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编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "SvvZIdo941", "title": "Learning a PRECISE language for small-molecule binding", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "e7DIU2hXBJ", "title": "SimPersona: Learning Discrete Buyer Personas from Raw Clickstreams for Grounded E-commerce Agents", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "Zinxc0GU6h", "title": "Scale-aware Identifiers for Generative Recommendation", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "8o38qsTBNE", "title": "Attentive Vector Quantization", "reason": "对图像 latent tokens 进行 VQ/codebook 学习，非 attention 激活或 KV 数值量化。" }, { "id": "6Y6ZbdvW7Q", "title": "Compositional Discrete Forecasting with Orthogonal Frequency-Band Codebooks", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": 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token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "YQ0Q2nxTD0", "title": "Hyperbolic Representation Learning for Multimodal-to-Unimodal Transfer in EEG-based Auditory Attention Decoding", "reason": "EEG 与辅助生理信号的共享表示码本，用于 auditory-attention 分类。" }, { "id": "ObTlBT032Y", "title": "Consolidating Specialist Representations into a Shared World Model", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "VDrqHWzZ3I", "title": "PointSTAR: Continuous Next-Scale Autoregressive Generation for 3D Point Cloud", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "56DlGfzPIB", "title": "DisAb: Self-Consistent Antibody Sequence--Structure Co-Design with Multi-Chain All-Atom Discrete Diffusion", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "WbDQOU4X2w", "title": "Scaling Collider Event Generation with Residual-Quantized Tokens", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "0bsGLcFOQt", "title": "High-Fidelity Song Generation with Joint Discrete and Continuous Modeling", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "h89pC2CMDo", "title": "Next Concept Prediction Leads to Stronger and More Scalable Language Models", "reason": "VQ 学习 hidden-state concept vocabulary，用于语义预训练，不是低精度模型计算。" }, { "id": "yDwwnEdFu0", "title": "MoTween: In-Betweening for Part-wise Text-to-Motion Generation", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "qhAZfg9j2q", "title": "AC-Scoreq: Amplitude-Coupled Scalable Complex 2-bit Residual Quantization for Efficient Vector Retrieval", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "0D7ikGwiUp", "title": "Decoupled Multi-Codebook Quantization", "reason": "多码本 VQ 用于数据向量重建/检索表示，未涉及模型权重或 KV 数值压缩。" }, { "id": "wbey6FqqUN", "title": "Tokenize the Physics, Not the Pixels: TessTok and Fold-Flow Quantization for Material Microstructures", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "f1nZgOniEs", "title": "GraphToken: A General Graph Vocabulary for Text-attributed Graph Foundation Models", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "Me4aIRmI32", "title": "When Does Quantization Form Reusable Concepts?", "reason": "分析表示 tokens 是否形成可复用 concepts，非模型参数/运行时数值量化。" }, { "id": "H2hUJf5Djl", "title": "Fast Multivariate Time Series Generation via Sampling from a Spherical Manifold", "reason": "连续 spherical manifold 时序生成；明确不使用 VQ，相关术语仅为背景对比。" }, { "id": "V2B53OQXU5", "title": "Learned Image Compression with Vector Quantization-Based Prior Modeling", "reason": "图像 codec 的 latent prior/码率编码，非模型数值精度量化。" }, { "id": "eev2PiUqvA", "title": "Target First, Background Later: Region-Progressive Latent Compression for Satellite Image Transmission", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "049lqWfTaP", "title": "TRANSFERRING ACTIVITY PROTOTYPES FOR ZERO- SHOT CROSS-CITY MOBILITY GENERATION", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "cDX4HofspM", "title": "Coverage-Aware Reasoning with Medical Tokens for Diagnosis Prediction", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "9kGZM8yCzA", "title": "BioSQ: Frequency-Aware Neural Compression of Biosignals", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "cCw8Ca0yEx", "title": "LaFree World Model: Label-Free Pretraining for Action-Conditioned JEPA World Models", "reason": "quantized latent actions 为 world-model 数据/动作表示。" }, { "id": "r0uyAXBe0E", "title": "Real-Time Sign Language Translation by Prototype-Assisted Causal Streaming", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "gGu3JNZGa5", "title": "A discrete generative model of neuronal spiking activity on microelectrode arrays", "reason": "量化实验神经脉冲数据的时空 motifs，非 SNN 模型参数/计算量化。" }, { "id": "W5kL3vlfvt", "title": "How Much of Your Codebook Survives Denoising?", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "pkbH50xGeO", "title": "Visual Tokenization with Latent Anchored Binary Quantization", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "lhzc2cZxzb", "title": "Beyond Reconstruction: Understanding Discrete Representations for Autoregressive Wearable Sensor Data Generation", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "aYNRC1ULAJ", "title": "Generative Retrieval for Reverse Target Fishing with a Proteome-Scale Benchmark", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "GBrSknDrcb", "title": "TRACE: Temporal Risk Assessment and Concept Editing for Safer Language Generation", "reason": "activation concept 识别与 safety steering，未压缩激活的数值精度或存储。" }, { "id": "KzObdwhaKi", "title": "Steering intonational tunes in text-to-speech synthesis", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "bOzJySQLDS", "title": "Learning Behavioural Abstractions For Programmatic Policies", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "c62T490HwA", "title": "When do discrete actions make good continuous controllers?", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "ax56hvx2w2", "title": "Subdivision Measure Matching: Generative Modeling in Integral Form", "reason": "生成数据域的递归细分与 measure matching，非模型数值精度量化。" }, { "id": "mtqSFJTgJC", "title": "LEXA: Learning a lexicon of gene-programme states from single-cell transcriptomes", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "hb8ZpFcUzY", "title": "Exploring Token-Space Manipulation in Latent Audio Tokenizers", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "QKkJNRYyZ9", "title": "Asymptotically Optimal Quantizer-Guided Sinkhorn Alignment for Mitigating Codebook Collapse", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "dmxUPwR9YV", "title": "Role-GAD: Role-Aware Graph Anomaly Detection via Dual-Codebook Vector Quantization", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "kNBGZoi3NC", "title": "ActWeave: Efficient Action Generation for Vision-Language-Action Models via Hierarchical Autoregression and Wavefront Decoding", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "1fjO9ha1Ep", "title": "KiRoSe: Using Kinematic Data for Efficient Segmentation of Robot Demonstrations", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "gC6mdwY8O7", "title": "SAuBER: Neural Audio Coding via a Learnable MDCT Tokenizer and Energy-Based Refinement", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "YJ8Ar8qmTF", "title": "Dual-Process Atomic Skill Learning: Decoupling Semantic Reasoning and Real-Time Control", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "WMtfpBzuVb", "title": "StimEM: Stimulus-Conditioned Masked Modeling for Eye-Movement Representation Learning", "reason": "FSQ 量化眼动波形的 ocular tokenizer。" }, { "id": "GjbqKYphGW", "title": "Act on the Low Frequencies: Prefix Execution of Vision-Language-Action Chunks", "reason": "官网摘要中的量化用于数据向量、离散 token、潜表示或 codec 编码；研究目标不属于模型权重、激活数值精度、KV 缓存、梯度/优化器状态的低精度量化。" }, { "id": "vBW7hBaTsu", "title": "DQ-PAD: Discriminative Quantized Prototype Alignment for Adversarial Patch Defense", "reason": "VQ-VAE 图像表示原型用于 patch detection，非检测器参数数值量化。" } ] }