Spatiotemporal forecasting has seen remarkable progress with the advent of deep learning, particularly with Spatiotemporal Graph Neural Networks (STGNNs). These models excel at answering the what question: predicting future numerical values with high accuracy. However, they fail to answer the crucial why question. In high-stakes domains such as meteorology, urban planning, and public health, this opacity creates a critical bottleneck for adoption. A model that predicts a severe pollution event without explaining its atmospheric drivers is a black box, limiting its trustworthiness and utility for decision-makers who need actionable, causal insights. To address this critical gap, I propose a long-term research project to develop Causal-LLM, a new class of foundation models for spatiotemporal data that are both predictively powerful and causally interpretable. My central thesis is that genuine interpretability cannot be an afterthought; it must be designed into the model's core learning process. By adapting the powerful Time-LLM reprogramming framework and introducing a novel training methodology I term causal data synthesis, Causal-LLM will learn to not only forecast future states but also to articulate the human-understandable causal narratives behind them. This research will make two primary contributions: (1) a novel hybrid architecture that synergizes the perceptual power of GNNs with the reasoning capabilities of LLMs for complex physical systems, and (2) a new training paradigm that explicitly teaches this mapping. A successful project would provide a blueprint for a new class of trustworthy foundation models for science, enabling applications such as a climate model that not only predicts a flood but also explains the atmospheric river causing it, empowering authorities to make more informed and trusted decisions.

Affordance grounding focuses on predicting the specific regions of objects that are associated with the actions to be performed by robots. It plays a vital role in the fields of human-robot interaction, human-object interaction, embodied manipulation, and embodied perception. Existing models often neglect the affordance shared among different objects because they lack the Chain-of-Thought(CoT) reasoning abilities, limiting their out-of-domain generalization and explicit reasoning capabilities. To address these challenges, we propose Affordance-R1, the first unified affordance grounding framework that integrates cognitive CoT guided Group Relative Policy Optimization (GRPO) within a reinforcement learning paradigm. Specifically, we designed a sophisticated affordance function, which contains format, perception, and cognition rewards to effectively guide optimization directions. Furthermore, we constructed a high-quality affordance-centric reasoning dataset, ReasonAff, to support training. Trained exclusively via reinforcement learning with GRPO and without explicit reasoning data, Affordance-R1 achieves robust zero-shot generalization and exhibits emergent test-time reasoning capabilities. Comprehensive experiments demonstrate that our model outperforms well-established methods and exhibits open-world generalization.

Zhiqing Cui, Binwu Wang, Qingxiang Liu, Yeqiang Wang, Zhengyang Zhou, Yuxuan Liang, Yang Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Air quality forecasting has attracted increasing attention as global air pollution worsens. Spatiotemporal graph neural networks have become a leading paradigm, thanks to their ability to capture complex spatial and temporal dynamics in Air Quality Index (AQI) data. However, existing methods remain limited by weak modeling of long-range temporal dependencies and insufficient integration of meteorological factors. Building on a publicly available nationwide air quality dataset spanning eight years, we propose MADGCN, a Meteorology-Aware Decoupled Spatio-Temporal Convolutional Network that jointly addresses long-horizon temporal modeling and meteorological context fusion. MADGCN includes a dynamic causality discovery module grounded in Granger causality, which captures time-varying causal relationships between meteorological conditions and AQI dynamics. The inferred causal structures further guide a causal graph convolution module and a PatchMixer module, enabling effective spatial interaction modeling and multiscale temporal dependency learning. Extensive experiments against 16 strong baselines show that MADGCN achieves competitive performance for long-horizon air pollution forecasting and generalizes well under high-pollution regimes.

Empathetic response generation necessitates the integration of emotional and intentional dynamics to foster meaningful interactions.Existing research either neglects the intricate interplay between emotion and intent, leading to suboptimal controllability of empathy, or resorts to large language models (LLMs), which incur significant computational overhead.In this paper, we introduce ReflectDiffu, a lightweight and comprehensive framework for empathetic response generation.This framework incorporates emotion contagion to augment emotional expressiveness and employs an emotion-reasoning mask to pinpoint critical emotional elements.Additionally, it integrates intent mimicry within reinforcement learning for refinement during diffusion.By harnessing an intent twice reflect mechanism of Exploring-Sampling-Correcting, ReflectDiffu adeptly translates emotional decision-making into precise intent actions, thereby addressing empathetic response misalignments stemming from emotional misrecognition.Through reflection, the framework maps emotional states to intents, markedly enhancing both response empathy and flexibility.Comprehensive experiments reveal that ReflectDiffu outperforms existing models regarding relevance, controllability, and informativeness, achieving stateof-the-art results in both automatic and human evaluations.

Air quality prediction plays a crucial role in the development of smart cities, garnering significant attention from both academia and industry. Current air quality prediction models encounter two major limitations: their high computational complexity limits scalability to nationwide datasets, and they often regard weather covariates as optional auxiliary information. In reality, weather covariates can have a substantial impact on air quality indices (AQI), exhibiting a significant causal association. In this paper, we first present a nationwide air quality dataset to address the lack of open-source, large-scale datasets in this field. Then we propose a causal learning model, CauAir, for air quality prediction that harnesses the powerful representation capabilities of the Transformer to explicitly model the causal association between weather covariates and AQI. To address the high complexity of traditional Transformers, we design CachLormer, which features two key innovations: a simplified architecture with redundant components removed, and a cache-attention mechanism that employs learnable embeddings for perceiving causal association between AQI and weather covariates in a coarsegrained perspective. We use information theory to illustrate the superiority of the proposed model. Finally, experimental results on three datasets with 28 as the baseline demonstrate that our model achieves competitive performance, while maintaining high training efficiency and low memory consumption. The source code is available at CauAir Official Repository.

Scientific diagrams are vital tools for communicating structured knowledge across disciplines. However, they are often published as static raster images, losing symbolic semantics and limiting reuse. While Multimodal Large Language Models (MLLMs) offer a pathway to bridging vision and structure, existing methods lack semantic control and structural interpretability, especially on complex diagrams. We propose Draw with Thought (DwT), a training-free framework that guides MLLMs to reconstruct diagrams into editable mxGraph XML code through cognitively inspired Chain-of-Thought reasoning. DwT enables interpretable and controllable outputs without model fine-tuning by dividing the task into two stages: Coarse-to-Fine Planning, which handles perceptual structuring and semantic specification, and Structure-Aware Code Generation, enhanced by format-guided refinement. To support evaluation, we release Plot2XML, a benchmark of 247 real-world scientific diagrams with gold-standard XML annotations. Extensive experiments across eight MLLMs show that our approach yields high-fidelity, semantically aligned, and structurally valid reconstructions, with human evaluations confirming strong alignment in both accuracy and visual aesthetics, offering a scalable solution for converting static visuals into structurally valid and renderable representations and advancing machine understanding of scientific graphics.

Infrared small target detection (IRSTD) remains challenging due to the extremely low signal-to-noise ratio (SNR). Existing methods struggle to balance accuracy and speed, especially under limited computational resources. To address these issues, we propose the frequency-spatial contextual fusion network (FSCFNet) based on You Only Look Once (YOLO) v10n architecture. Particularly, the novel frequency-spatial convolution (FSConv) is designed that decomposes input features via Haar Wavelet Transform. High-frequency cues focus on local details to highlight small targets, while low-frequency cues provide global information to complement spatial features. Subsequently, the asymmetric cross-domain attention (ACA) is developed to enhance the local central feature extraction, which reflects the typical spatial Gaussian pattern of small targets. Furthermore, we introduce the customized multi-scale receptive contextual Block (MRCB) to capture the long-range information by leveraging diverse dilated convolutions. In addition, the Wasserstein Distance Loss (WDL) is utilized to improve bounding box quality. Extensive experiments on three public datasets including IRSTD-1k, NUDT-SIRST, and NUAA-SIRST confirm the effectiveness of FSCFNet. Notably, FSCFNet surpasses the baseline by 4.7% in precision, 3.3% in recall, and 3.9% in AP@50 on IRSTD-1k, with only a 3.6% increase in parameters. FSCFNet provides a robust solution for real-time infrared surveillance systems under resource-constrained environments. More comparisons are shown in Fig. 1.

Abstract Intrusion detection represents an efficacious approach for addressing security concerns. However, given the substantial volume and high-dimensional nature of WLAN dataset features, existing methods exhibit limited effectiveness in feature extraction, thereby impacting classification performance. To address above problems, an improved deep neural network (DNN) model for WLAN intrusion detection was proposed. Firstly, the activation function and loss function of a single sparse autoencoders (SAE) were determined through experiments, followed by the addition of regularization terms to the autoencoder, to prevent the model from overfitting. Subsequently, multiple SAEs were employed for a stacked architecture. This configuration served the purpose of feature dimension reduction and facilitated the selection of suitable feature dimensions for training the dataset. The chosen features were then utilized as the input layer for a DNN, with a SoftMax classifier serving as the output layer. Secondly, to obtain better DNN model parameters, the grid search method was adopted to optimize the parameters of the DNN model, namely activation, epochs, batch_size, init_mode, and optimizer. The results were visualized for assessment and analysis. Finally, the receiver operating characteristic curves were generated to assess the performance of various models, the analysis results show that the model exhibited better classifier performance.