ICML 2026</p>\n","updatedAt":"2026-05-22T17:04:20.637Z","author":{"_id":"60eb9074cc720726777d22a2","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/60eb9074cc720726777d22a2/aI-pHJRmnYOfC5fH7fFzD.jpeg","fullname":"Jinuk Kim","name":"jusjinuk","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.6255497336387634},"editors":["jusjinuk"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/60eb9074cc720726777d22a2/aI-pHJRmnYOfC5fH7fFzD.jpeg"],"reactions":[],"isReport":false}},{"id":"6a11061aff36e60b0160263a","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":358,"isUserFollowing":false},"createdAt":"2026-05-23T01:42:50.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [CodeSpecBench: Benchmarking LLMs for Executable Behavioral Specification Generation](https://huggingface.co/papers/2604.12268) (2026)\n* [Inference-Time Code Selection via Symbolic Equivalence Partitioning](https://huggingface.co/papers/2604.06485) (2026)\n* [An Iterative Test-and-Repair Framework for Competitive Code Generation](https://huggingface.co/papers/2604.05560) (2026)\n* [VeriContest: A Competitive-Programming Benchmark for Verifiable Code Generation](https://huggingface.co/papers/2605.08553) (2026)\n* [POSTCONDBENCH: Benchmarking Correctness and Completeness in Formal Postcondition Inference](https://huggingface.co/papers/2605.03356) (2026)\n* [ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows](https://huggingface.co/papers/2605.12376) (2026)\n* [RepoZero: Can LLMs Generate a Code Repository from Scratch?](https://huggingface.co/papers/2605.07122) (2026)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"<p>This is an automated message from the <a href=\"https://huggingface.co/librarian-bots\">Librarian Bot</a>. I found the following papers similar to this paper. </p>\n<p>The following papers were recommended by the Semantic Scholar API </p>\n<ul>\n<li><a href=\"https://huggingface.co/papers/2604.12268\">CodeSpecBench: Benchmarking LLMs for Executable Behavioral Specification Generation</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.06485\">Inference-Time Code Selection via Symbolic Equivalence Partitioning</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.05560\">An Iterative Test-and-Repair Framework for Competitive Code Generation</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.08553\">VeriContest: A Competitive-Programming Benchmark for Verifiable Code Generation</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.03356\">POSTCONDBENCH: Benchmarking Correctness and Completeness in Formal Postcondition Inference</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.12376\">ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2605.07122\">RepoZero: Can LLMs Generate a Code Repository from Scratch?</a> (2026)</li>\n</ul>\n<p> Please give a thumbs up to this comment if you found it helpful!</p>\n<p> If you want recommendations for any Paper on Hugging Face checkout <a href=\"https://huggingface.co/spaces/librarian-bots/recommend_similar_papers\">this</a> Space</p>\n<p> You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: <code><span class=\"SVELTE_PARTIAL_HYDRATER contents\" data-target=\"UserMention\" data-props=\"{&quot;user&quot;:&quot;librarian-bot&quot;}\"><span class=\"inline-block\"><span class=\"contents\"><a href=\"/librarian-bot\">@<span class=\"underline\">librarian-bot</span></a></span> </span></span> recommend</code></p>\n","updatedAt":"2026-05-23T01:42:50.600Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":358,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.702603280544281},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.15669","authors":[{"_id":"6a108b9ad8ff13e4eeb2581d","name":"Jinuk Kim","hidden":false},{"_id":"6a108b9ad8ff13e4eeb2581e","name":"Junsoo Byun","hidden":false},{"_id":"6a108b9ad8ff13e4eeb2581f","name":"Donghwi Hwang","hidden":false},{"_id":"6a108b9ad8ff13e4eeb25820","name":"Seong-Jin Park","hidden":false},{"_id":"6a108b9ad8ff13e4eeb25821","name":"Hyun Oh Song","hidden":false}],"mediaUrls":["https://cdn-uploads.huggingface.co/production/uploads/60eb9074cc720726777d22a2/5VIG3jBzeaSorZGXhhxAL.png"],"publishedAt":"2026-05-15T00:00:00.000Z","submittedOnDailyAt":"2026-05-22T00:00:00.000Z","title":"Rule2DRC: Benchmarking LLM Agents for DRC Script Synthesis with Execution-Guided Test Generation","submittedOnDailyBy":{"_id":"60eb9074cc720726777d22a2","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/60eb9074cc720726777d22a2/aI-pHJRmnYOfC5fH7fFzD.jpeg","isPro":false,"fullname":"Jinuk Kim","user":"jusjinuk","type":"user","name":"jusjinuk"},"summary":"Manufacturable chip layouts must satisfy thousands of geometry-based design rules, and design rule checking (DRC) enforces them by running executable DRC scripts on layouts. Translating natural language rules into correct DRC scripts is labor-intensive and requires specialized expertise, motivating LLM agents for DRC script synthesis and debugging. However, existing benchmarks have small evaluation sets and often evaluate scripts by code similarity rather than execution correctness, and prior machine learning-based methods either ignore execution feedback or require labeled test layouts as agent's input. To this end, we introduce Rule2DRC, a large-scale benchmark for DRC script coding agents with 1,000 rule-to-script tasks and 13,921 evaluation chip layouts for execution-based scoring. Rule2DRC provides an evaluation pipeline that measures functional correctness via DRC execution outcomes without requiring evaluation layouts as input to the agent. We also propose SplitTester, a tester agent for program selection that uses execution feedback to generate discriminative test cases and separate previously indistinguishable candidate scripts, substantially improving Best-of-N selection performance in this domain. We release the code at https://github.com/snu-mllab/Rule2DRC.","upvotes":2,"discussionId":"6a108b9ad8ff13e4eeb25822","githubRepo":"https://github.com/snu-mllab/Rule2DRC","githubRepoAddedBy":"user","ai_summary":"Rule2DRC introduces a large-scale benchmark for DRC script synthesis with 1,000 rule-to-script tasks and 13,921 evaluation layouts, along with SplitTester which improves program selection through execution-based feedback.","ai_keywords":["DRC script synthesis","execution feedback","program selection","Best-of-N selection","design rule checking","natural language rules","DRC scripts","chip layouts","functional correctness","evaluation pipeline"],"githubStars":0,"organization":{"_id":"66d54dc8033492801db2bf5a","name":"SeoulNatlUniv","fullname":"Seoul National University","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/659ccc9d18897eb6594e897f/_-0BM-1UyM-d-lRiahFnf.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"60eb9074cc720726777d22a2","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/60eb9074cc720726777d22a2/aI-pHJRmnYOfC5fH7fFzD.jpeg","isPro":false,"fullname":"Jinuk Kim","user":"jusjinuk","type":"user"},{"_id":"6708e469d7c32e3238036bd3","avatarUrl":"/avatars/6c6273e7c2b4446ce5be76a11ecae83a.svg","isPro":false,"fullname":"Junyoung Yeom","user":"yeomjy","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"66d54dc8033492801db2bf5a","name":"SeoulNatlUniv","fullname":"Seoul National University","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/659ccc9d18897eb6594e897f/_-0BM-1UyM-d-lRiahFnf.png"}}">

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