arXiv:2511.16961v1 Announce Type: new Abstract: Bayesian Networks (BNs) are an important tool for assisting probabilistic reasoning, but despite being considered transparent models, people have trouble understanding them. Further, current User Interfaces (UIs) still do not clarify the reasoning of BNs. To address this problem, we have designed verbal and visual extensions to the standard BN […]
Fantastic Bugs and Where to Find Them in AI Benchmarks
arXiv:2511.16842v1 Announce Type: new Abstract: Benchmarks are pivotal in driving AI progress, and invalid benchmark questions frequently undermine their reliability. Manually identifying and correcting errors among thousands of benchmark questions is not only infeasible but also a critical bottleneck for reliable evaluation. In this work, we introduce a framework for systematic benchmark revision that leverages […]
Cognitive BASIC: An In-Model Interpreted Reasoning Language for LLMs
arXiv:2511.16837v1 Announce Type: new Abstract: Cognitive BASIC is a minimal, BASIC-style prompting language and in-model interpreter that structures large language model (LLM) reasoning into explicit, stepwise execution traces. Inspired by the simplicity of retro BASIC, we repurpose numbered lines and simple commands as an interpretable cognitive control layer. Modern LLMs can reliably simulate such short […]
Stable diffusion models reveal a persisting human and AI gap in visual creativity
arXiv:2511.16814v1 Announce Type: new Abstract: While recent research suggests Large Language Models match human creative performance in divergent thinking tasks, visual creativity remains underexplored. This study compared image generation in human participants (Visual Artists and Non Artists) and using an image generation AI model (two prompting conditions with varying human input: high for Human Inspired, […]
Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
arXiv:2508.13663v2 Announce Type: replace Abstract: Methods for query answering over incomplete knowledge graphs retrieve entities that are emphlikely to be answers, which is particularly useful when such answers cannot be reached by direct graph traversal due to missing edges. However, existing approaches have focused on queries formalized using first-order-logic. In practice, many real-world queries involve […]
DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing
arXiv:2511.17038v1 Announce Type: new Abstract: From a Bayesian perspective, score-based diffusion solves inverse problems through joint inference, embedding the likelihood with the prior to guide the sampling process. However, this formulation fails to explain its practical behavior: the prior offers limited guidance, while reconstruction is largely driven by the measurement-consistency term, leading to an inference […]
Emergence of psychopathological computations in large language models
arXiv:2504.08016v2 Announce Type: replace Abstract: Can large language models (LLMs) instantiate computations of psychopathology? An effective approach to the question hinges on addressing two factors. First, for conceptual validity, we require a general and computational account of psychopathology that is applicable to computational entities without biological embodiment or subjective experience. Second, psychopathological computations, derived from […]
Spanning Tree Autoregressive Visual Generation
arXiv:2511.17089v1 Announce Type: cross Abstract: We present Spanning Tree Autoregressive (STAR) modeling, which can incorporate prior knowledge of images, such as center bias and locality, to maintain sampling performance while also providing sufficiently flexible sequence orders to accommodate image editing at inference. Approaches that expose randomly permuted sequence orders to conventional autoregressive (AR) models in […]
PepEVOLVE: Position-Aware Dynamic Peptide Optimization via Group-Relative Advantage
arXiv:2511.16912v1 Announce Type: cross Abstract: Macrocyclic peptides are an emerging modality that combines biologics-like affinity with small-molecule-like developability, but their vast combinatorial space and multi-parameter objectives make lead optimization slow and challenging. Prior generative approaches such as PepINVENT require chemists to pre-specify mutable positions for optimization, choices that are not always known a priori, and […]
Supervised Fine Tuning of Large Language Models for Domain Specific Knowledge Graph Construction:A Case Study on Hunan’s Historical Celebrities
arXiv:2511.17012v1 Announce Type: cross Abstract: Large language models and knowledge graphs offer strong potential for advancing research on historical culture by supporting the extraction, analysis, and interpretation of cultural heritage. Using Hunan’s modern historical celebrities shaped by Huxiang culture as a case study, pre-trained large models can help researchers efficiently extract key information, including biographical […]
Mesh RAG: Retrieval Augmentation for Autoregressive Mesh Generation
arXiv:2511.16807v1 Announce Type: cross Abstract: 3D meshes are a critical building block for applications ranging from industrial design and gaming to simulation and robotics. Traditionally, meshes are crafted manually by artists, a process that is time-intensive and difficult to scale. To automate and accelerate this asset creation, autoregressive models have emerged as a powerful paradigm […]
ManifoldFormer: Geometric Deep Learning for Neural Dynamics on Riemannian Manifolds
arXiv:2511.16828v1 Announce Type: cross Abstract: Existing EEG foundation models mainly treat neural signals as generic time series in Euclidean space, ignoring the intrinsic geometric structure of neural dynamics that constrains brain activity to low-dimensional manifolds. This fundamental mismatch between model assumptions and neural geometry limits representation quality and cross-subject generalization. ManifoldFormer addresses this limitation through […]