SeqFeed: Improving Agentic RTL Code Generation with Sequential Behavior Feedback
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
RTL code generation is a critical stage in hardware design, and the emergence of agentic systems offers new opportunities to automate this process. To generate correct RTL code, agents must understand sequential behavior, including how signals evolve and propagate over multiple clock cycles. However, effectively conveying such temporal information to agents remains a significant challenge. RTL code does not expose cycle-level signal behavior for a specific execution, whereas full simulation waveforms are too voluminous and noisy for effective LLM analysis. To address these limitations, we study how human engineers reason about sequential behavior and identify three requirements for effective feedback: it should be event-addressable, dependency-traceable, and iteratively-queryable. Guided by these requirements, we propose textitSeqFeed, which comprises two complementary mechanisms: (1) textitSeQuery, an SQL-like waveform query language that enables agents to anchor queries to semantic events and sample signal values at relative time points; and (2) textitSeGraph, a dependency graph that tracks signal propagation across clock cycles. Experimental results across multiple LLMs demonstrate the effectiveness of SeqFeed in improving pass rates. SeQuery and SeGraph are each effective independently and provide complementary benefits when used together.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 16 of its 22 references that are in the map. First posted on arXiv 9 Aug 2026.
Builds on
16 of 22 references.
Closest ancestors · 2
Same direction, newest first
- 2026Dr. RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement16 Apr
- 2024MAGE: A Multi-Agent Engine for Automated RTL Code Generation10 Dec
Other references · 14
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
- 2023Teaching Large Language Models to Self-Debugcited by 963
- 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
- 2023VerilogEval: Evaluating Large Language Models for Verilog Code Generationcited by 246
- 2023VeriGen: A Large Language Model for Verilog Code Generationcited by 197
- 2023AutoChip: Automating HDL Generation Using LLM Feedbackcited by 80
- 2023LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automationcited by 40
- 2025A Survey of Research in Large Language Models for Electronic Design Automationcited by 38
- 2024MEIC: Re-thinking RTL Debug Automation using LLMscited by 25
- 2024Automatically Improving LLM-based Verilog Generation using EDA Tool Feedbackcited by 18
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- 2026VeriTrace: Human-Like Temporal Exploration Completes Agentic Action Space
- 2025DUET: Agentic Design Understanding via Experimentation and Testing
- 2025PEFA-AI: Advancing Open-source LLMs for RTL generation using Progressive Error Feedback Agentic-AI
- 2026TicTacBench: Benchmarking Timing Closure Capabilities of Coding Agents
- 2026ChipCraftBrain: Validation-First RTL Generation via Multi-Agent Orchestration
- 2026VeriAgent: A Tool-Integrated Multi-Agent System with Evolving Memory for PPA-Aware RTL Code Generation
- 2025RTLSquad: Multi-Agent Based Interpretable RTL Design