[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85153-en":3,"doc-seo-85153-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85153,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","AfterVibe: What Remains When the Conversation Ends","AfterVibe recovers natural-language specifications from a vibe coding session by distilling developer intent into an abstract spec from a produced code artifact and its conversation trajectory. An LLM-generated spec is validated with a regeneration test: a second blind AI agent rebuilds the artifact from the spec alone, and a multi-tier pipeline checks equivalence via tests, verification conditions, and structured reasoning. Evaluations on 72 real projects show high regeneration scores with controlled diversity, enabling specs as review records when AI code production outpaces traditional review.","arXiv :2607 .09900v 1 [ cs . SE] 10 Jul 2026  \nAfterVibe: What Remains When the Conversation Ends  \nMatteo Paltenghi , Satish Chandra Meta, USA  \nWe present AfterVibe, a framework that recovers natural-language specifications from a vibe coding session. Given a code artifact and the conversation trajectory that produced it, AfterVibe uses an LLM to extract an abstract natural-language specification capturing the developer’s intent, and validates it through a regeneration test: a second, blind AI agent re-implements the artifact from the spec alone, and the resulting code is graded against the original through a multi-tier validation pipeline. Spec quality is thus measured by whether an agent can regenerate passing code; if theverifiers deem the implementations equivalent the spec is considered strong, otherwise it is iteratively refined. Evaluating AfterVibe on 72 real-world vibe-coded projects from a company’s internal coding sessions, we find that its recovered specs are abstract by design—capturing behavioral intent without dictating implementation—yet strong. Multiple independent regenerations achieve a high mean regeneration score of 5.06 out of 6.0 while remaining diverse in their details, confirming that the spec constrains what without over-prescribing how. Besides outperforming existing human-authored descriptions, the specs can be further strengthened iteratively to a score of 5.74 . A practical implication is that specifications—not code—could become the primary artifact for human review and the source of record at a time when AI-generated code is outpacing customary code review.  \nCorrespondence: Matteo Paltenghi ([mattepalte@live.it](mattepalte@live.it)), Satish Chandra ([schandra@acm.org](schandra@acm.org))   \n1 Introduction  \nThe advent of powerful large language models (LLMs) and coding agents has enabled a new style of programming in which developers describe what they want in natural language and let an AI assistant generate the code. Andrej Karpathy coined the term vibe coding 1 to describe this practice: “you fully give in to the vibes, embrace exponentials, and forget that the code even exists.”What was once a niche experiment has rapidly become mainstream—millions of developers now use AI coding assistants daily, and entire startups ship products built almost exclusively through conversational prompting. In this work, we use “vibe coding” broadly to denote any code change produced through conversational interaction with an AI coding agent—including production-quality changes at scale that are ultimately reviewed and landed as accepted pull requests.  \nA well-known problem with vibe coding is no one fully understands the code—not even the developer who prompted it into existence. Traditional software engineering relies on the assumption that someone—be it the original author or a reviewer—can read, reason about, and vouch for the correctness of source code. Vibe coding undermines this assumption at its root. The developer’s intent lives ephemerally in a chat transcript; the generated code is voluminous and often opaque; and the mapping between the two is implicit at best. This situation is especially problematic for code review, one of the most important quality-assurance practices in modern software engineering (Bacchelli and Bird, 2013) . A reviewer confronted with ever-growing volume of AI-generated code has no concise, authoritative document that states what the code is supposed to do. The result is that a growing class of software that is effectively unreviewable, and not just because of the rate of code production. A recent industry study (DORA, 2025) finds code review not keeping up with AI-based code production.  \nWe propose AfterVibe, a framework that bridges the gap between vibe coding and traditional quality assurance. The name reflects its purpose: it captures what remains after a vibe coding session ends—a  \n1Andrej Karpathy, There’s a New Kind of Coding Emerging, X/Twitter post, February 2025","cbCaiecwqmwwC6Pq","https://ap.wps.com/l/cbCaiecwqmwwC6Pq","pdf",783650,3,1,27,"English","en",105,"# Introduction\n## Problem with Vibe Coding and Code Review\n## AfterVibe Framework and Spec Extraction\n## Regeneration Test and Verification Pipeline","[{\"question\":\"What problem does AfterVibe address in vibe coding workflows?\",\"answer\":\"AfterVibe targets the fact that vibe-coded implementations are often not fully understood, and developers lack a concise authoritative artifact describing what the code should do for review and quality assurance.\"},{\"question\":\"How does AfterVibe extract requirements from a vibe coding session?\",\"answer\":\"It takes the final code artifact and the conversation trajectory, then uses an LLM to distill an abstract natural-language specification that captures observable developer intent.\"},{\"question\":\"How does AfterVibe validate whether the extracted specification is faithful?\",\"answer\":\"It runs a regeneration test with a second blind AI agent that rebuilds the artifact from the spec alone, then compares regenerated code to the original using a three-tier verification 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problem does AfterVibe address in vibe coding workflows?","Question",{"text":75,"@type":76},"AfterVibe targets the fact that vibe-coded implementations are often not fully understood, and developers lack a concise authoritative artifact describing what the code should do for review and quality assurance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does AfterVibe extract requirements from a vibe coding session?",{"text":80,"@type":76},"It takes the final code artifact and the conversation trajectory, then uses an LLM to distill an abstract natural-language specification that captures observable developer intent.",{"name":82,"@type":73,"acceptedAnswer":83},"How does AfterVibe validate whether the extracted specification is faithful?",{"text":84,"@type":76},"It runs a regeneration test with a second blind AI agent that rebuilds the artifact from the spec alone, then compares regenerated code to the original using a three-tier verification 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