GenAI4ELab/papercli-papers huggingface dataset:
StressPrompt: Does Stress Impact Large Language Models and Human Performance Similarly?
Authors: Guobin Shen; Dongcheng Zhao; Aorigele Bao; Xiang He; Yiting Dong; Yi Zeng
Venue: AAAI 2025 (main)

Abstract: Human beings often experience stress, which can significantly influence their performance. This study explores whether Large Language Models (LLMs) exhibit stress responses similar to those of humans and whether their performance fluctuates under different stress-inducing prompts. To investigate this, we developed a novel set of prompts, termed StressPrompt, designed to induce varying levels of stress. These prompts were derived from established psychological frameworks and carefully calibrated based on ratings from human participants. We then applied these prompts to several LLMs to assess their responses across a range of tasks, including instruction-following, complex reasoning, and emotional intelligence. The findings suggest that LLMs, like humans, perform optimally under moderate stress, consistent with the Yerkes-Dodson law. Notably, their performance declines under both low and high-stress conditions. Our analysis further revealed that these StressPrompts significantly alter the internal states of LLMs, leading to changes in their neural representations that mirror human responses to stress. This research provides critical insights into the operational robustness and flexibility of LLMs, demonstrating the importance of designing AI systems capable of maintaining high performance in real-world scenarios where stress is prevalent, such as in customer service, healthcare, and emergency response contexts. Moreover, this study contributes to the broader AI research community by offering a new perspective on how LLMs handle different scenarios and their similarities to human cognition.
SUMI-IFL: An Information-Theoretic Framework for Image Forgery Localization with Sufficiency and Minimality Constraints
Authors: Ziqi Sheng; Wei Lu; Xiangyang Luo; Jiantao Zhou; Xiaochun Cao
Venue: AAAI 2025 (main)

Abstract: Image forgery localization (IFL) is a crucial technique for preventing tampered image misuse and protecting social safety. However, due to the rapid development of image tampering technologies, extracting more comprehensive and accurate forgery clues remains an urgent challenge. To address these challenges, we introduce a novel information-theoretic IFL framework named SUMI-IFL that imposes sufficiency-view and minimality-view constraints on forgery feature representation. First, grounded in the theoretical analysis of mutual information, the sufficiency-view constraint is enforced on the feature extraction network to ensure that the latent forgery feature contains comprehensive forgery clues. Considering that forgery clues obtained from a single aspect alone may be incomplete, we construct the latent forgery feature by integrating several orthogonal individual image features. Second, based on the information bottleneck, the minimality-view constraint is imposed on the feature reasoning network to achieve an accurate and concise forgery feature representation that counters the interference of task-unrelated features. Extensive experiments show the superior performance of SUMI-IFL to existing state-of-the-art methods, not only on in-dataset comparisons but also on cross-dataset comparisons.
SpeHeaTal: A Cluster-Enhanced Segmentation Method for Sperm Morphology Analysis
Authors: Yi Shi; Yun-Kai Wang; Xu-Peng Tian; Tie-Yi Zhang; Bing Yao; Hui Wang; Yong Shao; Cen-Cen Wang; Rong Zeng
Venue: AAAI 2025 (main)

Abstract: The accurate assessment of sperm morphology is crucial in andrological diagnostics, where the segmentation of sperm images presents significant challenges. Existing approaches frequently rely on large annotated datasets and often struggle with the segmentation of overlapping sperm and the presence of dye impurities. To address these challenges, this paper first analyzes the issue of overlapping sperm tails from a geometric perspective and introduces a novel clustering algorithm, Con2Dis, which effectively segments overlapping tails by considering three essential factors: CONnectivity, CONformity, and DIStance. Building on this foundation, we propose an unsupervised method, SpeHeaTal, designed for the comprehensive segmentation of the SPErm HEAd and TAiL. SpeHeaTal employs the Segment Anything Model (SAM) to generate masks for sperm heads while filtering out dye impurities, utilizes Con2Dis to segment tails, and then applies a tailored mask splicing technique to produce complete sperm masks. Experimental results underscore the superior performance of SpeHeaTal, particularly in handling images with overlapping sperm.
Autonomous Goal Detection and Cessation in Reinforcement Learning: A Case Study on Source Term Estimation
Authors: Yiwei Shi; Muning Wen; Qi Zhang; Weinan Zhang; Cunjia Liu; Weiru Liu
Venue: AAAI 2025 (main)

Abstract: Reinforcement Learning has revolutionized decision-making processes in dynamic environments, yet it often struggles with autonomously detecting and achieving goals without clear feedback signals. For example, in a Source Term Estimation problem, the lack of precise environmental information makes it challenging to provide clear feedback signals and to define and evaluate how the source's location is determined. To address this challenge, the Autonomous Goal Detection and Cessation (AGDC) module was developed, enhancing various RL algorithms by incorporating a self-feedback mechanism for autonomous goal detection and cessation upon task completion. Our method effectively identifies and ceases undefined goals by approximating the agent's belief, significantly enhancing the capabilities of RL algorithms in environments with limited feedback. To validate  effectiveness of our approach, we integrated AGDC with deep Q-Network, proximal policy optimization, and deep deterministic policy gradient algorithms, and evaluated its performance on the Source Term Estimation problem. The experimental results showed that AGDC-enhanced RL algorithms significantly outperformed traditional statistical methods such as infotaxis, entrotaxis, and dual control for exploitation and exploration, as well as a non-statistical random action selection method. These improvements were evident in terms of success rate, mean traveled distance, and search time, highlighting AGDC's effectiveness and efficiency in complex, real-world scenarios.
HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting
Authors: Feng Shibo; Peilin Zhao; Liu Liu; Pengcheng Wu; Zhiqi Shen
Venue: AAAI 2025 (main)

Abstract: Generative models have gained significant attention in multivariate time series forecasting (MTS), particularly due to their ability to generate high-fidelity samples. Forecasting the probability distribution of multivariate time series is a challenging yet practical task. Although some recent attempts have been made to handle this task, two major challenges persist: 1) some existing generative methods underperform in high-dimensional multivariate time series forecasting, which is hard to scale to higher dimensions; 2) The inherent high-dimensional multivariate attributes constrain the forecasting lengths of existing generative models. In this paper, we point out that discrete token representations can model high-dimensional MTS with faster inference time, and forecast the target with the long-term trends of itself can extend the forecasting length with high accuracy. Motivated by this, we propose a vector quantized framework called Hierarchical Discrete Transformer (HDT) that models time series into discrete token representations with l2 normalization enhanced vector quantized strategy, in which we transform the MTS forecasting into discrete tokens generation. To address the limitations of generative models in long-term forecasting, we propose a hierarchical discrete Transformer. This model captures the discrete long-term trend of the target at the low level and leverages this trend as a condition to generate the discrete representation of the target at the high level that introduces the features of target itself for extending the forecasting length in high-dimensional MTS. Extensive experiments on five popular MTS datasets verify the effectiveness of our proposed method. The source code will be released.
Enhancing Generalizability in Molecular Conformation Generation with METRIZATION-Informed Geometric Diffusion Pretraining
Authors: Xiaozhuang Song; Yuzhao Tu; Hangting Ye; Wei Fan; Qingquan Zhang; Xiaoxue Wang; Tianshu Yu
Venue: AAAI 2025 (main)

Abstract: Diffusion-based generative models have recently excelled in generating molecular conformations but struggled with the generalization issue -- models trained on one dataset may produce meaningless conformations on out-of-distribution molecules. 
On the other hand, distance geometry serves as a generalizable tool for the traditional computational chemistry methods of molecular conformation, which is predicated on the assumption that it is possible to adequately define the set of all potential conformations of any non-rigid molecular system using purely geometric constraints.
In this work, we for the first time explicitly incorporate distance geometry constraints into pretraining phase of diffusion-based molecular generation models to improve the generalizability.
Inspired by the classical distance geometry solution designed for solving the molecular distance geometry problem, we propose MiGDiff, a Metrization-Informed Geometric Diffusion framework. MiGDiff injects distance geometry constraints by pretraining the deep geometric diffusion backbone within the Metrization sampling approach, yielding a "Metrization-driven pretraining + Data-driven finetuning" paradigm. Experimental results demonstrate that MiGDiff outperforms state-of-the-art methods and possesses strong generalization capabilities, particularly on generating previously unseen molecules, revealing the vast untapped potential of combining traditional computational methods with deep generative models for 3D molecular generation.
Predicting User Behavior in Smart Spaces with LLM-Enhanced Logs and Personalized Prompts
Authors: Yunpeng Song; Jiawei Li; Yiheng Bian; Zhongmin Cai
Venue: AAAI 2025 (main)

Abstract: Enhancing the intelligence of smart systems, such as smart homes, smart vehicles, and smart grids, critically depends on developing sophisticated planning capabilities that can anticipate the next desired function based on historical interactions. While existing methods view user behaviors as sequential data and apply models like RNNs and Transformers to predict future actions, they often fail to incorporate domain knowledge and capture personalized user preferences. In this paper, we propose a novel approach that incorporates LLM-enhanced logs and personalized prompts. Our approach first constructs a graph that captures individual behavior preferences derived from their interaction histories. This graph effectively transforms into a soft continuous prompt that precedes the sequence of user behaviors. Then our approach leverages the vast general knowledge and robust reasoning capabilities of a pretrained LLM to enrich the oversimplified and incomplete log records. By enhancing these logs semantically, our approach better understands the user's actions and intentions, especially for those rare events in the dataset. We evaluate the method across four real-world datasets from both smart vehicle and smart home settings. The findings validate the effectiveness of our LLM-enhanced description and personalized prompt, shedding light on potential ways to advance the intelligence of smart space.
END^2: Robust Dual-Decoder Watermarking Framework Against Non-Differentiable Distortions
Authors: Nan Sun; Han Fang; Yuxing Lu; Chengxin Zhao; Hefei Ling
Venue: AAAI 2025 (main)

Abstract: DNN-based watermarking methods have rapidly advanced, with the ``Encoder-Noise Layer-Decoder'' (END) framework being the most widely used. To ensure end-to-end training, the noise layer in the framework must be differentiable. However, real-world distortions are often non-differentiable, leading to challenges in end-to-end training. 
Existing solutions only treat the distortion perturbation as additive noise, which does not fully integrate the effect of distortion in training.
To better incorporate non-differentiable distortions into training, we propose a novel dual-decoder architecture (END^2). Unlike conventional END architecture, our method employs two structurally identical decoders: the Teacher Decoder, processing pure watermarked images, and the Student Decoder, handling distortion-perturbed images. 
The gradient is backpropagated only through the Teacher Decoder branch to optimize the encoder thus bypassing the problem of non-differentiability. To ensure resistance to arbitrary distortions, we enforce alignment of the two decoders' feature representations by maximizing the cosine similarity between their intermediate vectors on a hypersphere. 
Extensive experiments demonstrate that our scheme outperforms state-of-the-art algorithms under various non-differentiable distortions. Moreover, even without the differentiability constraint, our method surpasses baselines with a differentiable noise layer. Our approach is effective and easily implementable across all END architectures, enhancing practicality and generalizability.
dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen
Authors: Cheng Tan; Yijie Zhang; Zhangyang Gao; Yufei Huang; Haitao Lin; Lirong Wu; Fandi Wu; Mathieu Blanchette; Stan Z. Li
Venue: AAAI 2025 (main)

Abstract: The development of therapeutic antibodies heavily relies on accurate predictions of how antigens will interact with antibodies. Existing computational methods in antibody design often overlook crucial conformational changes that antigens undergo during the binding process, significantly impacting the reliability of the resulting antibodies. To bridge this gap, we introduce dyAb, a flexible framework that incorporates AlphaFold2-driven predictions to model pre-binding antigen structures and specifically addresses the dynamic nature of antigen conformation changes. Our dyAb model leverages a unique combination of coarse-grained interface alignment and fine-grained flow matching techniques to simulate the interaction dynamics and structural evolution of the antigen-antibody complex, providing a realistic representation of the binding process. Extensive experiments show that dyAb significantly outperforms existing models in antibody design involving changing antigen conformations. These results highlight dyAb's potential to streamline the design process for therapeutic antibodies, promising more efficient development cycles and improved outcomes in clinical applications.
Embedding Robust Watermarking into Pattern to Protect the Copyright of Ceramic Artifacts
Authors: Lei Tan; Yuliang Xue; Guobiao Li; Zhenxing Qian; Sheng Li; Chunlei Bao
Venue: AAAI 2025 (main)

Abstract: Ceramic artworks with elegant patterns present enormous collectible value and profits. To claim the copyright, the builder usually pastes their conspicuous stamp on the bottom or side of the ceramic artworks, which inevitably affects the external image of the artwork. In addition, the stamp is weak in resisting forgery attacks due to its visible nature.
To address the above issues, we propose in this paper a novel framework for embedding invisible watermarking into patterns of the ceramic artworks.
In the framework, a template-based watermarking embedding scheme is designed to map the watermark to an invisible template, which is added to the ceramic pattern to create its watermarked version. 
A distortion layer is further proposed to model the distortion of ceramic patterns in the ceramic manufacturing process, where a color-halftoning and an adaptive brightness adjustment strategy are developed to counter the print and firing operations that introduce the most significant distortions. 
Finally, a deep decoder is learned to extract the watermarking from the distorted pattern.
Various experiments have been conducted to demonstrate the advantage of our proposed method for protecting the copyright of the ceramic artworks, which provides reliable watermark extraction accuracy without the need for a conspicuous stamp.
ODDN: Addressing Unpaired Data Challenges in Open-World Deepfake Detection on Online Social Networks
Authors: Renshuai Tao; Manyi Le; Chuangchuang Tan; Huan Liu; Haotong Qin; Yao Zhao
Venue: AAAI 2025 (main)

Abstract: Despite significant advances in deepfake detection, handling varying image quality, especially due to different compressions on online social networks (OSNs), remains challenging. Current methods succeed by leveraging correlations between paired images, whether raw or compressed. However, in open-world scenarios, paired data is scarce, with compressed images readily available but corresponding raw versions difficult to obtain. This imbalance, where unpaired data vastly outnumbers paired data, often leads to reduced detection performance, as existing methods struggle without corresponding raw images. To overcome this issue, we propose a novel approach named the open-world deepfake detection network (ODDN), which comprises two core modules: open-world data aggregation (ODA) and compression-discard gradient correction (CGC). ODA effectively aggregates correlations between compressed and raw samples through both fine-grained and coarse-grained analyses for paired and unpaired data, respectively. CGC incorporates a compression-discard gradient correction to further enhance performance across diverse compression methods in OSN. This technique optimizes the training gradient to ensure the model remains insensitive to compression variations. Extensive experiments conducted on 17 popular deepfake datasets demonstrate the superiority of the ODDN over SOTA baselines.
NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving
Authors: Chengyue Wang; Haicheng Liao; Bonan Wang; Yanchen Guan; Bin Rao; Ziyuan Pu; Zhiyong Cui; Cheng-Zhong Xu; Zhenning Li
Venue: AAAI 2025 (main)

Abstract: Accurate trajectory prediction is essential for the safety and efficiency of autonomous driving. Traditional models often struggle with real-time processing, capturing non-linearity and uncertainty in traffic environments, efficiency in dense traffic, and modeling temporal dynamics of interactions. We introduce NEST (Neuromodulated Small-world Hypergraph Trajectory Prediction), a novel framework that integrates Small-world Networks and hypergraphs for superior interaction modeling and prediction accuracy. This integration enables the capture of both local and extended vehicle interactions, while the Neuromodulator component adapts dynamically to changing traffic conditions. We validate the NEST model on several real-world datasets, including nuScenes, MoCAD, and HighD. The results consistently demonstrate that NEST outperforms existing methods in various traffic scenarios, showcasing its exceptional generalization capability, efficiency, and temporal foresight. Our comprehensive evaluation illustrates that NEST significantly improves the reliability and operational efficiency of autonomous driving systems, making it a robust solution for trajectory prediction in complex traffic environments.
PScalpel: A Machine Learning-based Guider for Protein Phase-Separating Behaviour Alteration
Authors: Jia Wang; Liyan Zhu; Zhe Wang; Chenqiu Zhang; Yaoxing Wu; Jun Cui; Jianqiang Li
Venue: AAAI 2025 (main)

Abstract: Missense mutations could affect the Liquid-Liquid Phase Separation (LLPS) propensity of proteins and lead to aberrant phase-separating behaviours, which are recently found to be associated with many diseases including Alzheimer's and cancer. However, the regulatory role of mutations in LLPS remains unclear due to challenges in accurately characterizing the LLPS ability of mutants, including the high similarity in features, lack of labeled data, and vast amounts of data involved. To bridge this gap and facilitate the discovery of therapeutic strategies, we propose the first machine learning-based guider for protein phase-separating behaviour alteration, PScalpel. PScalpel leverages both structural information and an auxiliary tasks-based graph contrastive learning framework to distinguish the mutants’ LLPS ability, and incorporates a genetic algorithms-based recommendation method to identify mutants with desired LLPS properties. Comprehensive computational and biological experiments validate the effectiveness of PScalpel as a versatile tool for guiding alterations in protein phase separation behavior.
Hybrid-Driving: An Autonomous Driving Decision Framework Integrating Large Language Models, Knowledge Graphs and Driving Rules
Authors: Jiabao Wang; Zepeng Wu; Qian Dong; Lingzhong Meng; Yunzhi Xue; Yukuan Yang
Venue: AAAI 2025 (main)

Abstract: Recent advancements have underscored the exceptional analytical and situational understanding capabilities of Large Language Models (LLMs) in autonomous driving decisions. However, the inherent hallucination issues of LLMs pose significant safety concerns when utilized as standalone decision-making systems. To address these challenges, we propose the Hybrid-Driving framework, which leverages LLMs' situational comprehension and reasoning abilities alongside the specialized driving expertise embedded in knowledge graphs and driving rules, thereby enhancing the safety, robustness, and reliability of autonomous driving decisions. To articulate driving experiences clearly, we introduce the Scenario Evolution Knowledge Graph (SEKG), which integrates scenario prediction and action risk analysis in autonomous driving. By delineating observation areas and defining Time-to-Collision (TTC) levels, we effectively control the number of driving scenario nodes and ensure scenario diversity. Based on the scenario evolution relationships within the SEKG, we predict scenarios and assess associated action risks. Additionally, we implement a rule-filtering mechanism to eliminate unreasonable actions and employ prompt engineering to integrate scenario information, optional actions, and SEKG-based action risk analysis into the LLMs for decision-making. Extensive experiments demonstrate that our approach substantially improves decision success rates compared to using LLMs alone (≥37.5%), as well as surpasses the DiLu framework with LLMs and few-shot driving memory (≥7.5%), and other reinforcement learning methods (≥11%). These results validate the effectiveness of the Hybrid-Driving framework in enhancing LLM reliability for autonomous driving and advocate for its broader application of domain-specific knowledge across other fields.
GTG: Generalizable Trajectory Generation Model for Urban Mobility
Authors: Jingyuan Wang; Yujing Lin; Yudong Li
Venue: AAAI 2025 (main)

Abstract: Trajectory data mining is crucial for smart city management. However, collecting large-scale trajectory datasets is challenging due to factors such as commercial conflicts and privacy regulations. Therefore, we urgently need trajectory generation techniques to address this issue. Existing trajectory generation methods rely on the global road network structure of cities. When the road network structure changes, these methods are often not transferable to other cities. In fact, there exist invariant mobility patterns between different cities: 1) People prefer paths with the minimal travel cost; 2) The travel cost of roads has an invariant relationship with the topological features of the road network. Based on the above insight, this paper proposes a Generalizable Trajectory Generation model (GTG). The model consists of three parts: 1) Extracting city-invariant road representation based on Space Syntax method; 2) Cross-city travel cost prediction through disentangled adversarial training; 3) Travel preference learning by shortest path search and preference update. By learning invariant movement patterns, the model is capable of generating trajectories in new cities. Experiments on three  datasets demonstrates that our model significantly outperforms existing models in terms of generalization ability.