{"slug": "ideaambig-benchmarking-implementation-critical-gaps-in-research-idea", "title": "IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications", "summary": "A September 9, 2026 arXiv paper introduced IdeaAMBIG, a benchmark of 660 evidence-grounded instances — 163 real-world gaps from reproducibility reports and GitHub issues plus 497 controlled synthetic gaps — for measuring whether research-method specifications are detailed enough for a competent implementer or coding agent to reproduce the intended method. Across 13 LLMs, the best model reached only 9.6% Macro Defect Recovery Rate on real-world instances but 80.6% Macro Clarification Action Success Rate when handed the annotated defect, and an oracle study supplying the gold resolution lifted the downstream codification-ready rate from 14% to 98%. The authors identify defect localization as the main bottleneck across all evaluated models.", "body_md": "# Computer Science > Computation and Language\n\n  [Submitted on 9 Sep 2026]\n\n# Title:IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications\n\n[View PDF](/pdf/2609.10539v1)\n\n[HTML (experimental)](https://arxiv.org/html/2609.10539v1)\n\nAbstract:A research idea may be novel, coherent, and scientifically plausible, yet its proposed method may remain insufficiently specified for faithful implementation. We study the codification readiness of implementation-facing research-method specifications, defined by whether they provide sufficient methodological information for a competent implementer or coding agent to construct the intended method without unsupported assumptions. We construct evidence-grounded specifications and their supported resolutions from papers, codebases, issue threads, and reproduction artifacts. We introduce IdeaAMBIG, a benchmark of 660 evidence-grounded instances: 163 real-world gaps from reproducibility reports and GitHub issues, and 497 controlled synthetic gaps injected into codification-ready references. IdeaAMBIG evaluates three capabilities: codification-readiness assessment, defect localization, and clarification action generation. Defect localization receives only the specification, whereas clarification additionally receives the annotated defect. Across 13 LLMs, the best model achieves 9.6% Macro Defect Recovery Rate on real-world instances but 80.6% Macro Clarification Action Success Rate when given the defect. In an oracle study, supplying the gold resolution raises the downstream codification-ready rate from 14% to 98%. Across all evaluated models, defect localization is the main bottleneck, with stronger clarification given the defect.\n    \n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/ideaambig-benchmarking-implementation-critical-gaps-in-research-idea", "canonical_source": "http://arxiv.org/abs/2609.10539v1", "published_at": "2026-09-10 14:31:42+00:00", "updated_at": "2026-09-10 14:46:43.103618+00:00", "lang": "en", "topics": ["ai-research", "large-language-models", "ai-agents", "ai-safety"], "entities": ["IdeaAMBIG", "arXiv", "GitHub"], "alternates": {"html": "https://wpnews.pro/news/ideaambig-benchmarking-implementation-critical-gaps-in-research-idea", "markdown": "https://wpnews.pro/news/ideaambig-benchmarking-implementation-critical-gaps-in-research-idea.md", "text": "https://wpnews.pro/news/ideaambig-benchmarking-implementation-critical-gaps-in-research-idea.txt", "jsonld": "https://wpnews.pro/news/ideaambig-benchmarking-implementation-critical-gaps-in-research-idea.jsonld"}}