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Xinrui Chen

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

Prune&Comp: Free Lunch for Layer-Pruned LLMs via Iterative Pruning with Magnitude Compensation

Xinrui Chen
Hongxing Zhang
Fanyi Zeng
Yongxian Wei
Yizhi Wang
Xitong Ling
Guanghao Li
Chun Yuan

Layer pruning is a viable technique for compressing large language models while achieving acceleration proportional to the pruning ratio. In this work, we identify that removing any layer induces a magnitude gap in hidden states, and demonstrate that a simple compensation operation leads to superior performance in iterative layer pruning. This key observation motivates us to propose Prune&Comp, a novel, plug-and-play iterative layer pruning scheme that leverages magnitude compensation to mitigate such gaps in a training-free manner. Specifically, we first estimate the magnitude gap of layer removal and then eliminate it by rescaling the remaining weights offline. We further demonstrate the advantages of Prune&Comp in improving the stability of iterative pruning. When integrated with an iterative prune-and-compensate loop, Prune&Comp consistently enhances existing layer pruning metrics. For instance, when 5 layers of LLaMA-3-8B are pruned with the prevalent Taylor+ metric, Prune&Comp reduces PPL from 512.78 to 16.34 and retains 90.57% of the original performance across 9 question-answering tasks, outperforming the baseline by 24.72%.

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NeurIPS Conference 2025 Conference Paper

A Simple Linear Patch Revives Layer-Pruned Large Language Models

Xinrui Chen
Haoli Bai
Tao Yuan
ruikang liu
Kang Zhao
Xianzhi Yu
Lu Hou
Tian Guan

Layer pruning has emerged as a widely used technique for compressing large language models (LLMs). However, existing layer pruning approaches often incur substantial performance degradation. We identify the majority of this degradation to a single yet previously overlooked issue: \textit{the mismatch of activation magnitudes at the pruning interface}. The pre-interface activations exhibit significantly different scales from the post-interface ones, causing the distributional shift as it propagates through the remaining layers. To address this issue, we introduce \textsc{LinearPatch}, a lightweight and plug-and-play technique that fuses two operations into one matrix multiply at the pruning interface: (i) a Hadamard transformation that suppresses massive outliers at particular tokens and (ii) a channel-wise scaling that aligns activation statistics. On LLaMA-3-8B, \textsc{LinearPatch} preserves up to \textbf{94. 15\%} of the original model's performance when pruning 5 out of 32 layers, outperforming the previous state of the art by \textbf{4\%}. The patch can be further refined with 5K unlabeled samples via memory-efficient offline distillation, pushing the retention to 95. 16\% within only 30 minutes on a single GPU. Code is available at \url{https: //github. com/chenxinrui-tsinghua/LinearPatch}.

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NeurIPS Conference 2025 Conference Paper

ToF-IP: Time-of-Flight Enhanced Sparse Inertial Poser for Real-time Human Motion Capture

Yuan Yao
Shifan Jiang
Yangqing Hou
Chengxu Zuo
Xinrui Chen
Shihui Guo
Yipeng Qin

Sparse inertial measurement units (IMUs) provide a portable, low-cost solution for human motion tracking but struggle with error accumulation from drift and sensor noise when estimating joint position through time-based linear acceleration integration (i. e. , indirect measurement). To address this, we propose ToF-IP, a novel 3D full-body pose estimation system that integrates Time-of-Flight (ToF) sensors with sparse IMUs. The distinct advantage of our approach is that ToF sensors provide direct distance measurements, effectively mitigating error accumulation without relying on indirect time-based integration. From a hardware perspective, we maintain the portability of existing solutions by attaching ToF sensors to selected IMUs with a negligible volume increase of just 3\%. On the software side, we introduce two novel techniques to enhance multi-sensor integration: (i) a Node-Centric Data Integration strategy that leverages a Transformer encoder to explicitly model both intra-node and inter-node data integration by treating each sensing node as a token; and (ii) a Dynamic Spatial Positional Encoding scheme that encodes the continuously changing spatial positions of wearable nodes as motion-conditioned functions, enabling the model to better capture human body dynamics in the embedding space. Additionally, we contribute a 208-minute human motion dataset from 10 participants, including synchronized IMU-ToF measurements and ground-truth from optical tracking. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches such as PNP, achieving superior accuracy in tracking complex and slow motions like Tai Chi, which remains challenging for inertial-only methods.

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ICML Conference 2024 Conference Paper

Position: Towards Unified Alignment Between Agents, Humans, and Environment

Zonghan Yang
An Liu
Zijun Liu
Kaiming Liu
Fangzhou Xiong
Yile Wang 0001
Zeyuan Yang 0002
Qingyuan Hu

The rapid progress of foundation models has led to the prosperity of autonomous agents, which leverage the universal capabilities of foundation models to conduct reasoning, decision-making, and environmental interaction. However, the efficacy of agents remains limited when operating in intricate, realistic environments. In this work, we introduce the principles of U nified A lignment for A gents ( UA $^2$), which advocate for the simultaneous alignment of agents with human intentions, environmental dynamics, and self-constraints such as the limitation of monetary budgets. From the perspective of UA $^2$, we review the current agent research and highlight the neglected factors in existing agent benchmarks and method candidates. We also conduct proof-of-concept studies by introducing realistic features to WebShop, including user profiles demonstrating intentions, personalized reranking reflecting complex environmental dynamics, and runtime cost statistics as self-constraints. We then follow the principles of UA $^2$ to propose an initial design of our agent and benchmark its performance with several candidate baselines in the retrofitted WebShop. The extensive experimental results further prove the importance of the principles of UA $^2$. Our research sheds light on the next steps of autonomous agent research with improved general problem-solving abilities.

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NeurIPS Conference 2023 Conference Paper

TexQ: Zero-shot Network Quantization with Texture Feature Distribution Calibration

Xinrui Chen
Yizhi Wang
Renao Yan
Yiqing Liu
Tian Guan
Yonghong He

Quantization is an effective way to compress neural networks. By reducing the bit width of the parameters, the processing efficiency of neural network models at edge devices can be notably improved. Most conventional quantization methods utilize real datasets to optimize quantization parameters and fine-tune. Due to the inevitable privacy and security issues of real samples, the existing real-data-driven methods are no longer applicable. Thus, a natural method is to introduce synthetic samples for zero-shot quantization (ZSQ). However, the conventional synthetic samples fail to retain the detailed texture feature distributions, which severely limits the knowledge transfer and performance of the quantized model. In this paper, a novel ZSQ method, TexQ is proposed to address this issue. We first synthesize a calibration image and extract its calibration center for each class with a texture feature energy distribution calibration method. Then, the calibration centers are used to guide the generator to synthesize samples. Finally, we introduce the mixup knowledge distillation module to diversify synthetic samples for fine-tuning. Extensive experiments on CIFAR10/100 and ImageNet show that TexQ is observed to perform state-of-the-art in ultra-low bit width quantization. For example, when ResNet-18 is quantized to 3-bit, TexQ achieves a 12. 18% top-1 accuracy increase on ImageNet compared to state-of-the-art methods. Code at https: //github. com/dangsingrue/TexQ.

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