arXiv:2508.20705v2 Announce Type: replace-cross Abstract: Recent advances in self-supervised learning for EEG representation have largely relied on masked reconstruction, where models are trained to recover randomly masked signal segments. While effective at modeling local dependencies, such objectives are inherently limited in capturing the global dynamics and long-range dependencies essential for characterizing neural activity. To address […]
New Hybrid Heuristics for Pseudo-Boolean Propagation
arXiv:2511.21417v2 Announce Type: replace Abstract: In pseudo-boolean solving the currently most successful unit propagation strategy is a hybrid mode combining the watched literal scheme with the counting method. This short paper introduces new heuristics for this hybrid decision, which are able to drastically outperform the current method in the RoundingSAT solver.
EDVD-LLaMA: Explainable Deepfake Video Detection via Multimodal Large Language Model Reasoning
arXiv:2510.16442v2 Announce Type: replace-cross Abstract: The rapid development of deepfake video technology has not only facilitated artistic creation but also made it easier to spread misinformation. Traditional deepfake video detection (DVD) methods face issues such as a lack of transparency in their principles and insufficient generalization capabilities to cope with evolving forgery techniques. This highlights […]
ResSVD: Residual Compensated SVD for Large Language Model Compression
arXiv:2505.20112v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated impressive capabilities in a wide range of downstream natural language processing tasks. Nevertheless, their considerable sizes and memory demands hinder practical deployment, underscoring the importance of developing efficient compression strategies. Singular value decomposition (SVD) decomposes a matrix into orthogonal components, enabling efficient low-rank approximation. […]
Discovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation
arXiv:2510.22107v2 Announce Type: replace-cross Abstract: Capturing diversity is crucial in conditional and prompt-based image generation, particularly when conditions contain uncertainty that can lead to multiple plausible outputs. To generate diverse images reflecting this diversity, traditional methods often modify random seeds, making it difficult to discern meaningful differences between samples, or diversify the input prompt, which […]
A Solver-in-the-Loop Framework for Improving LLMs on Answer Set Programming for Logic Puzzle Solving
arXiv:2512.17093v1 Announce Type: new Abstract: The rise of large language models (LLMs) has sparked interest in coding assistants. While general-purpose programming languages are well supported, generating code for domain-specific languages remains a challenging problem for LLMs. In this paper, we focus on the LLM-based generation of code for Answer Set Programming (ASP), a particularly effective […]
Reinforcement Learning for Self-Improving Agent with Skill Library
arXiv:2512.17102v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions but struggle to continuously improve and adapt when deployed in new environments. One promising approach is implementing skill libraries that allow agents to learn, validate, and apply new skills. However, current skill library approaches rely […]
Bitbox: Behavioral Imaging Toolbox for Computational Analysis of Behavior from Videos
arXiv:2512.17655v1 Announce Type: cross Abstract: Computational measurement of human behavior from video has recently become feasible due to major advances in AI. These advances now enable granular and precise quantification of facial expression, head movement, body action, and other behavioral modalities and are increasingly used in psychology, psychiatry, neuroscience, and mental health research. However, mainstream […]
Realistic threat perception drives intergroup conflict: A causal, dynamic analysis using generative-agent simulations
arXiv:2512.17066v1 Announce Type: new Abstract: Human conflict is often attributed to threats against material conditions and symbolic values, yet it remains unclear how they interact and which dominates. Progress is limited by weak causal control, ethical constraints, and scarce temporal data. We address these barriers using simulations of large language model (LLM)-driven agents in virtual […]
You Only Train Once: Differentiable Subset Selection for Omics Data
arXiv:2512.17678v1 Announce Type: cross Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling. However, most existing feature selection approaches either operate as multi-stage pipelines or rely on post hoc feature attribution, making selection and prediction weakly coupled. In this work, we present YOTO […]