Tong Zhang

Ke Yi, Zengke Liu, Jianwei Zhang et al.

Large language models have demonstrated promising capabilities upon scaling up parameters. However, serving large language models incurs substantial computation and memory movement costs due to their large scale. Quantization methods have been employed to reduce service costs and latency. Nevertheless, outliers in activations hinder the development of INT4 weight-activation quantization. Existing approaches separate outliers and normal values into two matrices or migrate outliers from activations to weights, suffering from high latency or accuracy degradation. Based on observing activations from large language models, outliers can be classified into channel-wise and spike outliers. In this work, we propose Rotated Runtime Smooth (RRS), a plug-and-play activation smoother for quantization, consisting of Runtime Smooth and the Rotation operation. Runtime Smooth (RS) is introduced to eliminate channel-wise outliers by smoothing activations with channel-wise maximums during runtime. The rotation operation can narrow the gap between spike outliers and normal values, alleviating the effect of victims caused by channel-wise smoothing. The proposed method outperforms the state-of-the-art method in the LLaMA and Qwen families and improves WikiText-2 perplexity from 57.33 to 6.66 for INT4 inference.

Rujing Yao, Yiquan Wu, Tong Zhang et al.

The rise of large language models has opened new avenues for users seeking legal advice. However, users often lack professional legal knowledge, which can lead to questions that omit critical information. This deficiency makes it challenging for traditional legal question-answering systems to accurately identify users' actual needs, often resulting in imprecise or generalized advice. In this work, we develop a legal question-answering system called Intelligent Legal Assistant, which interacts with users to precisely capture their needs. When a user poses a question, the system requests that the user select their geographical location to pinpoint the applicable laws. It then generates clarifying questions and options based on the key information missing from the user's initial question. This allows the user to select and provide the necessary details. Once all necessary information is provided, the system produces an in-depth legal analysis encompassing three aspects: overall conclusion, jurisprudential analysis, and resolution suggestions.

Jipeng Zhang, Kehao Miao, Renjie Pi et al.

Reinforcement Fine-Tuning (RFT) with verifiable rewards has advanced large language models but remains underexplored for Vision-Language (VL) models. The Vision-Language Reward Model (VL-RM) is key to aligning VL models by providing structured feedback, yet training effective VL-RMs faces two major challenges. First, the bootstrapping dilemma arises as high-quality training data depends on already strong VL models, creating a cycle where self-generated supervision reinforces existing biases. Second, modality bias and negative example amplification occur when VL models hallucinate incorrect visual attributes, leading to flawed preference data that further misguides training. To address these issues, we propose an iterative training framework leveraging vision experts, Chain-of-Thought (CoT) rationales, and Margin-based Rejection Sampling. Our approach refines preference datasets, enhances structured critiques, and iteratively improves reasoning. Experiments across VL-RM benchmarks demonstrate superior performance in hallucination detection and multimodal reasoning, advancing VL model alignment with reinforcement learning.

Zhaozhi Wang, Conghu Li, Qixiang Ye et al.

Most parameter-efficient fine-tuning (PEFT) methods rely on low-rank representations to adapt models. However, these approaches often oversimplify representations, particularly when the underlying data has high-rank or high-frequency components. This limitation hinders the model's ability to capture complex data interactions effectively. In this paper, we propose a novel approach that models network weights by leveraging a combination of physical priors, enabling more accurate approximations. We use three foundational equations -- heat diffusion, wave propagation, and Poisson's steady-state equation -- each contributing distinctive modeling properties: heat diffusion enforces local smoothness, wave propagation facilitates long-range interactions, and Poisson's equation captures global equilibrium. To combine these priors effectively, we introduce the Mixture of Physical Priors Adapter (MoPPA), using an efficient Discrete Cosine Transform (DCT) implementation. To dynamically balance these priors, a route regularization mechanism is designed to adaptively tune their contributions. MoPPA serves as a lightweight, plug-and-play module that seamlessly integrates into transformer architectures, with adaptable complexity depending on the local context. Specifically, using MAE pre-trained ViT-B, MoPPA improves PEFT accuracy by up to 2.1% on VTAB-1K image classification with a comparable number of trainable parameters, and advantages are further validated through experiments across various vision backbones, showcasing MoPPA's effectiveness and adaptability. The code will be made public available.