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 […]
Detecting and Steering LLMs’ Empathy in Action
arXiv:2511.16699v1 Announce Type: cross Abstract: We investigate empathy-in-action — the willingness to sacrifice task efficiency to address human needs — as a linear direction in LLM activation space. Using contrastive prompts grounded in the Empathy-in-Action (EIA) benchmark, we test detection and steering across Phi-3-mini-4k (3.8B), Qwen2.5-7B (safety-trained), and Dolphin-Llama-3.1-8B (uncensored). Detection: All models show AUROC […]
Prompt-Based Value Steering of Large Language Models
arXiv:2511.16688v1 Announce Type: cross Abstract: Large language models are increasingly used in applications where alignment with human values is critical. While model fine-tuning is often employed to ensure safe responses, this technique is static and does not lend itself to everyday situations involving dynamic values and preferences. In this paper, we present a practical, reproducible, […]
How Well Do LLMs Understand Tunisian Arabic?
arXiv:2511.16683v1 Announce Type: cross Abstract: Large Language Models (LLMs) are the engines driving today’s AI agents. The better these models understand human languages, the more natural and user-friendly the interaction with AI becomes, from everyday devices like computers and smartwatches to any tool that can act intelligently. Yet, the ability of industrial-scale LLMs to comprehend […]
DDTime: Dataset Distillation with Spectral Alignment and Information Bottleneck for Time-Series Forecasting
arXiv:2511.16715v1 Announce Type: cross Abstract: Time-series forecasting is fundamental across many domains, yet training accurate models often requires large-scale datasets and substantial computational resources. Dataset distillation offers a promising alternative by synthesizing compact datasets that preserve the learning behavior of full data. However, extending dataset distillation to time-series forecasting is non-trivial due to two fundamental […]
SafeR-CLIP: Mitigating NSFW Content in Vision-Language Models While Preserving Pre-Trained Knowledge
arXiv:2511.16743v1 Announce Type: cross Abstract: Improving the safety of vision-language models like CLIP via fine-tuning often comes at a steep price, causing significant drops in their generalization performance. We find this trade-off stems from rigid alignment strategies that force unsafe concepts toward single, predefined safe targets, disrupting the model’s learned semantic structure. To address this, […]