2057 papers
This paper proposes a transformer-based framework for skin cancer case retrieval that effectively combines reference images and textual descriptors by learning hierarchical representations and performing joint global-local alignment, thereby achieving state-of-the-art performance on the Derm7pt dataset to support clinical decision-making.
This paper proposes a unified taxonomy and evaluation framework for latent world models in automated driving, organizing design choices by latent representations and structural priors while identifying key internal mechanics and research directions to enhance robustness, generalization, and deployability.
This study demonstrates that integrating sentiment scores derived from a finetuned Qwen3 model analyzing English and Chinese news significantly enhances aluminum price forecasting accuracy and economic utility, particularly during periods of high market volatility, compared to traditional tabular data models.
GST-VLA introduces a novel framework that enhances Vision-Language-Action models by converting visual observations into anisotropic 3D Gaussian spatial tokens and employing 3D Depth-Aware Chain-of-Thought reasoning to achieve state-of-the-art performance on precision-demanding robotic manipulation tasks.
This paper introduces Doki, a text-native interface that enables users of varying expertise to author generative videos by defining assets, scenes, and edits directly within a freeform text document, thereby shifting video creation from specialized tools to a natural writing process.
The paper proposes Sim2Act, a robust simulation-to-decision framework that enhances policy reliability in mission-critical domains by combining an adversarial calibration mechanism to align simulation fidelity with decision impact and a group-relative perturbation strategy to stabilize learning without overly conservative constraints.
This paper introduces WS-Net, a deep unmixing framework that combines state-space modeling, wavelet-fused encoding, and a specialized weak signal attention mechanism to effectively recover weak spectral signals and significantly improve abundance estimation accuracy in hyperspectral images under low signal-to-noise conditions.
PlayWorld introduces a fully autonomous pipeline that trains high-fidelity, physically consistent video world models from unsupervised robot self-play, outperforming human-collected data in predicting complex interactions and significantly boosting real-world reinforcement learning success rates.
This paper presents an automated pipeline leveraging Large Language Models to detect and diagnose flaky tests in quantum software, successfully expanding an existing dataset by 54% and demonstrating that models like Google Gemini can achieve high accuracy (F1-scores up to 0.9643) in classifying flakiness and identifying root causes.
This paper introduces Pichay, a demand paging system that treats LLM context windows as a memory hierarchy rather than a static cache, successfully reducing context consumption by up to 93% in production by evicting stale content and dynamically reloading it only when needed.
This paper proposes "AI phenomenology" as a research framework that prioritizes users' first-person lived experiences over traditional performance metrics to better understand and guide the bidirectional alignment between humans and AI systems, offering a set of methodological tools, design concepts, and a research agenda derived from three empirical studies.
This paper introduces CMA-ES-IG, an algorithm that enhances robot preference learning by generating perceptually distinct and informative queries, thereby improving scalability, robustness, and user experience compared to existing state-of-the-art methods.
This paper analyzes gender bias in audio deepfake detection using the ASVspoof 5 dataset and a ResNet-18 classifier, demonstrating that while aggregate metrics like Equal Error Rate may suggest low disparity, fairness-aware evaluation reveals significant gender-specific error distributions that necessitate more equitable and robust detection systems.
This study systematically characterizes the unique threat landscape of multi-agent AI systems and empirically evaluates 16 security frameworks, revealing that none achieve majority coverage of the identified risks, with Non-Determinism and Data Leakage being the most under-addressed domains.
This paper introduces Arbiter, a framework that combines formal rules with multi-model LLM analysis to detect interference patterns in coding agent system prompts, revealing that prompt architecture influences failure types and that multi-model evaluation uncovers distinct vulnerabilities missed by single-model approaches.
This paper introduces Semantic Level of Detail (SLoD), a framework that utilizes heat kernel diffusion on hyperbolic manifolds to enable continuous, principled control over knowledge abstraction levels in AI memory systems, automatically detecting emergent semantic boundaries in both synthetic and real-world knowledge graphs without manual supervision.
This paper introduces \texttt{EinSum}, a tensor-relational extension of Einstein Summation Notation that automatically rewrites computations to leverage efficient numerical kernels for dense operations while utilizing relational systems to manage large-scale sparsity.
The paper introduces BiCLIP, a simple and parameter-efficient framework that achieves state-of-the-art few-shot domain adaptation for vision-language models by applying a structured geometric transformation to align multimodal features across disparate domains using a small set of anchor samples.
The paper introduces VoxEmo, a comprehensive benchmark and toolkit for evaluating Speech Large Language Models on speech emotion recognition across 35 corpora and 15 languages, featuring a distribution-aware soft-label protocol that reveals how these models uniquely align with human subjective emotion distributions despite trailing supervised baselines in hard-label accuracy.
PathoScribe is a unified retrieval-augmented large language model framework that transforms static pathology archives into an active, reasoning-enabled clinical intelligence platform, enabling natural language case retrieval, automated cohort construction, and real-time diagnostic support with high accuracy and efficiency.