手机端实时学习用户情绪，智能匹配同频好友的音乐偏好。
MeloTune: On-Device Arousal Learning and Peer-to-Peer Mood Coupling for Proactive Music Curation
- 用双网络架构在本地预测情绪变化并融合好友情绪数据
- 情绪预测准确率达96.6%，22次播放后对流行音乐信心达满
- 全流程本地运行，适合注重隐私的个性化音乐推荐场景
MeloTune 是部署于 iPhone 的音乐代理系统，采用网格记忆协议（MMP）与符号-向量注意力融合（SVAF），实现情感感知的主动音乐推荐与同伴情绪联动。每个设备运行两个闭式连续时间（CfC）网络：私有监听级CfC预测短期情绪轨迹（基于Russell环形模型），驱动主动推荐；共享网格运行级CfC在MMP第6层整合共听同伴的认知记忆块（CMBs）。CfC隐状态不跨设备传输，仅结构化CMBs共享。个人唤醒函数（PAF）取代传统音频强度到心理唤醒的线性映射，通过跳过、完成、收藏、音量等行为信号及用户声明情绪与机器推断的偏差进行个体化学习。同一首歌对不同用户生成不同唤醒预测。模型参数量为94,552，验证集上轨迹平均绝对误差0.414，模式准确率96.6%，意图准确率69.4%。一次真实部署中（46个观察，覆盖11种流派）显示学习回路端到端运作，流行音乐在22次观察后达到完全置信。所有推理均通过CoreML在设备端完成。据我们所知，这是首个在消费级移动硬件上部署MMP/SVAF的生产系统。配套SDK（sym-swift v0.3.78, SYMCore v0.3.7）强制执行协议一致性。音乐是案例，底层架构是贡献。
原文摘要 · Abstract (English)
MeloTune is an iPhone-deployed music agent that instantiates the Mesh Memory Protocol (MMP) and Symbolic-Vector Attention Fusion (SVAF) as a production system for affect-aware music curation with peer-to-peer mood coupling. Each device runs two closed-form continuous-time (CfC) networks: a private listener-level CfC that predicts a short-horizon affective trajectory on Russell's circumplex and drives proactive curation, and a shared mesh-runtime CfC at MMP Layer 6 that integrates Cognitive Memory Blocks (CMBs) from co-listening peers. CfC hidden states never cross the wire; only structured CMBs do. A Personal Arousal Function (PAF) replaces the standard linear mapping from audio intensity to psychological arousal with a per-listener learned adjustment, trained from behavioral signals (skip, completion, favorite, volume) and from drift between user-declared mood and machine inference. The same track receives different arousal predictions for different listeners. The model (94,552 parameters) achieves trajectory MAE 0.414, pattern accuracy 96.6%, and intent accuracy 69.4% on held-out validation. PAF evidence from a live deployment session (46 observations across 11 genres) demonstrates that the learning loop operates end-to-end, with pop reaching full confidence after 22 observations. All inference runs on-device via CoreML. To our knowledge, this is the first production deployment of MMP/SVAF on consumer mobile hardware. The accompanying SDK (sym-swift v0.3.78, SYMCore v0.3.7) enforces strict protocol conformance. Music is the case study; the substrate is the contribution.
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