๐ Original Info Title: ArXiv ID: 2512.17629 Date: Authors: Unknown ๐ Abstract Prescriptive Process Monitoring (PresPM) recommends interventions during business processes to optimize key performance indicators (KPIs). In realistic settings, โฆ
๐ Original Info Title: ArXiv ID: 2512.17756 Date: Authors: Unknown ๐ Abstract Comprehension of ancient texts plays an important role in archaeology and understanding of Chinese history and civilization. The rapid development of large language models โฆ
๐ Original Info Title: ArXiv ID: 2512.11896 Date: Authors: Unknown ๐ Abstract Pedestrian heat exposure is a critical health risk in dense tropical cities, yet standard routing algorithms often ignore micro-scale thermal variation. Hot Hแบปm is a GeoAI โฆ
๐ Original Info Title: ArXiv ID: 2512.12630 Date: Authors: Unknown ๐ Abstract Fig. 1 . Artists conceptualize original characters (OCs) in their minds before and during constant visual expressions, and such a process is often omitted by typical AI โฆ
๐ Original Info Title: A Comparative Study of Custom CNNs, Pre-trained Models, and Transfer Learning Across Multiple Visual Datasets ArXiv ID: 2601.02246 Date: 2026-01-05 Authors: Annoor Sharara Akhand ๐ Abstract Convolutional Neural Networks (CNNs) โฆ
๐ Original Info Title: A Data-Enhanced Agent-Based Model for Simulating 3D Cancer Spheroid Growth: Integrating Metabolism and Mechanics ArXiv ID: 2512.15361 Date: 2025-12-17 Authors: Pedro Garcia-Gomez, Paula Guerrero-Lopez, Silvia Hervas-Raluy, Jose โฆ
๐ Original Info Title: AgentSHAP: Interpreting LLM Agent Tool Importance with Monte Carlo Shapley Value Estimation ArXiv ID: 2512.12597 Date: 2025-12-14 Authors: Miriam Horovicz ๐ Abstract LLM agents that use external tools can solve complex tasks, โฆ
๐ Original Info Title: Arxiv 2601.01816 ArXiv ID: 2601.01816 Date: Authors: Unknown ๐ Abstract This paper introduces Admissibility Alignment: a reframing of AI alignment as a property of admissible action and decision selection over distributions of โฆ
๐ Original Info Title: Beyond Homophily: Community Search on Heterophilic Graphs ArXiv ID: 2601.01703 Date: 2026-01-05 Authors: Qing Sima, Xiaoyang Wang, Wenjie Zhang ๐ Abstract Community search aims to identify a refined set of nodes that are most โฆ
๐ Original Info Title: Can You Keep a Secret? Exploring AI for Care Coordination in Cognitive Decline ArXiv ID: 2512.12510 Date: 2025-12-14 Authors: Alicia, Lee, Mai Lee Chang, Sreehana Mandava, Destiny Deshields, Hugo Simรฃo, Aaron Steinfeld, Jodi โฆ
๐ Original Info Title: Cost-Efficient Cross-Lingual Retrieval-Augmented Generation for Low-Resource Languages: A Case Study in Bengali Agricultural Advisory ArXiv ID: 2601.02065 Date: 2026-01-05 Authors: Md. Asif Hossain, Nabil Subhan, Mantasha โฆ
๐ Original Info Title: Deferred Commitment Decoding for Diffusion Language Models ArXiv ID: 2601.02076 Date: 2026-01-05 Authors: Yingte Shu, Yuchuan Tian, Chao Xu, Yunhe Wang, Hanting Chen ๐ Abstract Diffusion language models (DLMs) have recently โฆ
๐ Original Info Title: DiG: Differential Grounding for Enhancing Fine-Grained Perception in Multimodal Large Language Model ArXiv ID: 2512.12633 Date: 2025-12-14 Authors: Zhou Tao, Shida Wang, Yongxiang Hua, Haoyu Cao, Linli Xu ๐ Abstract Multimodal โฆ
๐ Original Info Title: Enforcing Temporal Constraints for LLM Agents ArXiv ID: 2512.23738 Date: 2025-12-25 Authors: Adharsh Kamath, Sishen Zhang, Calvin Xu, Shubham Ugare, Gagandeep Singh, Sasa Misailovic ๐ Abstract LLM-based agents are increasingly โฆ
๐ Original Info Title: LIA: Supervised Fine-Tuning of Large Language Models for Automatic Issue Assignment ArXiv ID: 2601.01780 Date: 2026-01-05 Authors: Arsham Khosravani, Alireza Hosseinpour, Arshia Akhavan, Mehdi Keshani, Abbas Heydarnoori ๐ โฆ
๐ Original Info Title: Scale-aware Adaptive Supervised Network with Limited Medical Annotations ArXiv ID: 2601.01005 Date: 2026-01-02 Authors: Zihan Li, Dandan Shan, Yunxiang Li, Paul E. Kinahan, Qingqi Hong ๐ Abstract We propose SASNet, a โฆ
๐ Original Info Title: StressRoBERTa: Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection ArXiv ID: 2512.23813 Date: 2025-12-29 Authors: Amal Alqahtani, Efsun Kayi, Mona Diab ๐ Abstract The prevalence of chronic โฆ
๐ Original Info Title: The Silicon Psyche: Anthropomorphic Vulnerabilities in Large Language Models ArXiv ID: 2601.00867 Date: 2025-12-30 Authors: Giuseppe Canale, Kashyap Thimmaraju ๐ Abstract Large Language Models (LLMs) are rapidly transitioning โฆ
๐ Original Info Title: Theory Trace Card: Theory-Driven Socio-Cognitive Evaluation of LLMs ArXiv ID: 2601.01878 Date: 2026-01-05 Authors: Farzan Karimi-Malekabadi, Suhaib Abdurahman, Zhivar Sourati, Jackson Trager, Morteza Dehghani ๐ Abstract โฆ
๐ Original Info Title: ArXiv ID: 2512.19701 Date: Authors: Unknown ๐ Abstract The rapid growth of cloud computing in the Electronic Design Automation (EDA) industry has created a critical need for resource and job lifetime prediction to achieve โฆ
๐ Original Info Title: ArXiv ID: 2512.18504 Date: Authors: Unknown ๐ Abstract Vision-Language Models (VLMs) struggle in open-world applications, where Out-of-Distribution (OOD) concepts often trigger cross-modal alignment collapse and severely โฆ
๐ Original Info Title: ArXiv ID: 2512.20156 Date: Authors: Unknown ๐ Abstract Recent advancements in joint speech-text models have demonstrated great potential for seamless voice interactions. However, existing models face critical challenges: the โฆ
๐ Original Info Title: ArXiv ID: 2512.18689 Date: Authors: Unknown ๐ Abstract Electroencephalography (EEG) signal decoding is a key technology that translates brain activity into executable commands, laying the foundation for direct brainmachine โฆ
๐ Original Info Title: ArXiv ID: 2512.17983 Date: Authors: Unknown ๐ Abstract Human Activity Recognition (HAR) is a foundational task in ubiquitous computing with applications in health monitoring, smart environments, and human-computer interaction. โฆ
๐ Original Info Title: ArXiv ID: 2512.18797 Date: Authors: Unknown ๐ Abstract Detecting synthetic speech is challenging when labeled data are scarce and recording conditions vary. Existing end-to-end deep models often overfit or fail to generalize, โฆ
๐ Original Info Title: ArXiv ID: 2512.19737 Date: Authors: Unknown ๐ Abstract Reliable prediction of train delays is essential for enhancing the robustness and efficiency of railway transportation systems. In this work, we reframe delay forecasting โฆ
๐ Original Info Title: ArXiv ID: 2512.20420 Date: Authors: Unknown ๐ Abstract Multi-task learning (MTL) aims to leverage shared knowledge across tasks to improve generalization and parameter efficiency, yet balancing resources and mitigating โฆ
๐ Original Info Title: ArXiv ID: 2512.17846 Date: Authors: Unknown ๐ Abstract We present Planning as Descent (PaD), a framework for offline goal-conditioned reinforcement learning that grounds trajectory synthesis in verification. Instead of learning โฆ
๐ Original Info Title: ArXiv ID: 2512.17946 Date: Authors: Unknown ๐ Abstract Music emotion recognition is a key task in symbolic music understanding (SMER). Recent approaches have shown promising results by fine-tuning large-scale pre-trained models โฆ
๐ Original Info Title: ArXiv ID: 2512.17901 Date: Authors: Unknown ๐ Abstract Despite the superior performance of Large Reasoning Models (LRMs), their reasoning behaviors are often counterintuitive, leading to suboptimal reasoning capabilities. To โฆ
๐ Original Info Title: ArXiv ID: 2512.18619 Date: Authors: Unknown ๐ Abstract We present ChronoDreamer, an action-conditioned world model for contact-rich robotic manipulation. Given a history of egocentric RGB frames, contact maps, actions, and โฆ
๐ Original Info Title: ArXiv ID: 2512.18607 Date: Authors: Unknown ๐ Abstract Understanding what kinds of cooperative structures deep neural networks (DNNs) can represent remains a fundamental yet insufficiently understood problem. In this work, we โฆ
๐ Original Info Title: ArXiv ID: 2512.18857 Date: Authors: Unknown ๐ Abstract Large language models (LLMs) often solve challenging math exercises yet fail to apply the concept right when the problem requires genuine understanding. Popular โฆ
๐ Original Info Title: ArXiv ID: 2512.18733 Date: Authors: Unknown ๐ Abstract Large language model (LLM)-based multiagent systems (MAS) have shown strong capabilities in solving complex tasks. As MAS become increasingly autonomous in various โฆ
๐ Original Info Title: ArXiv ID: 2512.18826 Date: Authors: Unknown ๐ Abstract This survey reviews hyperbolic graph embedding models, and evaluate them on anomaly detection, highlighting their advantages over Euclidean methods in capturing complex โฆ
๐ Original Info Title: ArXiv ID: 2512.18605 Date: Authors: Unknown ๐ Abstract Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks with techniques like Chain-of-Thought (CoT) and Self-Consistency. However, these โฆ
๐ Original Info Title: ArXiv ID: 2512.17911 Date: Authors: Unknown ๐ Abstract Machine unlearning aims to erase requested data from trained models without full retraining. For Reasoning Multimodal Large Language Models (RMLLMs), this is uniquely โฆ
๐ Original Info Title: ArXiv ID: 2512.18158 Date: Authors: Unknown ๐ Abstract All-pairs shortest paths (APSP) is a fundamental algorithm used for routing, logistics, and network analysis, but the cubic time complexity and heavy data movement of the โฆ
๐ Original Info Title: ArXiv ID: 2512.18674 Date: Authors: Unknown ๐ Abstract Mixture-of-Experts (MoE) has become a dominant architecture in large language models (LLMs) due to its ability to scale model capacity via sparse expert activation. โฆ
๐ Original Info Title: ArXiv ID: 2512.18245 Date: Authors: Unknown ๐ Abstract Hyperspectral images with high spectral resolution provide new insights into recognizing subtle differences in similar substances. However, object detection in โฆ
๐ Original Info Title: ArXiv ID: 2512.18687 Date: Authors: Unknown ๐ Abstract Social comparison-the process of evaluating one's rewards relative to others-plays a fundamental role in primate social cognition. However, it remains unknown from a โฆ
๐ Original Info Title: ArXiv ID: 2512.18261 Date: Authors: Unknown ๐ Abstract Artificial Intelligence (AI) has revolutionized software development, particularly by automating repetitive tasks and improving developer productivity. While these โฆ
๐ Original Info Title: ArXiv ID: 2512.18202 Date: Authors: Unknown ๐ Abstract The rapid development of Large Language Models (LLMs) has elevated AI agents from task-specific tools to long-lived, decision-making entities capable of independent โฆ
๐ Original Info Title: ArXiv ID: 2512.18388 Date: Authors: Unknown ๐ Abstract Generative AI has begun to democratize creative work, enabling novices to produce complex artifacts such as code, images, and videos. However, in practice, existing โฆ
๐ Original Info Title: ArXiv ID: 2512.17864 Date: Authors: Unknown ๐ Abstract Plant diseases pose a significant threat to global food security, necessitating accurate and interpretable disease detection methods. This study introduces an interpretable โฆ
๐ Original Info Title: ArXiv ID: 2512.18737 Date: Authors: Unknown ๐ Abstract The estimation of individual treatment effects (ITE) focuses on predicting the outcome changes that result from a change in treatment. A fundamental challenge in โฆ
๐ Original Info Title: ArXiv ID: 2512.18105 Date: Authors: Unknown ๐ Abstract We reformulate the CHSH game in terms of indivisible stochastic processes. Using Barandes's stochastic-quantum correspondence and its associated definition of causal โฆ
๐ Original Info Title: ArXiv ID: 2512.18729 Date: Authors: Unknown ๐ Abstract Models of faults incorporating slip rate-and state-dependent friction have reproduced phenomena from spontaneous slow, aseismic slip to earthquake-generating dynamic โฆ
๐ Original Info Title: ArXiv ID: 2512.19697 Date: Authors: Unknown ๐ Abstract With the rapid growth of data volume in modern telecommunication networks and the continuous expansion of their scale, maintaining high reliability has become a critical โฆ
๐ Original Info Title: ArXiv ID: 2512.19928 Date: Authors: Unknown ๐ Abstract Accurate registration of brain MRI scans is fundamental for cross-subject analysis in neuroscientific studies. This involves aligning both the cortical surface of the brain โฆ
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