Libin Yang

Network traffic analysis is fundamentally challenged by the need to accurately identify malicious behaviors and application types from massive, structurally heterogeneous packet data. Existing deep learning and Large Language Model (LLM)-based methods typically rely on shared parameter spaces optimized for isolated, single-task scenarios. Consequently, when deployed in dynamic multi-task environments, these approaches may be affected by cross-task gradient interference and negative transfer. To address these limitations, we propose TrafficMoE, an LLM-based Mixture-of-Experts (MoE) framework for supervised multi-task network traffic classification. Specifically, TrafficMoE formulates heterogeneous traffic tasks under a unified instruction-to-label generation paradigm by converting packet metadata into text. To mitigate task interference, the framework integrates a shared traffic expert to capture common semantics, multiple task-specialized experts to separate domain knowledge, and a lightweight context-aware router for automatic, sample-wise dispatching under mixed-task inputs. Furthermore, to support continual task evolution, we design a modular sequential expansion strategy that accommodates new tasks by adding a single expert and updating the router. Extensive evaluations across ten public benchmark datasets show that TrafficMoE achieves an average F1-score of 0.9823, while supporting modular task expansion with reduced adaptation overhead.

Automated chest X-ray report generation requires not only clinical accuracy but also transparent and interpretable diagnostic reasoning. In this work, we propose FiR-Rad, a two-stage framework that combines explicit structured reasoning with targeted fine-grained optimization. In the first stage, a supervised chain-of-thought approach guides the model to sequentially analyze and describe a comprehensive range of clinically significant thoracic abnormalities, ensuring clinically meaningful coverage. In the second stage, we introduce a segment-level reinforcement learning strategy based on Group Relative Policy Optimization (GRPO), which assigns precise rewards to each disease-specific reasoning step by evaluating the accuracy of corresponding findings in the synthesized report. This design provides direct feedback for intermediate reasoning and encourages consistency between detailed abnormality analysis and final diagnostic conclusions. Experimental results on the MIMIC-CXR and IU-Xray datasets demonstrate that our framework achieves state-of-the-art performance across clinical and linguistic metrics, with strong zero-shot generalization on IU-Xray. The proposed method significantly enhances interpretability and clinical accuracy, effectively addressing key limitations in automated radiology report generation.

Parameter-Efficient Fine-Tuning (PEFT) of Large Language Models (LLMs) in multitask scenarios is cutting-edge research currently. This paper introduces an innovative PEFT approach, named HybridMoE, by applying the Low-Rank Adaptation (LoRA) network into the Mixture-of-Expert (MoE) architecture. Considering the similarity and diversity between multiple tasks, we introduce the Multi-A MoE and Multi-B MoE architectures to allow experts of MoE to share either the decomposition $A$ matrix or $B$ matrix among themselves. HybridMoE integrates the Multi-A and Multi-B architectures, and the multiple tasks automatically learn to choose either of them through a gating network. Extensive experiments show that HybridMoE outperforms MoLoRA in performance on the HuggingFace Open LLM benchmark with various open-sourced LLMs, including LLaMA-2, Baichuan2, Qwen, and Yi. By sharing the parameters, HybridMoE requires fewer trainable parameters and is more efficient. The experimental results demonstrate that HybridMoE provides a novel solution for fine-tuning LLMs, striking a balance between performance and resource allocation, and opening up possibilities for broader research directions.

Direct Preference Optimization (DPO) has gained significant attention for its simplicity and computational efficiency in aligning large language models (LLMs). Recent advancements have extended DPO to multimodal scenarios, achieving strong performance. However, traditional DPO relies on binary preference optimization, rewarding or penalizing entire responses without considering fine-grained segment correctness, leading to suboptimal solutions. The root of this issue lies in the absence of fine-grained supervision during the optimization process. To address this, we propose Adaptive Sentence-level Preference Optimization (ASPO), which evaluates individual sentences for more precise preference optimization. By dynamically calculating adaptive rewards at the sentence level based on model predictions, ASPO enhances response content assessment without additional models or parameters. This significantly improves the alignment of multimodal features. Extensive experiments show that ASPO substantially enhances the overall performance of multimodal models.

Existing Vision-Language Pretraining (VLP) methods have achieved remarkable improvements across a variety of vision-language tasks, confirming their effectiveness in capturing coarse-grained semantic correlations. However, their capability for fine-grained understanding, which is critical for many nuanced vision-language applications, remains limited. Prevailing VLP models often overlook the intricate distinctions in expressing different modal features and typically depend on the similarity of holistic features for cross-modal interactions. Moreover, these models directly align and integrate features from different modalities, focusing more on coarse-grained general representations, thus failing to capture the nuanced differences necessary for tasks demanding a more detailed perception. In response to these limitations, we introduce Negative Augmented Samples(NAS), a refined vision-language pretraining model that innovatively incorporates NAS to specifically address the challenge of fine-grained understanding. NAS utilizes a Visual Dictionary(VD) as a semantic bridge between visual and linguistic domains. Additionally, it employs a Negative Visual Augmentation(NVA) method based on the VD to generate challenging negative image samples. These samples deviate from positive samples exclusively at the token level, thereby necessitating that the model discerns the subtle disparities between positive and negative samples with greater precision. Comprehensive experiments validate the efficacy of NAS components and underscore its potential to enhance fine-grained vision-language comprehension.

Despite the significant success of Large Vision-Language models(LVLMs), these models still suffer hallucinations when describing images, generating answers that include non-existent objects. It is reported that these models tend to over-focus on certain irrelevant image tokens that do not contain critical information for answering the question and distort the output. To address this, we propose an Instruction-Aligned Visual Attention(IAVA) approach, which identifies irrelevant tokens by comparing changes in attention weights under two different instructions. By applying contrastive decoding, we dynamically adjust the logits generated from original image tokens and irrelevant image tokens, reducing the model's over-attention to irrelevant information. The experimental results demonstrate that IAVA consistently outperforms existing decoding techniques on benchmarks such as MME, POPE, and TextVQA in mitigating object hallucinations. Our IAVA approach is available online at https://github.com/Lee-lab558/IAVA.