NVIDIA Research introduced HORIZON, a hands-free agent framework for hardware design. It treats hardware design as repository-level code evolution. This research team exercises the register-transfer level (RTL) instantiation. A structured Markdown harness becomes a project pack. A self-contained agent loop then evolves an isolated git worktree. It commits a version only when an executable acceptance gate passes.
The research team reports 100% completion across every evaluated RTL benchmark suite. It also states plainly that agentic hardware design is not solved.
What is HORIZON?
Single-turn code generation has a clear limit on executable design tasks. Plausible Verilog is not enough for real hardware. Correctness depends on cycle-level behavior, reset conventions, bit widths, and simulator feedback.
HORIZON hosts each design problem as a version-controlled repository, not a one-shot prompt. The only required input is a structured Markdown harness. That harness carries four components: a goal, domain-knowledge directions, an evaluator specification, and an acceptance predicate.
A bootstrap agent compiles the harness into a project pack. The research team writes this as p = (πagent, Ep, Ap, Γp, Ωp). Those terms cover the agent policy, the executable evaluator, and the acceptance predicate. They also cover the version-control policy and the domain skills.
For RTL, the evaluator Ep may include compilation, simulation, coverage extraction, and assertion or testbench checks. In other domains, that same slot could hold unit tests, theorem provers, profilers, or synthesis tools. Problems are therefore defined over git worktrees, not over a fixed repository type.
https://arxiv.org/pdf/2606.28279
How the Repository-Level Loop Works
After bootstrap, the loop runs without further human input. Each cycle plans a target, edits the worktree, invokes tools, and runs the evaluator. The acceptance predicate then decides one thing: commit the new version, or log the failure.
Git is the substrate here, not incidental bookkeeping. Diffs expose proposed state changes. Commits define accepted checkpoints. Notes attach evaluator evidence. The log recovers the full trajectory.
The loop leans on native git commands to keep tracing cheap. Staged edits are inspected with git diff –cached. Each accepted attempt becomes a git commit whose notes carry the verdict and reward. Successful commits become positive repair examples. Rejected attempts are logged as negative examples. The repository history is the experience buffer, not a separate datastore.
The research team borrow semi-Markov decision process vocabulary for one narrow purpose. It names the recorded objects, nothing more. A ‘state’ is a versioned snapshot of the repository. An “option” is one episode between two checkpoints. HORIZON does not train or update an RL policy in this work. The agent backbone stays fixed throughout a campaign.
Session reuse keeps cost down. HORIZON holds a persistent model session across iterations. The harness, project pack, and stable sources are served from the provider’s prompt cache. Newly billed tokens are then dominated by the current diff and the latest evaluator output.
Where HORIZON Sits Among Self-Evolving Systems
HORIZON extends a lineage of repository-scale self-evolution. Earlier systems evolved the software that engineers run. HORIZON instead evolves the hardware artifacts that engineers create.
SystemObject evolvedDomainEvaluation signalAlphaEvolve (2025)Algorithmic kernelsScientific and algorithmic discoveryAutomated evaluatorsSATLUTION (2025)Full SAT-solver repositoriesSAT solvingDistributed correctness and runtimeABCEvo (2026)ABC logic-synthesis systemEDA softwareCorrectness and QoRHORIZON (this work)RTL sources, testbenches, verification artifactsHardware designCompile, simulate, coverage, assertion checks
All four share one principle. A candidate change is admitted only when executable evidence supports it.
Benchmark Results
The backbone is GPT-5.3, fixed for all experiments. Every result uses single-agent, hands-free mode. Campaigns ran on an AMD EPYC 9334 32-core host with 512 GB of RAM.
The evaluation spans ChipBench, RTLLM-2.0, and Verilog-Eval. It adds nine CVDP code- and verification-generation categories, CID 002 to 016. CVDP contains 783 human-authored problems across 13 task categories (Pinckney et al., 2025).
An iteration is one automated outer step. The agent edits the worktree, runs the evaluator, then commits a pass or logs a rejection. HORIZON reaches a 100% pass rate on every suite. The one residual miss is a ChipBench specification-harness defect, not an agent failure.
The aggregate first-iteration pass rate is 47.8%. Iteration-0 is not a standalone Pass@1 measurement. It is the repository state after the first agent iteration. The agent may defer debugging and repair to later iterations by design.
Suite / categoryFocusIter. 0Conv. iter.HORIZONChipBenchMixed RTL generation20.05100.0RTLLM-2.0NL spec to RTL78.02100.0Verilog-Eval-v2HDLBits-style Verilog86.22100.0CVDP CID 002RTL code completion3.282100.0CVDP CID 003NL spec to RTL19.224100.0CVDP CID 004RTL code modification10.936100.0CVDP CID 005Spec-to-RTL module reuse9.114100.0CVDP CID 007Linting / QoR improvement0.024100.0CVDP CID 012Test-plan to stimulus generation47.832100.0CVDP CID 013Test-plan to checker generation3.819100.0CVDP CID 014Test-plan to assertion generation79.11100.0CVDP CID 016Debugging and bug fixing25.713100.0
Convergence difficulty varies widely across categories. RTLLM-2.0 and Verilog-Eval reach 100% within two iterations. Checker generation (CID 013) starts at just 3.8%. Yet it climbs steadily to 100% by iteration 19, with almost no plateau. Code completion (CID 002) needs 82 iterations. Its long tail is the single largest token cost.
Interactive Metrics Explainer