LPDP：用于可变长度DNA生成的推理时间奖励控制
LPDP: Inference-Time Reward Control for Variable-Length DNA Generation with Edit Flows
AI总结：
本文提出LPDP，一种无需训练的局部重解算子，用于可变长度DNA编辑动作生成。通过局部离散程序聚焦于连贯的替换、插入和删除子图，并利用硬Max或软LSE备份聚合局部延续，用于增强子优化和外显子内含子外显子修复。
摘要：
我们研究了最近的Edit Flows在DNA序列生成中的应用，用于推理时间奖励控制。与大多数奖励引导的DNA生成框架不同，Edit Flows能够通过生物合理的方式生成可变长度的DNA。我们提出了Local Perturbation Discrete Programming (LPDP)，一种无需训练的、中间状态和动作感知的局部重解算子，用于推理时间的可变长度DNA编辑动作生成。在每个引导的rollout步骤中，LPDP对一步根编辑进行评分，保留一个近最佳的根带，并通过在子序列周围解决一个有界的局部离散程序来重新排名每个保留的根。该局部程序利用编辑动作的类型几何来聚焦于连贯的替换、插入或删除子图，并通过硬Max备份或软log-sum-exponential (LSE)备份聚合局部延续。我们分别在两种模式中实例化LPDP：前加载奖励倾斜用于增强子优化，其中早期编辑对建立全局调控序列结构至关重要；后加载奖励倾斜用于外显子内含子外显子修复，其中后期编辑微调剪接边界上下文。
英文摘要：
We study the application of recent Edit Flows for inference-time reward control for DNA sequence generation. Unlike most reward-guided DNA generation frameworks, which operate on fixed-length sequence spaces, Edit Flows have a potential to generate variable-length DNA through biologically plausible insertion, deletion, and substitution operations. In particular, we propose Local Perturbation Discrete Programming (LPDP), a training-free, intermediate-state and action-aware local re-solving operator for variable-length DNA edit-action generators at inference time. More specifically, at each guided rollout step, LPDP scores one-step root edits, retains a near-best root band, and re-ranks each retained root by solving a bounded local discrete program around its child sequence. This local program uses the typed geometry of edit actions to focus on coherent substitution, insertion, or deletion subgraphs, and aggregates local continuations with either a hard Max backup or a soft log-sum-exponential (LSE) backup. We instantiate LPDP in two regimes: front-loaded reward tilting for enhancer optimization, where early edits are critical for establishing global regulatory sequence structure, and back-loaded reward tilting for exon-intron-exon inpainting, where late edits fine-tune splice-boundary contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
