Yizhou Ma

Puneet Mishra, Martijntje Vollebregt, Yizhou Ma et al.

Background In analytical chemistry, spatial information about materials is commonly captured through imaging techniques, such as traditional color cameras or with advanced hyperspectral cameras and microscopes. However, efficiently extracting and analyzing this spatial information for exploratory and predictive purposes remains a challenge, especially when using traditional chemometric methods. Recent advances in deep learning and artificial intelligence have significantly enhanced image processing capabilities, enabling the extraction of multiscale deep features that are otherwise challenging to capture with conventional image processing techniques. Despite the wide availability of open-source deep learning models, adoption in analytical chemistry remains limited because of the absence of structured, step-by-step guidance for implementing these models. Results This tutorial aims to bridge this gap by providing a step-by-step guide for applying deep learning approaches to extract spatial information from imaging data and integrating it with other data sources, such as spectral information. Importantly, the focus of this work is not on training deep learning models for image processing but on using existing open source models to extract deep features from imaging data. Significance The tutorial provides MATLAB code tutorial demonstrations, showcasing the processing of imaging data from various imaging modalities commonly encountered in analytical chemistry. Readers must run the tutorial steps on their own datasets using the codes presented in this tutorial.

Yuhang Zang, Xiaoyi Dong, Pan Zhang et al. · pku

Despite the promising performance of Large Vision Language Models (LVLMs) in visual understanding, they occasionally generate incorrect outputs. While reward models (RMs) with reinforcement learning or test-time scaling offer the potential for improving generation quality, a critical gap remains: publicly available multi-modal RMs for LVLMs are scarce, and the implementation details of proprietary models are often unclear. We bridge this gap with InternLM-XComposer2.5-Reward (IXC-2.5-Reward), a simple yet effective multi-modal reward model that aligns LVLMs with human preferences. To ensure the robustness and versatility of IXC-2.5-Reward, we set up a high-quality multi-modal preference corpus spanning text, image, and video inputs across diverse domains, such as instruction following, general understanding, text-rich documents, mathematical reasoning, and video understanding. IXC-2.5-Reward achieves excellent results on the latest multi-modal reward model benchmark and shows competitive performance on text-only reward model benchmarks. We further demonstrate three key applications of IXC-2.5-Reward: (1) Providing a supervisory signal for RL training. We integrate IXC-2.5-Reward with Proximal Policy Optimization (PPO) yields IXC-2.5-Chat, which shows consistent improvements in instruction following and multi-modal open-ended dialogue; (2) Selecting the best response from candidate responses for test-time scaling; and (3) Filtering outlier or noisy samples from existing image and video instruction tuning training data. To ensure reproducibility and facilitate further research, we have open-sourced all model weights and training recipes at https://github.com/InternLM/InternLM-XComposer/tree/main/InternLM-XComposer-2.5-Reward

Yizhu Jiao, Xuchao Zhang, Zhaoyang Wang et al.

Current Large Language Models (LLMs) excel in general reasoning yet struggle with specialized tasks requiring proprietary or domain-specific knowledge. Fine-tuning large models for every niche application is often infeasible due to black-box constraints and high computational overhead. To address this, we propose a collaborative framework that pairs a specialized weak model with a general strong model. The weak model, tailored to specific domains, produces initial drafts and background information, while the strong model leverages its advanced reasoning to refine these drafts, extending LLMs' capabilities to critical yet specialized tasks. To optimize this collaboration, we introduce a collaborative feedback to fine-tunes the weak model, which quantifies the influence of the weak model's contributions in the collaboration procedure and establishes preference pairs to guide preference tuning of the weak model. We validate our framework through experiments on three domains. We find that the collaboration significantly outperforms each model alone by leveraging complementary strengths. Moreover, aligning the weak model with the collaborative preference further enhances overall performance.