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This paper proposes a unified rate-distortion perspective on discrete visual tokenization, resolving key questions about quantization objectives and comparisons, and shows that vector quantization achieves the lowest distortion under controlled conditions.
Codebook Agent introduces a vector-quantized autoencoder method to design query-adaptive communication topologies for LLM multi-agent systems, improving accuracy and reducing token consumption on multiple benchmarks.
The paper proposes P2E-VQ, a retrieval-augmented framework that enhances PPG representations by retrieving ECG-linked information via discrete patch retrieval, improving downstream tasks without requiring ECG during inference.
This paper introduces VQ-bench, a unified framework for composing and benchmarking vector quantization algorithms, re-expressing 25 common quantizers as pipelines of primitives and publishing reproducible benchmarks.
This paper proposes a three-stream detector that combines residual-stream motion with coarse regions and fine directions to better identify reasoning errors in LLMs, improving selection accuracy by up to 12% over state-of-the-art displacement-only methods.
This paper proposes NOVA-KV, a transform-coding approach to KV cache quantization that uses attention-preserving transforms to allocate bits where queries actually attend, improving long-context retrieval accuracy at low bit rates compared to prior methods.
Introduces FairDiffuseVQVAE, a two-stage tabular diffusion model that achieves fairness at sampling time by conditioning on protected attributes, outperforming prior fair tabular generators on demographic parity and equalized odds.
Proposes LC-QAT, a 2-bit weight-only vector quantization aware training framework for LLMs that uses a learned affine mapping to enable end-to-end training, achieving state-of-the-art results with only 0.1%-10% of training data.
UniSVQ proposes a unified 2-bit quantization framework that bridges scalar and vector quantization by parameterizing codewords as an affine transform of integer lattices, achieving state-of-the-art performance among scalar methods and matching vector methods with higher throughput.
LiftQuant introduces a 'lift-then-project' mechanism enabling continuous (non-integer) bit-width quantization for LLMs, allowing precise fitting to hardware memory budgets. The framework compresses a 70B LLM to 2.4-bit to fit a 24GB GPU, outperforming state-of-the-art 2-bit models.
This paper introduces inner product aware quantization methods that preserve inner products with unseen vectors, developing fast and adaptive algorithms with provable guarantees, achieving 2-10x speedup over prior ASQ methods.
Shard is a drop-in HuggingFace Cache that achieves 10x KV cache compression for Llama-3.1-8B by using PCA plus int4 quantization on K and Hadamard rotation plus vector quantization on V, without accuracy loss on benchmarks.
This paper introduces SDFlow, a similarity-driven flow matching framework for time series generation that addresses exposure bias in autoregressive models. It achieves state-of-the-art performance and inference speedups by operating in the frozen VQ latent space with low-rank manifold decomposition.
IndexTTS is an enhanced text-to-speech system that combines XTTS and Tortoise models with hybrid character-pinyin modeling and optimized vector quantization, achieving improved naturalness, controllable pronunciation, and faster inference than existing open-source TTS systems.