{"paper":{"title":"Is Agentic AI Ready for Real-World Hardware Engineering? A Deep Dive with Phoenix-bench","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Software-tuned AI agents struggle with hardware engineering because bugs propagate through signal flows across instantiated modules rather than along call graphs.","cross_cats":["cs.AI","cs.SE"],"primary_cat":"cs.AR","authors_text":"Bingsheng He, Feng Yu, Hongshi Tan, Qingyun Zou, WengFai Wong","submitted_at":"2026-05-13T14:14:54Z","abstract_excerpt":"We ask whether agentic AI systems built for software engineering transfer to realistic hardware engineering. Existing hardware LLM benchmarks isolate sub-tasks but none jointly requires repository navigation, hierarchy-aware localization, Electronic Design Automation (EDA) executable verification, and maintenance-style patching. We introduce \\textbf{Phoenix-bench}, a synchronized corpus of 511 verified Verilator instances from 114 GitHub repositories, each shipped with the developer patch, design-flow labels, fail-to-pass and pass-to-pass testbenches, and a Docker-pinned EDA environment so res"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Software and hardware are fundamentally different engineering tasks: the same agent loses 37% to 58% from SWE-bench Verified to Phoenix-bench because hardware bugs propagate across parallel instantiated modules through signal flow rather than along a software-style call graph, and software-tuned agents stop at the symptom file instead of tracing back through the instantiation chain.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The 511 instances drawn from 114 GitHub repositories, together with their developer patches and testbenches, form a representative sample of real-world hardware engineering work that requires repository navigation, hierarchy-aware localization, EDA verification, and multi-file patching.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Phoenix-bench shows agentic AI systems lose 37-58% resolved rate when moving from SWE-bench Verified to hardware tasks because bugs spread across parallel modules via signal flow, with testbench feedback lifting performance by 42-45% while file-level oracles add only 1.4%.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Software-tuned AI agents struggle with hardware engineering because bugs propagate through signal flows across instantiated modules rather than along call graphs.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d89d35a0c32a3b758f70b5086df730f87f6da4bc990be0dcfa7be1400611d24b"},"source":{"id":"2605.15226","kind":"arxiv","version":1},"verdict":{"id":"8abfb133-075e-407c-af20-a15097023b61","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T17:44:20.485864Z","strongest_claim":"Software and hardware are fundamentally different engineering tasks: the same agent loses 37% to 58% from SWE-bench Verified to Phoenix-bench because hardware bugs propagate across parallel instantiated modules through signal flow rather than along a software-style call graph, and software-tuned agents stop at the symptom file instead of tracing back through the instantiation chain.","one_line_summary":"Phoenix-bench shows agentic AI systems lose 37-58% resolved rate when moving from SWE-bench Verified to hardware tasks because bugs spread across parallel modules via signal flow, with testbench feedback lifting performance by 42-45% while file-level oracles add only 1.4%.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The 511 instances drawn from 114 GitHub repositories, together with their developer patches and testbenches, form a representative sample of real-world hardware engineering work that requires repository navigation, hierarchy-aware localization, EDA verification, and multi-file patching.","pith_extraction_headline":"Software-tuned AI agents struggle with hardware engineering because bugs propagate through signal flows across instantiated modules rather than along call 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