GenAI4ELab/papercli-papers huggingface dataset:
Network Inversion of Convolutional Neural Nets (Student Abstract)
Authors: Pirzada Suhail; Amit Sethi
Venue: AAAI 2025 (main)

Abstract: Neural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes."  This opacity raises concerns about their interpretability and reliability, especially in safety-critical scenarios. Network inversion techniques offer a solution by allowing us to peek inside these black boxes, revealing the features and patterns learned by the networks behind their decision-making processes and thereby provide valuable insights into how neural networks arrive at their conclusions, making them more interpretable and trustworthy. This paper presents a simple yet effective approach to network inversion using a meticulously conditioned generator that learns the data distribution in the input space of the trained neural network, enabling the reconstruction of inputs that would most likely lead to the desired outputs. To capture the diversity in the input space for a given output, instead of simply revealing the conditioning labels to the generator, we encode the conditioning label information into vectors and intermediate matrices and further minimize the cosine similarity between features of the generated images.
Efficient Federated Learning via Clients-to-Server Knowledge Distillation (Student Abstract)
Authors: Huifang Sun; Jiaming Pei; Lukun Wang
Venue: AAAI 2025 (main)

Abstract: To diminish the substantial communication costs incurred by federated learning during the training of the global model and enhance the model update efficiency across both clients and server domains, we have integrated knowledge distillation into the federated learning framework. This integration has led to the development of a novel approach termed ClientsToServerKDFL, which streamlines the distillation process by directly transferring model insights from clients to the server for computational learning without the need for extensive computations across numerous clients. This iterative process ensures model accuracy and curtails communication expenses. Experimental data analysis has validated the efficacy of this algorithm.
Towards Building Human-like Smart Agents in Modern 3D Video Games (Student Abstract)
Authors: Zhihang Sun; Shuhan Qi; Xinhao Huang; Xinyu Xiao; Jiajia Zhang; Xuan Wang; Peixi Peng
Venue: AAAI 2025 (main)

Abstract: In recent years, reinforcement learning has been widely applied in the field of games. However, most studies focus on assisting agents to achieve victory, with less attention paid to whether the agents exhibit human-like characteristics. In order to build human-like agents with high performance, we propose a method for learning the strategies of human players in modern three-dimensional video games. Our method utilizes a hierarchical framework, learning basic behaviors and intentions of human players at the lower level through imitation learning, and generalized policies at the high level through reinforcement learning. Compared with other existing methods, our method demonstrates significant advantages in learning human-like strategies in complex environments.
Sequential Order Adjustment of Action Decisions for Multi-Agent Transformer (Student Abstract)
Authors: Shota Takayama; Katsuhide Fujita
Venue: AAAI 2025 (main)

Abstract: Multi-agent reinforcement learning (MARL) trains multiple agents in shared environments. Recently, MARL models have significantly improved performance by leveraging sequential decision-making processes. Although these models can enhance performance, they do not explicitly con-sider the importance of the order in which agents make decisions. We propose AOAD-MAT, a novel model incorporating action decision sequence into learning. AOAD-MAT uses a Transformer-based actor-critic architecture to dynamically adjust agent action order. It introduces a subtask predicting the next agent to act, integrated into a PPO-based loss function. Experiments on StarCraft Multi-Agent Challenge and Multi-Agent MuJoCo benchmarks show AOAD-MAT out-performs existing models, demonstrating the effectiveness of adjusting agent order in MARL.
Training Recurrent Neural Networks with Inherent Missing Data for Wearable Device Applications (Student Abstract)
Authors: Sutashu Tomonaga; Haruo Mizutani; Kenji Doya
Venue: AAAI 2025 (main)

Abstract: Wearable devices are transforming healthcare by providing continuous, real-time physiological data for monitoring and analysis. However, data often suffer from noise and significant missing values due to operational constraints and user compliance. Traditional approaches address these issues through data imputation during pre-processing, introducing biases and inaccuracies. We propose a novel method enabling Recurrent Neural Networks (RNNs) to inherently handle missing data without imputation. By implementing teacher-forcing during Backpropagation Through Time (BPTT) when data are available and switching to autonomous mode otherwise, our approach leverages RNNs' dynamics to model physiological signals accurately. We demonstrate our method's effectiveness using the Lorenz 63 system as a surrogate dataset, achieving robust reconstructions with 80% missing data.
When Neutral Summaries Are Not That Neutral: Quantifying Political Neutrality in LLM-Generated News Summaries (Student Abstract)
Authors: Supriti Vijay; Aman Priyanshu; Ashiqur R. KhudaBukhsh
Venue: AAAI 2025 (main)

Abstract: In an era where societal narratives are increasingly shaped by algorithmic curation, investigating the political neutrality of LLMs is an important research question. This study presents a fresh perspective on quantifying the political neutrality of LLMs through the lens of abstractive text summarization of polarizing news articles. We consider five pressing issues in current US politics: abortion, gun control/rights, healthcare, immigration, and LGBTQ+ rights. Via a substantial corpus of 20,344 news articles, our study reveals a consistent trend towards pro-Democratic biases in several well-known LLMs, with gun control and healthcare exhibiting the most pronounced biases (max polarization differences of -9.49% and -6.14%, respectively). Further analysis uncovers a strong convergence in the vocabulary of the LLM outputs for these divisive topics (55% overlap for Democrat-leaning representations, 52% for Republican). Being months away from a US election of consequence, we consider our findings important.
When to Learn and When to Stop: Quitting at the Optimal Time (Student Abstract)
Authors: Diana Vins; Jean Erik Delanois; Maxim Bazhenov
Venue: AAAI 2025 (main)

Abstract: Artificial neural networks (ANNs) struggle with continual learning, sacrificing performance on previously learned tasks to acquire new task knowledge. Here we propose a new approach allowing to mitigate catastrophic forgetting during continuous task learning. Typically a new task is trained until it reaches maximal performance, causing complete catastrophic forgetting of the previous tasks. In our new approach, termed Optimal Stopping (OS), network training on each new task continues only while the mean validation accuracy across all the tasks (current and previous) increases. The stopping criterion creates an explicit balance: lower performance on new tasks is accepted in exchange for preserving knowledge of previous tasks, resulting in higher overall network performance. The overall performance is further improved when OS is combined with Sleep Replay Consolidation (SRC), wherein the network converts to a Spiking Neural Network (SNN) and undergoes unsupervised learning modulated by Hebbian plasticity. During the SRC, the network spontaneously replays activation patterns from previous tasks, helping to maintain and restore prior task performance. This combined approach offers a promising avenue for enhancing the robustness and longevity of learned representations in continual learning models, achieving over twice the mean accuracy of baseline continuous learning while maintaining stable performance across tasks.
An Expert System for Danish Traffic Law Cases (Student Abstract)
Authors: Jonas Vistrup
Venue: AAAI 2025 (main)

Abstract: We present FÆRDXEL, an expert system for providing answers and explanations to legal questions (queries) regarding Danish traffic law cases. 
It utilizes a Datalog encoding of Danish traffic laws and uses SLD-resolution to answer queries. 
The SLD-resolution’s trace of operations can be converted into a legal explanation for the query. 
A user interface allows legal professionals to input case facts, ask questions, and explore explanations.
Feedback from legal experts identified usability challenges and potential end-users, including traffic police and judges. 
Future steps include empirical evaluation of soundness, integrating punishment reasoning, and enhancing usability through natural language processing.
Multi-View Unsupervised Column Subset Selection via Combinatorial Search (Student Abstract)
Authors: Guihong Wan; Ninghui Hao; Crystal Maung; Haim Schweitzer; Chen Zhao; Kun-Hsing Yu; Yevgeniy R. Semenov
Venue: AAAI 2025 (main)

Abstract: Given a data matrix, unsupervised column subset selection refers to the problem of identifying a subset of columns that can be used to linearly approximate the original data matrix. This problem has many applications, such as feature selection and representative selection, but solving it optimally is known to be NP-hard. We consider multi-view unsupervised column subset selection, which extends the concept of (single-view) column subset selection to data represented in multiple views or modalities. We introduce a combinatorial search algorithm for this generalized problem. One variant of the algorithm is guaranteed to compute an optimal solution in a setting similar to the classical A* algorithm. Other suboptimal variants, in a setting similar to the weighted A* algorithm, are much faster and provide a solution along with a bound on its quality.
Extended LSTMs for Knowledge Tracing: Peeking Inside the Black Box (Student Abstract)
Authors: Deliang Wang; Yu Lu; Gaowei Chen
Venue: AAAI 2025 (main)

Abstract: This paper proposes extended Long Short-Term Memory (LSTM) networks for the knowledge tracing task and employs explainable AI methods to address interpretability issues. Specifically, we developed an extended LSTM-based model to automatically diagnose students' knowledge states. We then leveraged three interpreting methods—gradient sensitivity, gradient*input, and Deep SHAP—to explain the model's predictions by computing input contributions. The results demonstrate that the proposed model outperforms DKT, and the three methods effectively explain its predictions. Additionally, we identified three key insights into the model's working mechanisms.
An Evaluation of Approaches to Train Embeddings for Logical Inference (Student Abstract)
Authors: Yasir White; Jevon Lipsey; Jeff Heflin
Venue: AAAI 2025 (main)

Abstract: Knowledge bases traditionally require manual optimization to ensure reasonable performance when answering queries. We build on previous neurosymbolic approaches by improving the training of an embedding model for logical statements that maximizes similarity between unifying atoms and minimizes similarity of non-unifying atoms. In particular, we evaluate different approaches to training this model.
Neuro-Guided Graph Search for Symbolic Regression (Student Abstract)
Authors: Piotr Wyrwiński; Krzysztof Krawiec
Venue: AAAI 2025 (main)

Abstract: This study introduces a neurosymbolic approach that performs iterative graph expansion guided by a graph neural network to solve symbolic regression problems. Empirical evaluation demonstrates superior performance of the method compared to baseline algorithms. We also integrate the method with an evolutionary algorithm, which results in further performance improvements.
Linear Decoding of Morphology Relations in Language Models (Student Abstract)
Authors: Eric Xia; Jugal Kalita
Venue: AAAI 2025 (main)

Abstract: The recent success of transformer language models owes much to their conversational fluency, which includes linguistic and morphological proficiency. An affine Taylor approximation has been found to be a good approximation for transformer computations over certain factual and encyclopedic relations. We show that the truly linear approximation W s, where s is a early layer representation of the base form and W is a local model derivative, is necessary and sufficient
to approximate morphological derivation, achieving above 80% top-1 accuracy across most morphological tasks in the Bigger Analogy Test Set. We argue that many morphological forms in transformer models are likely linearly encoded.
ERFSL: An Efficient Reward Function Searcher via Large Language Models for Custom-Environment Multi-Objective Reinforcement Learning (Student Abstract)
Authors: Guanwen Xie; Jingzehua Xu; Yiyuan Yang; Yimian Ding; Shuai Zhang
Venue: AAAI 2025 (main)

Abstract: We propose ERFSL, an efficient reward function searcher using large language models (LLMs) for custom-environment, multi-objective reinforcement learning (RL). ERFSL generates reward components based on explicit user requirements and rectifies them, and iteratively optimizes the weights of these components based on textual context. Applied to an underwater data collection RL task, ERFSL corrects reward codes with only one feedback iteration per requirement, and acquires diverse reward functions within the Pareto set. ERFSL also presents robust capability for deviated weights and small-size LLMs such as GPT-4o mini. The full-text prompts, examples of LLM-generated answers, and source code are available at https://360zmem.github.io/LLMRsearcher/ .
UACOF: A USV-AUV Collaboration Framework for Underwater Tasks Under Extreme Sea Conditions (Student Abstract)
Authors: Jingzehua Xu; Guanwen Xie; Yimian Ding; Yongming Zeng; Haoyu Wang; Shuai Zhang
Venue: AAAI 2025 (main)

Abstract: Ocean exploration requires effective collaboration between the unmanned surface vehicle (USV) and autonomous underwater vehicles (AUVs). We propose UACOF, a USV-AUV collaboration framework that enhances multi-AUV performance under extreme sea conditions. The framework includes high-precision multi-AUV location via USV path planning with Fisher information matrix optimization and reinforcement learning training for cooperative tasks. Experimental results show UACOF's superior feasibility, performance, coordination and robustness in extreme conditions.
A High-Efficiency Federated Learning Method Using Complementary Pruning for D2D Communication (Student Abstract)
Authors: Xiaoqing Xu; Jiaming Pei; Lukun Wang
Venue: AAAI 2025 (main)

Abstract: In federated learning, frequent parameter transmission between clients and the server results in significant communication overhead, particularly due to redundancy within the parameters. To address this issue, we propose a Complementary Pruning for Device-to-Device Communication (FedCPD) method. This approach effectively reduces the amount of transmitted parameters by applying complementary pruning techniques on both the server and clients. Additionally, we decrease the communication frequency between clients and the server by employing chain updates among clients (i.e., device-to-device communication). We conducted experiments on the MNIST, FMNIST, CIFAR-10, and CIFAR-100 datasets, and the results demonstrate that our method significantly reduces communication costs while improving model accuracy.
Temporal Streaming Batch Principal Component Analysis for Time Series Classification (Student Abstract)
Authors: Enshuo Yan; Huachuan Wang; Weihao Xia
Venue: AAAI 2025 (main)

Abstract: In multivariate time series classification, although current sequence analysis models have excellent classification capabilities, they show significant shortcomings when dealing with long sequence multivariate data. This paper focuses on optimizing model performance for long-sequence multivariate data by mitigating the impact of extended time series and multiple variables on the model. We propose a principal component analysis (PCA)-based temporal streaming compression and dimensionality reduction algorithm for time series data (temporal streaming batch PCA, TSBPCA), which continuously updates the compact representation of the entire sequence through streaming PCA time estimation with time block updates, enhancing the data representation capability of a range of sequence analysis models.We evaluated this method using various models on five datasets, and the experimental results show that our method demonstrates outstanding performance in both classification accuracy and time efficiency.
Multimodal Commonsense Knowledge Distillation for Visual Question Answering (Student Abstract)
Authors: Shuo Yang; Siwen Luo; Soyeon Caren Han
Venue: AAAI 2025 (main)

Abstract: Existing Multimodal Large Language Models (MLLMs) and Visual Language Pretrained Models (VLPMs) have shown remarkable performances in general Visual Question Answering (VQA). However, these models struggle with VQA questions that require external commonsense knowledge due to the challenges in generating high-quality prompts and the high computational costs of fine-tuning. In this work, we propose a novel graph-based multimodal commonsense knowledge distillation framework that constructs a unified relational graph over commonsense knowledge, visual objects and questions through a Graph Convolutional Network (GCN) following a teacher-student environment. This proposed framework is flexible with any type of teacher and student models without further fine-tuning, and has achieved competitive performances on the ScienceQA dataset. The code is in https://github.com/adlnlp/MCKDVQA.
Understanding Annotator Perception: Modeling Psychological Inference from First- and Third-Person Annotations (Student Abstract)
Authors: Gerard Christopher Yeo; Kokil Jaidka
Venue: AAAI 2025 (main)

Abstract: Large language models (LLMs) are trained on vast amounts of publicly available text. However, the current training frameworks take for granted that these annotations are accurate reflections of the authors’ true intents. This study questions that assumption by examining the gaps between writers’ actual psychological states and the inferences made by third-party annotators. We explore how readers interpret psychological cues in text and demonstrate that third-person annotations often fail to align with first-person realities. By integrating both first- and third-person annotations, we develop computational models that reveal significant biases in how psychological states are perceived and the downstream effects these perceptions have on reader behavior. Our findings challenge the foundational assumptions of LLM training, suggesting that the reliance on potentially flawed third-person annotations could impact model accuracy and real-world applications.
Understanding Unique Behavioral Patterns through Multimodal Analysis of Eye-Hand Coordination in Autistic Children (Student Abstract)
Authors: Emily Yu
Venue: AAAI 2025 (main)

Abstract: Data-driven analysis has shown promising results in identifying subtle patterns in the behavior of individuals with Autism Spectrum Disorder (ASD) for diagnosis and intervention. However, most existing methods primarily focus on a single behavioral modality (e.g., eye movements) instead of capturing the intricate multimodal behavior of humans. We propose a multimodal approach that investigates the underlying connections between eye movements and hand motions through eye-to-hand prediction. To tackle the highly noisy and irregular behavioral data, we propose a novel approach that defines the prediction as a machine translation problem and leverages a sequence-to-sequence machine learning model for the prediction. An experimental study on a dataset collected from a VR system has demonstrated high prediction accuracy. The significant difference in the prediction accuracy between the autistic group and their typically developing (TD) peers serves as quantitative evidence to objectively understand the restricted and repetitive behaviors (RRBs) in autistic children. The source code can be accessed here: https://github.com/mathjams/AAAI_2024.
Preliminary Evaluation of the Test-Time Training Layers in Recommendation System (Student Abstract)
Authors: Tianyu Zhan; Zheqi Lv; Shengyu Zhang; Jiwei Li
Venue: AAAI 2025 (main)

Abstract: This paper explores the application and effectiveness of TestTime Training (TTT) layers in improving the performance of recommendation systems. We developed a model, TTT4Rec, utilizing TTT-Linear as the feature extraction layer. Our tests across multiple datasets indicate that TTT4Rec, as a base model, performs comparably or even surpasses other baseline models in similar environments.
Efficient Image Similarity Search with Quadtrees (Student Abstract)
Authors: Yifan Zhang; Jeff Heflin
Venue: AAAI 2025 (main)

Abstract: In this paper, we present a new image similarity search algorithm designed to enhance traditional information retrieval(IR) by adding an image search capability. Our approach uses a quadtree data structure to organize image data, significantly reducing search space and improving retrieval efficiency. We describe an indexing strategy and two query algorithms that can be implemented in any IR system. We tested our method on a 70K material microscopy image dataset, achieving a 25 times improvement in retrieval speed with only a 20% reduction in ranking accuracy.
Personalized Learning in Action: Exploring AI and Robotics for Early Childhood Education
Authors: Nicholas Abram
Venue: AAAI 2025 (main)

Abstract: The lack of personalization in early education can often leave students with weak foundational skills, causing said students to be behind in their studies. Personalized learning, the idea of tailoring a unique lesson plan to a student, has been shown to improve the understanding of content learned. Robots utilizing personalization techniques in educational settings, coined social robots, have been able to form a connection with students, thereby keeping them engaged while learning. This proposal seeks to study the effects of AI-driven social robotic tutors coupled with personalized learning on early childhood education. The study will consist of five groups of K-4 students: two groups learning while utilizing both a social robot and a tablet (one group with personalized learning and the other without), the two groups interacting with only the tablet (with and without personalization), and the last group learning utilizing both a non-personalized learning tablet and a non-social robot. This study aims to determine whether the combination of robotic interaction and personalized learning leads to better outcomes than solely tablet-based or non-personalized methods. This study will focus on teaching mathematics to the participants. Pre and post-tests will measure learning progress, and the influence of robot interaction on student engagement will also be evaluated. It is expected that the students with social robotic tutors and personalized learning tablets will show the greatest knowledge retention, outperforming all other categories. These findings could have significant implications for the integration of AI and robotics in early education, potentially revolutionizing how personalized learning is implemented therefore improving educational outcomes for young learners.