3
  12. 2023PQC-HA: A Framework for Prototyping and In-Hardware Evaluation of Post-Quantum Cryptography Hardware Acceleratorscited by 1
2 more
  1. 2023KyberMat: Efficient Accelerator for Matrix-Vector Polynomial Multiplication in CRYSTALS-Kyber Scheme via NTT and Polyphase Decompositioncited by 1
  2. 2023KiD: A Hardware Design Framework Targeting Unified NTT Multiplication for CRYSTALS-Kyber and CRYSTALS-Dilithium on FPGAcited by 1

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  3. 2026HLSFactory-Agent: Large-Scale Agentic HLS Dataset Construction from Academic and Open-Source Projects
  4. 2026AgRefactor: Self-Evolving Agentic Workflow for HLS Compatibility and Performance
  5. 2025Automated Multi-Agent Workflows for RTL Design
  6. 2026Shift-Left High-Level Synthesis Verification via Knowledge-Augmented LLM Agent ↗
  7. 2024SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generation
  8. 2024Agentic-HLS: An agentic reasoning based high-level synthesis system using large language models (AI for EDA workshop 2024)