arXiv:2605.05155v1 Announce Type: cross Abstract: As 3D Gaussian Splatting (3DGS) gains attention in immersive media and digital content creation, assessing the aesthetics of 3D scenes becomes important in helping creators build more visually compelling 3D content. However, existing evaluation methods for 3D scenes primarily emphasize reconstruction fidelity and perceptual realism, largely overlooking higher-level aesthetic attributes […]
Taming Outlier Tokens in Diffusion Transformers
arXiv:2605.05206v1 Announce Type: cross Abstract: We study outlier tokens in Diffusion Transformers (DiTs) for image generation. Prior work has shown that Vision Transformers (ViTs) can produce a small number of high-norm tokens that attract disproportionate attention while carrying limited local information, but their role in generative models remains underexplored. We show that this phenomenon appears […]
8 quick tips for data-model integration in ecology
arXiv:2511.15721v2 Announce Type: replace Abstract: Theoretical ecologists have long leveraged empirical data in various forms to advance ecology. Recently increased volumes and access to ecological data present an expanding set of opportunities for theoreticians to inform model development, framing, and interpretation. Whereas statisticians have collective guidance on best practices for data use, theoreticians might lack […]
Multiple timescales in collective motion: daily and intraday upstream fish migration focusing on Feller condition
arXiv:2602.06606v2 Announce Type: replace Abstract: Fish migration is a collective phenomenon that has multiple timescales, ranging from daily to intraday (hourly or even finer). We propose a unified mathematical approach using diffusion bridges, nonlinear stochastic differential equations with pinned initial and terminal conditions, to model both daily and intraday fish migration phenomena. Drift and diffusion […]
ROZA Graphs: Self-Improving Near-Deterministic RAG through Evidence-Centric Feedback
arXiv:2604.07595v3 Announce Type: replace Abstract: Language model agents reason from scratch on every query, discarding their chain of thought after each run. The result is lower accuracy and high run-to-run variance. We introduce reasoning graphs, which persist the per-evidence chain of thought as structured edges. Unlike prior memory that retrieves distilled strategies by query similarity, […]
Valley3: Scaling Omni Foundation Models for E-commerce
arXiv:2605.01278v2 Announce Type: replace Abstract: In this work, we present Valley3, an omni multimodal large language model (MLLM) developed for diverse global e-commerce tasks, with unified understanding and reasoning capabilities across text, images, video, and audio. A key feature of Valley3 is its native multilingual audio capability for e-commerce, developed by extending vision-language models to […]
CreativityBench: Evaluating Agent Creative Reasoning via Affordance-Based Tool Repurposing
arXiv:2605.02910v2 Announce Type: replace Abstract: Recent advances in large language models have led to strong performance on reasoning and environment-interaction tasks, yet their ability for creative problem-solving remains underexplored. We study this capability through the lens of creative tool use, where a model repurposes available objects by reasoning about their affordances and attributes rather than […]
Actionable Real-Time Modeling of Surgical Team Dynamics via Time-Expanded Interaction Graphs
arXiv:2605.04169v1 Announce Type: new Abstract: Surgical team performance arises from complex interactions between technical execution and non-technical skills, including communication and coordination dynamics. However, current surgical AI systems predominantly model visual workflow signals, lacking structured representations of intraoperative team interactions over time. We propose a real-time actionable approach for modeling surgical team dynamics using time-expanded […]
Dataset-Driven Channel Masks in Transformers for Multivariate Time Series
arXiv:2410.23222v3 Announce Type: replace-cross Abstract: Recent advancements in foundation models have been successfully extended to the time series (TS) domain, facilitated by the emergence of large-scale TS datasets. However, previous efforts have primarily Capturing channel dependency (CD) is essential for modeling multivariate time series (TS), and attention-based methods have been widely employed for this purpose. […]
Seeing the Goal, Missing the Truth: Human Accountability for AI Bias
arXiv:2602.09504v2 Announce Type: replace-cross Abstract: This research explores how human-defined goals influence the behavior of Large Language Models (LLMs) through purpose-conditioned cognition. Using financial prediction tasks, we show that revealing the downstream use (e.g., predicting stock returns or earnings) of LLM outputs leads the LLM to generate biased sentiment and competition measures, even though these […]
The Tsetlin Machine Goes Deep: Logical Learning and Reasoning With Graphs
arXiv:2507.14874v2 Announce Type: replace-cross Abstract: Pattern recognition with concise and flat AND-rules makes the Tsetlin Machine (TM) both interpretable and efficient, while the power of Tsetlin automata enables accuracy comparable to deep learning on an increasing number of datasets. We introduce the Graph Tsetlin Machine (GraphTM) for learning interpretable deep clauses from graph-structured input. Moving […]
Are Multimodal LLMs Ready for Clinical Dermatology? A Real-World Evaluation in Dermatology
arXiv:2605.04098v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have demonstrated promise on publicly available dermatology benchmarks. However, benchmark performance may not generalize to real-world dermatologic decision-making. To quantify this benchmark-to-bedside gap, we evaluated four open-weight MLLMs (InternVL-Chat v1.5, LLaVA-Med v1.5, SkinGPT4 and MedGemma-4B-Instruct) and one commercial MLLM (GPT-4.1) across three publicly available dermatology […]