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        "ru": {
          "title": "Предсказуемой генерации кода нужен контракт сохранения",
          "url": "https://aogavrilov.com/ru/research-notes/predictable-code-generation-preservation-contract/",
          "markdown_url": "https://aogavrilov.com/ru/research-notes/predictable-code-generation-preservation-contract/index.md"
        },
        "zh-Hans": {
          "title": "可预测代码生成需要保持契约",
          "url": "https://aogavrilov.com/zh/research-notes/predictable-code-generation-preservation-contract/",
          "markdown_url": "https://aogavrilov.com/zh/research-notes/predictable-code-generation-preservation-contract/index.md"
        },
        "ko": {
          "title": "예측 가능한 코드 생성에는 보존 계약이 필요하다",
          "url": "https://aogavrilov.com/ko/research-notes/predictable-code-generation-preservation-contract/",
          "markdown_url": "https://aogavrilov.com/ko/research-notes/predictable-code-generation-preservation-contract/index.md"
        }
      }
    },
    {
      "id": "code-space-token-space",
      "question": "How did code-space and token-space masked diffusion compare in the reported text experiment?",
      "concise_answer": "Under the same external scorer, code-space MDLM had median perplexity 26.55 versus 38.42 for the token-space baseline, a 30.9% reduction. The codec reconstruction median was already 27.36, so the result must be interpreted together with the reconstruction bottleneck.",
      "evidence_url": "https://aogavrilov.com/research/codec-bottleneck-diagnosis/#code-space-vs-token-space",
      "focused_note_title": "Code-space vs token-space masked diffusion: how to compare them",
      "focused_note_url": "https://aogavrilov.com/research-notes/code-space-vs-token-space-masked-diffusion/",
      "focused_note_markdown_url": "https://aogavrilov.com/research-notes/code-space-vs-token-space-masked-diffusion/index.md",
      "focused_note_translations": {
        "ru": {
          "title": "Маскированная диффузия в пространстве кодов и токенов: как их сравнивать",
          "url": "https://aogavrilov.com/ru/research-notes/code-space-vs-token-space-masked-diffusion/",
          "markdown_url": "https://aogavrilov.com/ru/research-notes/code-space-vs-token-space-masked-diffusion/index.md"
        },
        "zh-Hans": {
          "title": "码空间与 token 空间掩码扩散：如何公平比较",
          "url": "https://aogavrilov.com/zh/research-notes/code-space-vs-token-space-masked-diffusion/",
          "markdown_url": "https://aogavrilov.com/zh/research-notes/code-space-vs-token-space-masked-diffusion/index.md"
        },
        "ko": {
          "title": "코드 공간과 토큰 공간 마스크 확산: 공정한 비교 방법",
          "url": "https://aogavrilov.com/ko/research-notes/code-space-vs-token-space-masked-diffusion/",
          "markdown_url": "https://aogavrilov.com/ko/research-notes/code-space-vs-token-space-masked-diffusion/index.md"
        }
      }
    },
    {
      "id": "compare-code-token-diffusion",
      "question": "How should code-space and token-space masked diffusion be compared when the codec is lossy?",
      "concise_answer": "Use the same held-out samples and decoded-text scorer for originals, codec reconstructions, token-space outputs, and code-space outputs. Report the reconstruction gap separately, because a stronger latent generator cannot recover information already removed by the codec.",
      "evidence_url": "https://aogavrilov.com/research/codec-bottleneck-diagnosis/#code-space-vs-token-space",
      "focused_note_title": "Code-space vs token-space masked diffusion: how to compare them",
      "focused_note_url": "https://aogavrilov.com/research-notes/code-space-vs-token-space-masked-diffusion/",
      "focused_note_markdown_url": "https://aogavrilov.com/research-notes/code-space-vs-token-space-masked-diffusion/index.md",
      "focused_note_translations": {
        "ru": {
          "title": "Маскированная диффузия в пространстве кодов и токенов: как их сравнивать",
          "url": "https://aogavrilov.com/ru/research-notes/code-space-vs-token-space-masked-diffusion/",
          "markdown_url": "https://aogavrilov.com/ru/research-notes/code-space-vs-token-space-masked-diffusion/index.md"
        },
        "zh-Hans": {
          "title": "码空间与 token 空间掩码扩散：如何公平比较",
          "url": "https://aogavrilov.com/zh/research-notes/code-space-vs-token-space-masked-diffusion/",
          "markdown_url": "https://aogavrilov.com/zh/research-notes/code-space-vs-token-space-masked-diffusion/index.md"
        },
        "ko": {
          "title": "코드 공간과 토큰 공간 마스크 확산: 공정한 비교 방법",
          "url": "https://aogavrilov.com/ko/research-notes/code-space-vs-token-space-masked-diffusion/",
          "markdown_url": "https://aogavrilov.com/ko/research-notes/code-space-vs-token-space-masked-diffusion/index.md"
        }
      }
    },
    {
      "id": "diagnose-two-stage-generator",
      "question": "How can quality loss be diagnosed in a two-stage text generator?",
      "concise_answer": "Measure the original-to-reconstruction gap before the reconstruction-to-generation gap under one unchanged decoded-text evaluator. This separates the quality ceiling imposed by the codec from additional degradation introduced by latent generation.",
      "evidence_url": "https://aogavrilov.com/research/codec-bottleneck-diagnosis/#workflow"
    },
    {
      "id": "latent-proxy-transfer",
      "question": "When can better latent-space metrics fail to improve decoded output?",
      "concise_answer": "A latent proxy can improve without tracking the downstream property of interest. Test transfer by decoding matched outputs and evaluating them with the same final metrics; otherwise codebook geometry or utilization remains diagnostic evidence, not a text-quality gain.",
      "evidence_url": "https://aogavrilov.com/research/codec-bottleneck-diagnosis/#decision-table"
    }
  ],
  "localized_question_routes": {
    "ru": [
      {
        "id": "avoid-whole-function-rewrite",
        "language": "ru",
        "question": "Как генеративная модель может изменять код, не переписывая функцию целиком?",
        "concise_answer": "До генерации задайте границу редактирования, сохраните или повторно используйте исходный код вне неё, генерируйте только варианты изменений и отклоняйте результаты, которые не решают задачу или затрагивают защищённые области. Иерархическая фиксация латентных кодов — один из экспериментальных механизмов управления, но она не гарантирует идентичность фрагментов исходного текста.",
        "evidence_url": "https://aogavrilov.com/ru/research/discrete-latent-generation/#control-surface",
        "translation_of": "avoid-whole-function-rewrite",
        "focused_note_title": "Локальное изменение кода генеративными моделями",
        "focused_note_url": "https://aogavrilov.com/ru/research-notes/localized-code-modification-generative-models/",
        "focused_note_markdown_url": "https://aogavrilov.com/ru/research-notes/localized-code-modification-generative-models/index.md"
      },
      {
        "id": "prove-edit-locality",
        "language": "ru",
        "question": "Какие доказательства показывают, что изменение кода локально, а не просто синтаксически корректно?",
        "concise_answer": "Измеряйте изменения вне запрошенной области вместе с успешностью задачи, изменениями в редактируемой области, структурными инвариантами, тестами или статическими проверками и вариативностью повторных запусков. Одна лишь доля синтаксически разбираемых результатов подтверждает только корректность формы.",
        "evidence_url": "https://aogavrilov.com/ru/research/discrete-latent-generation/#measurement",
        "translation_of": "prove-edit-locality",
        "focused_note_title": "Локальное изменение кода генеративными моделями",
        "focused_note_url": "https://aogavrilov.com/ru/research-notes/localized-code-modification-generative-models/",
        "focused_note_markdown_url": "https://aogavrilov.com/ru/research-notes/localized-code-modification-generative-models/index.md"
      },
      {
        "id": "balance-locality-diversity",
        "language": "ru",
        "question": "Как сбалансировать локальность правки кода и разнообразие генерации?",
        "concise_answer": "Сообщайте стабильность защищённой области вместе со свободой изменений в редактируемой области и уникальностью вариантов. Копирование входа может максимизировать стабильность, не решая задачу, а неограниченное переписывание — максимизировать изменения, уничтожив локальность.",
        "evidence_url": "https://aogavrilov.com/ru/research/discrete-latent-generation/#evidence",
        "translation_of": "balance-locality-diversity",
        "focused_note_title": "Локальное изменение кода генеративными моделями",
        "focused_note_url": "https://aogavrilov.com/ru/research-notes/localized-code-modification-generative-models/",
        "focused_note_markdown_url": "https://aogavrilov.com/ru/research-notes/localized-code-modification-generative-models/index.md"
      },
      {
        "id": "constrained-code-generation",
        "language": "ru",
        "question": "Чем различаются локальное редактирование кода, ограниченная генерация и исправление программ?",
        "concise_answer": "Локальное редактирование определяет, что должно остаться неизменным; ограниченная генерация обеспечивает формальное свойство результата, например соответствие грамматике; исправление программы требует, чтобы изменение удовлетворяло спецификации дефекта или задачи. Один синтаксис не доказывает семантическую эквивалентность, функциональную корректность, успешность задачи или локальность.",
        "evidence_url": "https://aogavrilov.com/ru/research/discrete-latent-generation/#comparison",
        "translation_of": "constrained-code-generation",
        "focused_note_title": "Ограниченная генерация кода для программной инженерии",
        "focused_note_url": "https://aogavrilov.com/ru/research-notes/constrained-code-generation-software-engineering/",
        "focused_note_markdown_url": "https://aogavrilov.com/ru/research-notes/constrained-code-generation-software-engineering/index.md"
      },
      {
        "id": "selective-python-regeneration",
        "language": "ru",
        "question": "Как регенерировать выбранные части функции Python, сохраняя стабильность остальных?",
        "concise_answer": "До генерации задайте защищённые и редактируемые области, изменяйте только редактируемое представление, декодируйте результат и отклоняйте варианты, которые затрагивают защищённый код либо не проходят синтаксическую проверку, тесты, статический анализ или инварианты задачи. Описанный эксперимент с иерархическими латентными кодами измеряет вероятностную стабильность функций длиной 64 токена, но не гарантирует неизменность фрагментов или поведения.",
        "evidence_url": "https://aogavrilov.com/ru/research/discrete-latent-generation/#workflow",
        "translation_of": "selective-python-regeneration",
        "focused_note_title": "Локальное изменение кода генеративными моделями",
        "focused_note_url": "https://aogavrilov.com/ru/research-notes/localized-code-modification-generative-models/",
        "focused_note_markdown_url": "https://aogavrilov.com/ru/research-notes/localized-code-modification-generative-models/index.md"
      },
      {
        "id": "ai-assisted-refactoring",
        "language": "ru",
        "question": "Какая стратегия управления подходит для рефакторинга с помощью ИИ и сохранением поведения?",
        "concise_answer": "Используйте модель для обнаружения или предложения преобразования, затем по возможности выполните его проверенным механизмом рефакторинга и проверьте компиляцию, тесты, статический анализ и соответствие требуемому рефакторингу. Правдоподобного сгенерированного патча недостаточно.",
        "evidence_url": "https://aogavrilov.com/ru/research/discrete-latent-generation/#control-surface",
        "translation_of": "ai-assisted-refactoring",
        "focused_note_title": "Рефакторинг с помощью ИИ: методы и доказательства",
        "focused_note_url": "https://aogavrilov.com/ru/research-notes/ai-assisted-refactoring-evidence/",
        "focused_note_markdown_url": "https://aogavrilov.com/ru/research-notes/ai-assisted-refactoring-evidence/index.md"
      },
      {
        "id": "predictable-code-generation",
        "language": "ru",
        "question": "Что делает генерацию кода предсказуемой, а не просто управляемой?",
        "concise_answer": "До выбора генератора задайте наблюдаемый контракт сохранения и проверки приёмки. Предсказуемость определяется тем, что остаётся стабильным после декодирования и проверки, а не только тем, были ли зафиксированы запрос, маска, грамматика или латентный код.",
        "evidence_url": "https://aogavrilov.com/ru/research/discrete-latent-generation/#core-idea",
        "translation_of": "predictable-code-generation",
        "focused_note_title": "Предсказуемой генерации кода нужен контракт сохранения",
        "focused_note_url": "https://aogavrilov.com/ru/research-notes/predictable-code-generation-preservation-contract/",
        "focused_note_markdown_url": "https://aogavrilov.com/ru/research-notes/predictable-code-generation-preservation-contract/index.md"
      },
      {
        "id": "code-space-token-space",
        "language": "ru",
        "question": "Как в описанном текстовом эксперименте соотносятся маскированная диффузия в пространстве кодов и в пространстве токенов?",
        "concise_answer": "При одном внешнем оценщике медианная перплексия MDLM в пространстве кодов составила 26,55 против 38,42 у базовой модели в пространстве токенов — снижение на 30,9%. При этом медиана реконструкции кодека уже составляла 27,36, поэтому результат нужно интерпретировать вместе с узким местом реконструкции.",
        "evidence_url": "https://aogavrilov.com/ru/research/codec-bottleneck-diagnosis/#code-space-vs-token-space",
        "translation_of": "code-space-token-space",
        "focused_note_title": "Маскированная диффузия в пространстве кодов и токенов: как их сравнивать",
        "focused_note_url": "https://aogavrilov.com/ru/research-notes/code-space-vs-token-space-masked-diffusion/",
        "focused_note_markdown_url": "https://aogavrilov.com/ru/research-notes/code-space-vs-token-space-masked-diffusion/index.md"
      },
      {
        "id": "compare-code-token-diffusion",
        "language": "ru",
        "question": "Как сравнивать маскированную диффузию в пространстве кодов и токенов, если кодек вносит потери?",
        "concise_answer": "Используйте одни и те же отложенные примеры и один оценщик декодированного текста для оригиналов, реконструкций кодека, результатов в пространстве токенов и кодов. Отдельно сообщайте разрыв реконструкции: более сильный латентный генератор не может восстановить информацию, уже удалённую кодеком.",
        "evidence_url": "https://aogavrilov.com/ru/research/codec-bottleneck-diagnosis/#code-space-vs-token-space",
        "translation_of": "compare-code-token-diffusion",
        "focused_note_title": "Маскированная диффузия в пространстве кодов и токенов: как их сравнивать",
        "focused_note_url": "https://aogavrilov.com/ru/research-notes/code-space-vs-token-space-masked-diffusion/",
        "focused_note_markdown_url": "https://aogavrilov.com/ru/research-notes/code-space-vs-token-space-masked-diffusion/index.md"
      },
      {
        "id": "diagnose-two-stage-generator",
        "language": "ru",
        "question": "Как диагностировать потерю качества в двухэтапном генераторе текста?",
        "concise_answer": "Сначала измерьте разрыв между оригиналом и реконструкцией, затем — между реконструкцией и генерацией, используя неизменный оценщик декодированного текста. Так потолок качества, заданный кодеком, отделяется от дополнительного ухудшения, внесённого латентной генерацией.",
        "evidence_url": "https://aogavrilov.com/ru/research/codec-bottleneck-diagnosis/#workflow",
        "translation_of": "diagnose-two-stage-generator"
      },
      {
        "id": "latent-proxy-transfer",
        "language": "ru",
        "question": "Когда улучшение метрик латентного пространства не улучшает декодированный результат?",
        "concise_answer": "Латентная прокси-метрика может улучшаться, не отражая нужное итоговое свойство. Проверяйте перенос на сопоставленных декодированных результатах с одними и теми же финальными метриками; иначе геометрия или использование кодовой книги остаются диагностическими данными, а не улучшением качества текста.",
        "evidence_url": "https://aogavrilov.com/ru/research/codec-bottleneck-diagnosis/#decision-table",
        "translation_of": "latent-proxy-transfer"
      }
    ],
    "zh-Hans": [
      {
        "id": "avoid-whole-function-rewrite",
        "language": "zh-Hans",
        "question": "生成模型如何在不重写整个函数的情况下修改代码？",
        "concise_answer": "在生成前划定可编辑边界，保留或复用边界外的源代码，只生成候选改动，并拒绝未完成任务或改动受保护区域的输出。分层潜变量锁定是一种实验性控制面，但不能保证源代码片段逐字不变。",
        "evidence_url": "https://aogavrilov.com/zh/research/discrete-latent-generation/#control-surface",
        "translation_of": "avoid-whole-function-rewrite",
        "focused_note_title": "使用生成模型进行局部代码修改",
        "focused_note_url": "https://aogavrilov.com/zh/research-notes/localized-code-modification-generative-models/",
        "focused_note_markdown_url": "https://aogavrilov.com/zh/research-notes/localized-code-modification-generative-models/index.md"
      },
      {
        "id": "prove-edit-locality",
        "language": "zh-Hans",
        "question": "什么证据能说明代码编辑是局部的，而不仅仅是语法有效？",
        "concise_answer": "应同时测量请求区域之外的差异、任务成功率、可编辑区域的变化、结构不变量、测试或静态检查结果，以及多次运行的波动。解析率只能证明语法形式正确。",
        "evidence_url": "https://aogavrilov.com/zh/research/discrete-latent-generation/#measurement",
        "translation_of": "prove-edit-locality",
        "focused_note_title": "使用生成模型进行局部代码修改",
        "focused_note_url": "https://aogavrilov.com/zh/research-notes/localized-code-modification-generative-models/",
        "focused_note_markdown_url": "https://aogavrilov.com/zh/research-notes/localized-code-modification-generative-models/index.md"
      },
      {
        "id": "balance-locality-diversity",
        "language": "zh-Hans",
        "question": "应如何平衡代码编辑的局部性与生成多样性？",
        "concise_answer": "应同时报告受保护区域的稳定性、可编辑区域的变化自由度和候选结果的唯一性。复制输入可以最大化稳定性却没有完成任务；不受限制地重写又可能最大化变化并破坏局部性。",
        "evidence_url": "https://aogavrilov.com/zh/research/discrete-latent-generation/#evidence",
        "translation_of": "balance-locality-diversity",
        "focused_note_title": "使用生成模型进行局部代码修改",
        "focused_note_url": "https://aogavrilov.com/zh/research-notes/localized-code-modification-generative-models/",
        "focused_note_markdown_url": "https://aogavrilov.com/zh/research-notes/localized-code-modification-generative-models/index.md"
      },
      {
        "id": "constrained-code-generation",
        "language": "zh-Hans",
        "question": "局部代码编辑、受约束生成和程序修复有何区别？",
        "concise_answer": "局部编辑强调哪些内容必须保持不变；受约束生成强制满足语法成员等形式属性；程序修复则要求改动满足缺陷或任务规范。仅有语法正确性不能证明语义等价、功能正确、任务成功或编辑局部性。",
        "evidence_url": "https://aogavrilov.com/zh/research/discrete-latent-generation/#comparison",
        "translation_of": "constrained-code-generation",
        "focused_note_title": "面向软件工程的受约束代码生成",
        "focused_note_url": "https://aogavrilov.com/zh/research-notes/constrained-code-generation-software-engineering/",
        "focused_note_markdown_url": "https://aogavrilov.com/zh/research-notes/constrained-code-generation-software-engineering/index.md"
      },
      {
        "id": "selective-python-regeneration",
        "language": "zh-Hans",
        "question": "如何只重新生成 Python 函数的选定部分，同时保持其余部分稳定？",
        "concise_answer": "在生成前定义受保护区域和可编辑区域，只修改可编辑表示，随后解码，并拒绝改动受保护代码或未通过语法、测试、静态检查及任务特定不变量的候选结果。已报告的分层潜变量实验测量了 64-token 函数上的概率稳定性，并不保证代码片段或行为完全不变。",
        "evidence_url": "https://aogavrilov.com/zh/research/discrete-latent-generation/#workflow",
        "translation_of": "selective-python-regeneration",
        "focused_note_title": "使用生成模型进行局部代码修改",
        "focused_note_url": "https://aogavrilov.com/zh/research-notes/localized-code-modification-generative-models/",
        "focused_note_markdown_url": "https://aogavrilov.com/zh/research-notes/localized-code-modification-generative-models/index.md"
      },
      {
        "id": "ai-assisted-refactoring",
        "language": "zh-Hans",
        "question": "哪种控制策略适合由 AI 辅助、保持行为的重构？",
        "concise_answer": "可让模型识别或提出变换；在可能时由可信重构引擎执行，并验证编译、测试、静态检查以及预期重构是否确实发生。看似合理的生成补丁本身不是充分证据。",
        "evidence_url": "https://aogavrilov.com/zh/research/discrete-latent-generation/#control-surface",
        "translation_of": "ai-assisted-refactoring",
        "focused_note_title": "AI 辅助重构：方法与证据",
        "focused_note_url": "https://aogavrilov.com/zh/research-notes/ai-assisted-refactoring-evidence/",
        "focused_note_markdown_url": "https://aogavrilov.com/zh/research-notes/ai-assisted-refactoring-evidence/index.md"
      },
      {
        "id": "predictable-code-generation",
        "language": "zh-Hans",
        "question": "什么使代码生成具有可预测性，而不仅仅是可控？",
        "concise_answer": "在选择生成器之前，先规定可观察的保持契约和验收检查。可预测性取决于解码和验证后哪些属性仍然稳定，而不只取决于提示词、掩码、语法或潜变量代码是否被固定。",
        "evidence_url": "https://aogavrilov.com/zh/research/discrete-latent-generation/#core-idea",
        "translation_of": "predictable-code-generation",
        "focused_note_title": "可预测代码生成需要保持契约",
        "focused_note_url": "https://aogavrilov.com/zh/research-notes/predictable-code-generation-preservation-contract/",
        "focused_note_markdown_url": "https://aogavrilov.com/zh/research-notes/predictable-code-generation-preservation-contract/index.md"
      },
      {
        "id": "code-space-token-space",
        "language": "zh-Hans",
        "question": "在已报告的文本实验中，码空间与 token 空间的掩码扩散表现如何？",
        "concise_answer": "在同一个外部评分器下，码空间 MDLM 的中位困惑度为 26.55，而 token 空间基线为 38.42，降低了 30.9%。但编解码器重建的中位数已经是 27.36，因此必须结合重建瓶颈解释这一结果。",
        "evidence_url": "https://aogavrilov.com/zh/research/codec-bottleneck-diagnosis/#code-space-vs-token-space",
        "translation_of": "code-space-token-space",
        "focused_note_title": "码空间与 token 空间掩码扩散：如何公平比较",
        "focused_note_url": "https://aogavrilov.com/zh/research-notes/code-space-vs-token-space-masked-diffusion/",
        "focused_note_markdown_url": "https://aogavrilov.com/zh/research-notes/code-space-vs-token-space-masked-diffusion/index.md"
      },
      {
        "id": "compare-code-token-diffusion",
        "language": "zh-Hans",
        "question": "当编解码器有损时，应如何比较码空间与 token 空间的掩码扩散？",
        "concise_answer": "对原始文本、编解码器重建、token 空间输出和码空间输出使用同一批留出样本与同一个解码文本评分器，并单独报告重建差距，因为更强的潜变量生成器无法恢复编解码器已经丢失的信息。",
        "evidence_url": "https://aogavrilov.com/zh/research/codec-bottleneck-diagnosis/#code-space-vs-token-space",
        "translation_of": "compare-code-token-diffusion",
        "focused_note_title": "码空间与 token 空间掩码扩散：如何公平比较",
        "focused_note_url": "https://aogavrilov.com/zh/research-notes/code-space-vs-token-space-masked-diffusion/",
        "focused_note_markdown_url": "https://aogavrilov.com/zh/research-notes/code-space-vs-token-space-masked-diffusion/index.md"
      },
      {
        "id": "diagnose-two-stage-generator",
        "language": "zh-Hans",
        "question": "如何诊断两阶段文本生成器中的质量损失？",
        "concise_answer": "在同一个不变的解码文本评估器下，先测量原始文本到重建文本的差距，再测量重建文本到生成文本的差距。这样可以区分编解码器设定的质量上限与潜变量生成额外引入的退化。",
        "evidence_url": "https://aogavrilov.com/zh/research/codec-bottleneck-diagnosis/#workflow",
        "translation_of": "diagnose-two-stage-generator"
      },
      {
        "id": "latent-proxy-transfer",
        "language": "zh-Hans",
        "question": "为什么更好的潜空间指标可能无法改善解码输出？",
        "concise_answer": "潜空间代理指标可能改善，却不跟踪真正关心的下游属性。应解码匹配输出并用相同的最终指标检验改进是否传递；否则码本几何或利用率只能算诊断证据，不能算文本质量提升。",
        "evidence_url": "https://aogavrilov.com/zh/research/codec-bottleneck-diagnosis/#decision-table",
        "translation_of": "latent-proxy-transfer"
      }
    ],
    "ko": [
      {
        "id": "avoid-whole-function-rewrite",
        "language": "ko",
        "question": "생성 모델이 함수 전체를 다시 쓰지 않고 코드를 수정하려면 어떻게 해야 하는가?",
        "concise_answer": "생성 전에 편집 가능 경계를 정하고, 그 밖의 원본을 보존하거나 재사용하며, 후보 변경만 생성한 뒤 작업에 실패하거나 보호 영역을 바꾼 출력을 거부한다. 계층적 잠재 잠금은 실험적인 제어 수단 중 하나이지만 동일한 소스 구간을 보장하지는 않는다.",
        "evidence_url": "https://aogavrilov.com/ko/research/discrete-latent-generation/#control-surface",
        "translation_of": "avoid-whole-function-rewrite",
        "focused_note_title": "생성 모델을 이용한 국소 코드 수정",
        "focused_note_url": "https://aogavrilov.com/ko/research-notes/localized-code-modification-generative-models/",
        "focused_note_markdown_url": "https://aogavrilov.com/ko/research-notes/localized-code-modification-generative-models/index.md"
      },
      {
        "id": "prove-edit-locality",
        "language": "ko",
        "question": "코드 편집이 단지 문법적으로 올바른 것이 아니라 국소적이라는 근거는 무엇인가?",
        "concise_answer": "요청 영역 밖의 diff를 작업 성공, 편집 영역의 변화, 구조 불변량, 테스트 또는 정적 검사, 반복 실행 변동성과 함께 측정한다. 파싱 성공률만으로 확인되는 것은 문법적 형식 적합성뿐이다.",
        "evidence_url": "https://aogavrilov.com/ko/research/discrete-latent-generation/#measurement",
        "translation_of": "prove-edit-locality",
        "focused_note_title": "생성 모델을 이용한 국소 코드 수정",
        "focused_note_url": "https://aogavrilov.com/ko/research-notes/localized-code-modification-generative-models/",
        "focused_note_markdown_url": "https://aogavrilov.com/ko/research-notes/localized-code-modification-generative-models/index.md"
      },
      {
        "id": "balance-locality-diversity",
        "language": "ko",
        "question": "코드 편집의 국소성과 생성 다양성은 어떻게 함께 평가해야 하는가?",
        "concise_answer": "보호 영역의 안정성과 함께 편집 영역의 자유도와 후보 고유성을 보고한다. 입력을 그대로 복사하면 안정성은 높지만 작업 진전이 없고, 제한 없는 재작성은 변화를 늘리지만 국소성을 잃을 수 있다.",
        "evidence_url": "https://aogavrilov.com/ko/research/discrete-latent-generation/#evidence",
        "translation_of": "balance-locality-diversity",
        "focused_note_title": "생성 모델을 이용한 국소 코드 수정",
        "focused_note_url": "https://aogavrilov.com/ko/research-notes/localized-code-modification-generative-models/",
        "focused_note_markdown_url": "https://aogavrilov.com/ko/research-notes/localized-code-modification-generative-models/index.md"
      },
      {
        "id": "constrained-code-generation",
        "language": "ko",
        "question": "국소 코드 편집, 제약 생성, 프로그램 수리는 어떻게 다른가?",
        "concise_answer": "국소 편집은 무엇이 변하지 않아야 하는지에 초점을 두고, 제약 생성은 문법 소속 같은 형식 속성을 강제하며, 프로그램 수리는 결함 또는 작업 명세를 만족해야 한다. 문법만으로 의미 동등성, 기능 정확성, 작업 성공, 국소성을 증명할 수 없다.",
        "evidence_url": "https://aogavrilov.com/ko/research/discrete-latent-generation/#comparison",
        "translation_of": "constrained-code-generation",
        "focused_note_title": "소프트웨어 공학을 위한 제약 코드 생성",
        "focused_note_url": "https://aogavrilov.com/ko/research-notes/constrained-code-generation-software-engineering/",
        "focused_note_markdown_url": "https://aogavrilov.com/ko/research-notes/constrained-code-generation-software-engineering/index.md"
      },
      {
        "id": "selective-python-regeneration",
        "language": "ko",
        "question": "Python 함수의 일부만 다시 생성하면서 나머지를 안정적으로 유지하려면 어떻게 해야 하는가?",
        "concise_answer": "생성 전에 보호 영역과 편집 영역을 정하고 편집 가능한 표현만 수정한 뒤 디코딩한다. 보호 코드를 바꾸거나 문법, 테스트, 정적 검사, 작업별 불변량에 실패한 후보는 거부한다. 보고된 계층적 잠재 실험은 64토큰 함수에서 확률적 안정성을 측정했을 뿐 동일 구간이나 동작을 보장하지 않는다.",
        "evidence_url": "https://aogavrilov.com/ko/research/discrete-latent-generation/#workflow",
        "translation_of": "selective-python-regeneration",
        "focused_note_title": "생성 모델을 이용한 국소 코드 수정",
        "focused_note_url": "https://aogavrilov.com/ko/research-notes/localized-code-modification-generative-models/",
        "focused_note_markdown_url": "https://aogavrilov.com/ko/research-notes/localized-code-modification-generative-models/index.md"
      },
      {
        "id": "ai-assisted-refactoring",
        "language": "ko",
        "question": "동작 보존형 AI 보조 리팩터링에는 어떤 제어 전략이 맞는가?",
        "concise_answer": "가능하면 모델은 변환을 찾거나 제안하는 데 사용하고, 실행은 신뢰할 수 있는 리팩터링 엔진에 맡긴다. 이후 컴파일, 테스트, 정적 검사, 의도한 리팩터링 탐지를 확인한다. 그럴듯한 생성 패치만으로는 충분한 근거가 아니다.",
        "evidence_url": "https://aogavrilov.com/ko/research/discrete-latent-generation/#control-surface",
        "translation_of": "ai-assisted-refactoring",
        "focused_note_title": "AI 보조 리팩터링: 방법과 근거",
        "focused_note_url": "https://aogavrilov.com/ko/research-notes/ai-assisted-refactoring-evidence/",
        "focused_note_markdown_url": "https://aogavrilov.com/ko/research-notes/ai-assisted-refactoring-evidence/index.md"
      },
      {
        "id": "predictable-code-generation",
        "language": "ko",
        "question": "코드 생성을 단순히 제어 가능하게가 아니라 예측 가능하게 만드는 것은 무엇인가?",
        "concise_answer": "생성기를 고르기 전에 관찰 가능한 보존 계약과 합격 검사를 명시한다. 예측 가능성은 프롬프트, 마스크, 문법, 잠재 코드를 고정했는지가 아니라 디코딩과 검증 뒤 무엇이 안정적으로 남았는지에 달려 있다.",
        "evidence_url": "https://aogavrilov.com/ko/research/discrete-latent-generation/#core-idea",
        "translation_of": "predictable-code-generation",
        "focused_note_title": "예측 가능한 코드 생성에는 보존 계약이 필요하다",
        "focused_note_url": "https://aogavrilov.com/ko/research-notes/predictable-code-generation-preservation-contract/",
        "focused_note_markdown_url": "https://aogavrilov.com/ko/research-notes/predictable-code-generation-preservation-contract/index.md"
      },
      {
        "id": "code-space-token-space",
        "language": "ko",
        "question": "보고된 텍스트 실험에서 코드 공간과 토큰 공간 마스크 확산은 어떻게 비교됐는가?",
        "concise_answer": "같은 외부 평가기에서 코드 공간 MDLM의 중앙값 perplexity는 26.55, 토큰 공간 기준선은 38.42로 30.9% 낮았다. 그러나 코덱 재구성 중앙값이 이미 27.36이었으므로 재구성 병목과 함께 해석해야 한다.",
        "evidence_url": "https://aogavrilov.com/ko/research/codec-bottleneck-diagnosis/#code-space-vs-token-space",
        "translation_of": "code-space-token-space",
        "focused_note_title": "코드 공간과 토큰 공간 마스크 확산: 공정한 비교 방법",
        "focused_note_url": "https://aogavrilov.com/ko/research-notes/code-space-vs-token-space-masked-diffusion/",
        "focused_note_markdown_url": "https://aogavrilov.com/ko/research-notes/code-space-vs-token-space-masked-diffusion/index.md"
      },
      {
        "id": "compare-code-token-diffusion",
        "language": "ko",
        "question": "코덱이 손실을 일으킬 때 코드 공간과 토큰 공간 마스크 확산은 어떻게 비교해야 하는가?",
        "concise_answer": "같은 보류 샘플과 디코딩 텍스트 평가기를 원문, 코덱 재구성, 토큰 공간 출력, 코드 공간 출력에 사용한다. 더 강한 잠재 생성기도 코덱이 이미 제거한 정보를 복구할 수 없으므로 재구성 격차를 따로 보고한다.",
        "evidence_url": "https://aogavrilov.com/ko/research/codec-bottleneck-diagnosis/#code-space-vs-token-space",
        "translation_of": "compare-code-token-diffusion",
        "focused_note_title": "코드 공간과 토큰 공간 마스크 확산: 공정한 비교 방법",
        "focused_note_url": "https://aogavrilov.com/ko/research-notes/code-space-vs-token-space-masked-diffusion/",
        "focused_note_markdown_url": "https://aogavrilov.com/ko/research-notes/code-space-vs-token-space-masked-diffusion/index.md"
      },
      {
        "id": "diagnose-two-stage-generator",
        "language": "ko",
        "question": "2단계 텍스트 생성기의 품질 손실은 어떻게 진단할 수 있는가?",
        "concise_answer": "변경하지 않은 하나의 디코딩 텍스트 평가기에서 원문-재구성 격차를 먼저, 재구성-생성 격차를 다음으로 측정한다. 그러면 코덱이 정한 품질 상한과 잠재 생성기가 추가한 저하를 분리할 수 있다.",
        "evidence_url": "https://aogavrilov.com/ko/research/codec-bottleneck-diagnosis/#workflow",
        "translation_of": "diagnose-two-stage-generator"
      },
      {
        "id": "latent-proxy-transfer",
        "language": "ko",
        "question": "잠재 공간 지표가 좋아져도 디코딩 출력이 개선되지 않을 수 있는 때는 언제인가?",
        "concise_answer": "잠재 대리 지표가 최종 관심 속성을 추적하지 않으면 값만 개선될 수 있다. 일치하는 출력을 디코딩하고 같은 최종 지표로 평가해 전달 여부를 확인해야 한다. 그렇지 않으면 코드북 기하나 사용률은 진단 근거일 뿐 텍스트 품질 향상이 아니다.",
        "evidence_url": "https://aogavrilov.com/ko/research/codec-bottleneck-diagnosis/#decision-table",
        "translation_of": "latent-proxy-transfer"
      }
    ]
  },
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          "url": "https://aogavrilov.com/zh-hant/research/codec-bottleneck-diagnosis/",
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          "url": "https://aogavrilov.com/hi/research/codec-bottleneck-diagnosis/",
          "markdown_url": "https://aogavrilov.com/hi/research/codec-bottleneck-diagnosis/index.md"
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          "url": "https://aogavrilov.com/fr/research/codec-bottleneck-diagnosis/",
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          "title": "Self-Edit: Fault-Aware Code Editor for Code Generation",
          "arxiv_id": "2305.04087",
          "url": "https://arxiv.org/abs/2305.04087"
        },
        {
          "title": "Coeditor: Leveraging Contextual Changes for Multi-round Code Auto-editing",
          "arxiv_id": "2305.18584",
          "url": "https://arxiv.org/abs/2305.18584"
        },
        {
          "title": "PAFT: Preservation Aware Fine-Tuning for Minimal-Edit Program Repair",
          "arxiv_id": "2604.03113",
          "url": "https://arxiv.org/abs/2604.03113"
        },
        {
          "title": "Constrained Decoding of Diffusion LLMs with Context-Free Grammars",
          "arxiv_id": "2508.10111",
          "url": "https://arxiv.org/abs/2508.10111"
        },
        {
          "title": "Neural Discrete Representation Learning",
          "arxiv_id": "1711.00937",
          "url": "https://arxiv.org/abs/1711.00937"
        },
        {
          "title": "Simple and Effective Masked Diffusion Language Models",
          "arxiv_id": "2406.07524",
          "url": "https://arxiv.org/abs/2406.07524"
        },
        {
          "title": "An Empirical Study on the Potential of LLMs in Automated Software Refactoring",
          "arxiv_id": "2411.04444",
          "url": "https://arxiv.org/abs/2411.04444"
        },
        {
          "title": "SWE-Refactor: A Repository-Level Benchmark for Real-World LLM-Based Code Refactoring",
          "arxiv_id": "2602.03712",
          "url": "https://arxiv.org/abs/2602.03712"
        },
        {
          "title": "EfficientEdit: Accelerating Code Editing via Edit-Oriented Speculative Decoding",
          "arxiv_id": "2506.02780",
          "url": "https://arxiv.org/abs/2506.02780"
        },
        {
          "title": "Hydra: Efficient, Correct Code Generation via Checkpoint-and-Rollback Support",
          "arxiv_id": "2605.15238",
          "url": "https://arxiv.org/abs/2605.15238"
        }
      ],
      "markdown_url": "https://aogavrilov.com/research/discrete-latent-generation/index.md"
    }
  ]
}