Can LLMs Simulate Personas with Reversed Performance? A Benchmark for Counterfactual Instruction Following

Large Language Models (LLMs) are now increasingly widely used to simulate personas in virtual environments, leveraging their instruction-following capability. However, we discovered that even state-of-the-art LLMs cannot simulate personas with reversed performance (e.g., student personas with low proficiency in educational settings), which impairs the simulation diversity and limits the practical applications of the simulated environments. In this work, using mathematical reasoning as a representative scenario, we propose the first benchmark dataset for evaluating LLMs on simulating personas with reversed performance, a capability that we dub "counterfactual instruction following". We evaluate both open-weight and closed-source LLMs on this task and find that LLMs, including the OpenAI o1 reasoning model, all struggle to follow counterfactual instructions for simulating reversedly performing personas. Intersectionally simulating both the performance level and the race population of a persona worsens the effect even further. These results highlight the challenges of counterfactual instruction following and the need for further research.

Context-Aware Self-Adaptation for Domain Generalization

Domain generalization aims at developing suitable learning algorithms in source training domains such that the model learned can generalize well on a different unseen testing domain. We present a novel two-stage approach called Context-Aware Self-Adaptation (CASA) for domain generalization. CASA simulates an approximate meta-generalization scenario and incorporates a self-adaptation module to adjust pre-trained meta source models to the meta-target domains while maintaining their predictive capability on the meta-source domains. The core concept of self-adaptation involves leveraging contextual information, such as the mean of mini-batch features, as domain knowledge to automatically adapt a model trained in the first stage to new contexts in the second stage. Lastly, we utilize an ensemble of multiple meta-source models to perform inference on the testing domain. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on standard benchmarks.

Single Domain Generalization with Adversarial Memory

Domain Generalization (DG) aims to train models that can generalize to unseen testing domains by leveraging data from multiple training domains. However, traditional DG methods rely on the availability of multiple diverse training domains, limiting their applicability in data-constrained scenarios. Single Domain Generalization (SDG) addresses the more realistic and challenging setting by restricting the training data to a single domain distribution. The main challenges in SDG stem from the limited diversity of training data and the inaccessibility of unseen testing data distributions. To tackle these challenges, we propose a single domain generalization method that leverages an adversarial memory bank to augment training features. Our memory-based feature augmentation network maps both training and testing features into an invariant subspace spanned by diverse memory features, implicitly aligning the training and testing domains in the projected space. To maintain a diverse and representative feature memory bank, we introduce an adversarial feature generation method that creates features extending beyond the training domain distribution. Experimental results demonstrate that our approach achieves state-of-the-art performance on standard single domain generalization benchmarks.

Position: Untrained Machine Learning for Anomaly Detection

Anomaly detection based on 3D point cloud data is an important research problem and receives more and more attention recently. Untrained anomaly detection based on only one sample is an emerging research problem motivated by real manufacturing industries such as personalized manufacturing that only one sample can be collected without any additional labels. How to accurately identify anomalies based on one 3D point cloud sample is a critical challenge in both industrial applications and the field of machine learning. This paper aims to provide a formal definition of untrained anomaly detection problem based on 3D point cloud data, discuss the differences between untrained anomaly detection and current unsupervised anomaly detection methods. Unlike unsupervised learning, untrained methods do not rely on any data, including unlabeled data. Instead, they leverage prior knowledge about the manufacturing surfaces and anomalies. Examples are used to illustrate these prior knowledge and untrained machine learning model. Afterwards, literature review on untrained anomaly detection based on 3D point cloud data is also provided, and the potential of untrained deep neural networks for anomaly detection is also discussed as outlooks.

Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations

Class imbalance is pervasive in real-world graph datasets, where the majority of annotated nodes belong to a small set of classes (majority classes), leaving many other classes (minority classes) with only a handful of labeled nodes. Graph Neural Networks (GNNs) suffer from significant performance degradation in the presence of class imbalance, exhibiting bias towards majority classes and struggling to generalize effectively on minority classes. This limitation stems, in part, from the message passing process, leading GNNs to overfit to the limited neighborhood of annotated nodes from minority classes and impeding the propagation of discriminative information throughout the entire graph. In this paper, we introduce a novel Unified Graph Neural Network Learning (Uni-GNN) framework to tackle class-imbalanced node classification. The proposed framework seamlessly integrates both structural and semantic connectivity representations through semantic and structural node encoders. By combining these connectivity types, Uni-GNN extends the propagation of node embeddings beyond immediate neighbors, encompassing non-adjacent structural nodes and semantically similar nodes, enabling efficient diffusion of discriminative information throughout the graph. Moreover, to harness the potential of unlabeled nodes within the graph, we employ a balanced pseudo-label generation mechanism that augments the pool of available labeled nodes from minority classes in the training set. Experimental results underscore the superior performance of our proposed Uni-GNN framework compared to state-of-the-art class-imbalanced graph learning baselines across multiple benchmark datasets.

A Low-Cost Monopulse Receiver with Enhanced Estimation Accuracy Via Deep Neural Network

In this paper, a low-cost monopulse receiver with an enhanced direction of arrival (DoA) estimation accuracy via deep neural network (DNN) is proposed. The entire system is composed of a 4-element patch array, a fully planar symmetrical monopulse comparator network, and a down conversion link. Unlike the conventional design topology, the proposed monopulse comparator network is configured by four novel port-transformation rat-race couplers. In specific, the proposed coupler is designed to symmetrically allocate the sum ({\Sigma}) / delta ({\Delta}) ports with input ports, where a 360{\deg} phase delay crossover is designed to transform the unsymmetrical ports in the conventional rat-race coupler. This new rat-race coupler resolves the issues in conventional monopulse receiver comparator network design using multilayer and expensive fabrication technology. To verify the design theory, a prototype of the proposed planar monopulse comparator network operating at 2 GHz is designed, simulated, and measured. In addition, the monopulse radiation patterns and direction of arrival are also decently evaluated. To further boost the accuracy of angular information, a deep neural network is introduced to map the misaligned target angular positions in the measurement to the actual physical location under detection.

When Graph meets Multimodal: Benchmarking on Multimodal Attributed Graphs Learning

Multimodal attributed graphs (MAGs) are prevalent in various real-world scenarios and generally contain two kinds of knowledge: (a) Attribute knowledge is mainly supported by the attributes of different modalities contained in nodes (entities) themselves, such as texts and images. (b) Topology knowledge, on the other hand, is provided by the complex interactions posed between nodes. The cornerstone of MAG representation learning lies in the seamless integration of multimodal attributes and topology. Recent advancements in Pre-trained Language/Vision models (PLMs/PVMs) and Graph neural networks (GNNs) have facilitated effective learning on MAGs, garnering increased research interest. However, the absence of meaningful benchmark datasets and standardized evaluation procedures for MAG representation learning has impeded progress in this field. In this paper, we propose Multimodal Attribute Graph Benchmark (MAGB)}, a comprehensive and diverse collection of challenging benchmark datasets for MAGs. The MAGB datasets are notably large in scale and encompass a wide range of domains, spanning from e-commerce networks to social networks. In addition to the brand-new datasets, we conduct extensive benchmark experiments over MAGB with various learning paradigms, ranging from GNN-based and PLM-based methods, to explore the necessity and feasibility of integrating multimodal attributes and graph topology. In a nutshell, we provide an overview of the MAG datasets, standardized evaluation procedures, and present baseline experiments. The entire MAGB project is publicly accessible at https://github.com/sktsherlock/ATG.

PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling

Document understanding is a challenging task to process and comprehend large amounts of textual and visual information. Recent advances in Large Language Models (LLMs) have significantly improved the performance of this task. However, existing methods typically focus on either plain text or a limited number of document images, struggling to handle long PDF documents with interleaved text and images, especially in academic papers. In this paper, we introduce PDF-WuKong, a multimodal large language model (MLLM) which is designed to enhance multimodal question-answering (QA) for long PDF documents. PDF-WuKong incorporates a sparse sampler that operates on both text and image representations, significantly improving the efficiency and capability of the MLLM. The sparse sampler is integrated with the MLLM's image encoder and selects the paragraphs or diagrams most pertinent to user queries for processing by the language model. To effectively train and evaluate our model, we construct PaperPDF, a dataset consisting of a broad collection of academic papers sourced from arXiv, multiple strategies are proposed to generate automatically 1M QA pairs along with their corresponding evidence sources. Experimental results demonstrate the superiority and high efficiency of our approach over other models on the task of long multimodal PDF understanding, surpassing proprietary products by an average of 8.6% on F1. Our code and dataset will be released at https://github.com/yh-hust/PDF-Wukong.

Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator

Owing to the unprecedented capability in semantic understanding and logical reasoning, the pre-trained large language models (LLMs) have shown fantastic potential in developing the next-generation recommender systems (RSs). However, the static index paradigm adopted by current methods greatly restricts the utilization of LLMs capacity for recommendation, leading to not only the insufficient alignment between semantic and collaborative knowledge, but also the neglect of high-order user-item interaction patterns. In this paper, we propose Twin-Tower Dynamic Semantic Recommender (TTDS), the first generative RS which adopts dynamic semantic index paradigm, targeting at resolving the above problems simultaneously. To be more specific, we for the first time contrive a dynamic knowledge fusion framework which integrates a twin-tower semantic token generator into the LLM-based recommender, hierarchically allocating meaningful semantic index for items and users, and accordingly predicting the semantic index of target item. Furthermore, a dual-modality variational auto-encoder is proposed to facilitate multi-grained alignment between semantic and collaborative knowledge. Eventually, a series of novel tuning tasks specially customized for capturing high-order user-item interaction patterns are proposed to take advantages of user historical behavior. Extensive experiments across three public datasets demonstrate the superiority of the proposed methodology in developing LLM-based generative RSs. The proposed TTDS recommender achieves an average improvement of 19.41% in Hit-Rate and 20.84% in NDCG metric, compared with the leading baseline methods.

Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning

The behavior decision-making subsystem is a key component of the autonomous driving system, which reflects the decision-making ability of the vehicle and the driver, and is an important symbol of the high-level intelligence of the vehicle. However, the existing rule-based decision-making schemes are limited by the prior knowledge of designers, and it is difficult to cope with complex and changeable traffic scenarios. In this work, an advanced deep reinforcement learning model is adopted, which can autonomously learn and optimize driving strategies in a complex and changeable traffic environment by modeling the driving decision-making process as a reinforcement learning problem. Specifically, we used Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) for comparative experiments. DQN guides the agent to choose the best action by approximating the state-action value function, while PPO improves the decision-making quality by optimizing the policy function. We also introduce improvements in the design of the reward function to promote the robustness and adaptability of the model in real-world driving situations. Experimental results show that the decision-making strategy based on deep reinforcement learning has better performance than the traditional rule-based method in a variety of driving tasks.