Juntao Liu

Liulu He, XuanAng Liu, Juntao Liu et al.

Existing quantization methods are fundamentally limited by rigid, integer-based bit-widths (e.g., 2, 3-bit), resulting in a ``deployment gap" where Large Language Models cannot be optimally fitted to specific memory budgets. To bridge this gap, we introduce LiftQuant, a novel framework that enables continuous bit-width control for true Pareto-optimal deployment. The core innovation is a ``lift-then-project" mechanism which approximates low-dimensional weight vectors by projecting a simple 1-bit lattice from a higher-dimensional ``lifted" space. Crucially, the effective bit-width is determined simply by the ratio of the lifted dimension to the original dimension, which allows the bit-width to be tuned quasi-continuous as the dimension is a flexible structural parameter. This projection generates a structured yet non-uniform codebook, capturing the expressive power of Vector Quantization (VQ). While beneficial over VQ, LiftQuant's decoding path relies solely on linear transformations and 1-bit uniform quantizers, retaining hardware-friendly nature. This flexibility is transformative: LiftQuant enables a 70B LLM to be compressed to 2.4 bits to precisely fit a 24GB GPU, where its performance significantly surpasses state-of-the-art 2-bit models fitted on the same device. Our code and ckpt is available at https://github.com/Heliulu/LiftQuant.

Hui Zhang, Juntao Liu, Zongkai Liu et al.

Instruction-based image editing aims to modify specific content within existing images according to user-provided instructions while preserving non-target regions. Beyond traditional object- and style-centric manipulation, text-centric image editing focuses on modifying, translating, or rearranging textual elements embedded within images. However, existing leading models often struggle to execute complex text editing precisely, frequently producing blurry or hallucinated characters. We attribute these failures primarily to the lack of specialized training paradigms tailored for text-centric editing, as well as the absence of large-scale datasets and standardized benchmarks necessary for a closed-loop training and evaluation system. To address these limitations, we present WeEdit, a systematic solution encompassing a scalable data construction pipeline, two benchmarks, and a tailored two-stage training strategy. Specifically, we propose a novel HTML-based automatic editing pipeline, which generates 330K training pairs covering diverse editing operations and 15 languages, accompanied by standardized bilingual and multilingual benchmarks for comprehensive evaluation. On the algorithmic side, we employ glyph-guided supervised fine-tuning to inject explicit spatial and content priors, followed by a multi-objective reinforcement learning stage to align generation with instruction adherence, text clarity, and background preservation. Extensive experiments demonstrate that WeEdit outperforms previous open-source models by a clear margin across diverse editing operations.

Aojie Jiang, Kang Zhu, Zhiheng Zhang et al.

In-network computing techniques, exemplified by NVLink Sharp (NVLS), offer a promising approach to addressing the communication bottlenecks in LLM inference by offloading collective operations, such as All-Reduce, to switches. However, the accelerator-centric architecture of NVLS suffers from two fundamental limitations: 1) it relies on GPU load instructions to trigger reduction operations, which means that the data reduced in the switch must be additionally transferred back to the initiating GPU rather than being broadcast directly, thereby introducing unnecessary communication overhead; 2) due to its architectural constraints, NVLS cannot offload operators that are not decomposable into memory-semantic instructions, such as the in-network quantization (INQ) proposed in this work. As a result, All-Reduce in NVLS must operate at FP16/BF16 precision, leading to substantial bandwidth waste.To address these limitations, we propose SCIN, the first switch-centric in-network architecture for shared-memory networks of AI accelerators, enabling both low-latency and high-bandwidth All-Reduce. Specifically, we introduce an in-switch accelerator (ISA) capable of initiating memory-semantic operations for in-network processing, together with a co-designed communication fabric that incurs negligible protocol overhead. By eliminating redundant data movement, SCIN delivers lower All-Reduce latency than NVLS. Moreover, by integrating a quantization module into the ISA, SCIN enables INQ for All-Reduce, reducing its precision to 8 bits and nearly doubling bandwidth with negligible accuracy loss. We also present a prototype of SCIN on a multi-FPGA system to demonstrate its feasibility and effectiveness. Experimental results show that our design accelerates All-Reduce by up to 8.7x for small messages and 3.8x for large messages, leading up to 1.74x faster TTFT and 1.34x faster TPOT on LLaMA-2 models.

Juntao Liu, Liqiang Niu, Wenchao Chen et al.

Existing visual token compression methods for Multimodal Large Language Models (MLLMs) predominantly operate as post-encoder modules, limiting their potential for efficiency gains. To address this limitation, we propose LaCo (Layer-wise Visual Token Compression), a novel framework that enables effective token compression within the intermediate layers of the vision encoder. LaCo introduces two core components: 1) a layer-wise pixel-shuffle mechanism that systematically merges adjacent tokens through space-to-channel transformations, and 2) a residual learning architecture with non-parametric shortcuts that preserves critical visual information during compression. Extensive experiments indicate that our LaCo outperforms all existing methods when compressing tokens in the intermediate layers of the vision encoder, demonstrating superior effectiveness. In addition, compared to external compression, our method improves training efficiency beyond 20% and inference throughput over 15% while maintaining strong performance.

Mingliang Zhang, Zhen Cao, Juntao Liu et al.

In this paper, we introduce WeLayout, a novel system for segmenting the layout of corporate documents, which stands for WeChat Layout Analysis System. Our approach utilizes a sophisticated ensemble of DINO and YOLO models, specifically developed for the ICDAR 2023 Competition on Robust Layout Segmentation. Our method significantly surpasses the baseline, securing a top position on the leaderboard with a mAP of 70.0. To achieve this performance, we concentrated on enhancing various aspects of the task, such as dataset augmentation, model architecture, bounding box refinement, and model ensemble techniques. Additionally, we trained the data separately for each document category to ensure a higher mean submission score. We also developed an algorithm for cell matching to further improve our performance. To identify the optimal weights and IoU thresholds for our model ensemble, we employed a Bayesian optimization algorithm called the Tree-Structured Parzen Estimator. Our approach effectively demonstrates the benefits of combining query-based and anchor-free models for achieving robust layout segmentation in corporate documents.