CLOSER-Bench: Evaluating Budgeted Cross-Stage Design Closure for Hardware Agents
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Hardware engineering exposes coding agents to a form of long-horizon work that is difficult to capture with pass-at-k: progress is continuous, tool feedback is delayed and heterogeneous, and a backend failure may require revising RTL rather than tuning another physical-design parameter. Existing benchmarks measure RTL generation, repository repair, verification, PPA evolution, or physical implementation, but their different designs and oracles make it hard to determine where an agent succeeds or fails across abstraction boundaries. We introduce CLOSER-Bench, a controlled evaluation protocol for budgeted cross-stage design closure. For one design and one hidden objective, it pairs spec-to-RTL, RTL-to-GDS, and spec-to-GDS tasks, records every simulator, synthesis, STA, and place-and-route invocation, and measures final quality, anytime progress, tool cost, and cross-stage recovery. The benchmark is built on open-source Verilator, Yosys, OpenROAD, KLayout, Sky130, and the Harbor agent harness. A ten-task pilot spanning RTL repair, mutation-based verification, coverage, PPA optimization, design-space exploration, cross-model debugging, and security establishes the executable harness and exposes a sharp completion--closure gap: three agents solve a localized AXI repair task, while the matched verification-closure task separates a frontier agent from two otherwise successful baselines. We further validate a full RTL-to-GDS flow and construct a macro-based AXI/DMA streaming accelerator for the stage-paired evaluation. These results motivate treating hardware closure as a budgeted sequential decision problem rather than a collection of independent code generation tasks.
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 11 of its 14 references that are in the map. First posted on arXiv 18 Jul 2026.
Builds on
11 of 14 references.
Closest ancestors · 5
Same direction, newest first
- 2026Agentic Hardware Design as Repository-Level Code Evolution26 Jun
- 2026Is Agentic AI Ready for Real-World Hardware Engineering? A Deep Dive with Phoenix-bench13 May
- 2026HWE-Bench: Benchmarking LLM Agents on Real-World Hardware Bug Repair Tasks16 Apr
- 2025ORFS-agent: Tool-Using Agents for Chip Design Optimization10 Jun
- 2023ChatEDA: A Large Language Model Powered Autonomous Agent for EDA20 Aug
Other references · 6
- 2023VerilogEval: Evaluating Large Language Models for Verilog Code Generationcited by 246
- 2026ChipBench: A Next-Step Benchmark for Evaluating LLM Performance in AI-Aided Chip Designcited by 7
- 2026Design Conductor: An agent autonomously builds a 1.5 GHz Linux-capable RISC-V CPUcited by 4
- 2026CktEvo: Repository-Level RTL Code Benchmark for Design Evolutioncited by 3
- 2026HSCO-Bench: An Agent-Driven End-to-End Hardware-Software Co-design Benchmark for Systems-on-Chipcited by 1
- 2026HighTide: An Agent-Curated Open-Source VLSI Benchmark Suitecited by 1
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- 2025Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification
- 2025DUET: Agentic Design Understanding via Experimentation and Testing
- 2026ACE-RTL: When Agentic Context Evolution Meets RTL-Specialized LLMs
- 2026SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks
- 2026Clover: A Neural-Symbolic Agentic Harness with Stochastic Tree-of-Thoughts for Verified RTL Repair
- 2026Veri-Sure: A Contract-Aware Multi-Agent Framework with Temporal Tracing and Formal Verification for Correct RTL Code Generation
- 2026From Dead Code and Static Requirements to Working Engines: Software Revival with Coding Agents