3754 papers
MemOCR is a multimodal memory agent that enhances long-horizon reasoning under tight context budgets by converting structured rich-text history into a visually compressed image, allowing the agent to prioritize crucial evidence through layout-aware information density while aggressively reducing low-value details.
This paper introduces MHDash, an open-source platform that enables fine-grained, multi-turn evaluation of AI assistants for mental health support, revealing that conventional aggregate metrics fail to capture critical safety risks and that models often struggle specifically with high-risk cases despite strong overall performance.
This paper establishes a rate-distortion theorem demonstrating that hallucinations in large language models are an inevitable consequence of information-theoretic optimal memory compression when storing sparse facts, forcing the model to confidently assign high scores to non-facts rather than abstain.
This paper introduces EverMemBench, the first benchmark designed to evaluate long-horizon memory in multi-party collaborative dialogues, revealing that current LLM systems struggle with multi-hop reasoning, temporal versioning, and implicit relevance retrieval in realistic, complex interaction scenarios.
This paper introduces DEFT, a diffusion-based trajectory generator that enables robots to achieve fail-active operation by successfully completing tasks under arbitrary actuation failures, outperforming classical methods in both simulation and real-world scenarios while demonstrating robust zero-shot generalization.
The paper proposes DMS2F-HAD, a dual-branch Mamba-based network that efficiently fuses spatial and spectral features to achieve state-of-the-art accuracy and significantly faster inference speeds for hyperspectral anomaly detection across multiple benchmark datasets.
This paper introduces Fine-grained Group Policy Optimization (FGO), a reinforcement learning algorithm that effectively compresses verbose Chain-of-Thought reasoning in Large Language Models while simultaneously addressing the data inefficiency and entropy collapse limitations of Group Relative Policy Optimization (GRPO).
UniWeTok is a unified binary tokenizer featuring a massive $2^{128}$ codebook, a convolution-attention hybrid architecture with SigLu activation, and a novel three-stage training framework that achieves state-of-the-art performance in image generation and multimodal understanding with significantly lower computational costs than existing models.
TikArt is a reinforcement learning-trained multimodal agent that stabilizes fine-grained visual reasoning by employing a Think-Aperture-Observe loop to sequentially acquire and linguistically encode evidence through zooming and segmentation, thereby overcoming the limitations of single-pass global image encoding.
The paper proposes GOT-JEPA, a model-predictive pretraining framework that learns to predict robust tracking models from corrupted observations to improve generalization, and introduces OccuSolver to enhance occlusion handling through iterative, object-aware visibility estimation.
This paper demonstrates that fully autonomous AI analysts can cheaply replicate the analytic diversity and conflicting conclusions observed in human many-analyst studies, revealing that empirical results are highly sensitive to analytic choices and prompting a new transparency norm requiring multiverse-style reporting and full prompt disclosure for AI-generated science.
This paper introduces LAVIDA, a zero-shot video anomaly detection framework that leverages a Multimodal Large Language Model and an Anomaly Exposure Sampler to train exclusively on pseudo-anomalies, achieving state-of-the-art performance across multiple benchmarks without requiring real anomaly data.
PatchDenoiser is a lightweight, parameter-efficient multi-scale patch-based framework that effectively denoises low-dose CT images by balancing noise suppression with anatomical detail preservation, outperforming state-of-the-art CNN and GAN methods while significantly reducing computational costs and energy consumption.
This paper introduces Hubscan, an open-source security scanner that utilizes a multi-detector architecture to identify and mitigate hubness poisoning attacks in Retrieval-Augmented Generation (RAG) systems, achieving high recall rates in detecting adversarial hubs across various vector databases and real-world benchmarks.
This paper proposes Alignment-Aware Masked Learning (AML), a training strategy that improves Referring Image Segmentation by quantifying pixel-level vision-language alignment to mask unreliable regions during optimization, thereby achieving state-of-the-art performance without architectural changes or inference overhead.
The paper introduces a minimal, open-source agentic baseline for automated theorem proving that achieves competitive performance against state-of-the-art systems through iterative refinement and library search, demonstrating superior sample efficiency and cost-effectiveness while serving as a reference for future research.
This paper identifies and quantifies "Defensive Refusal Bias," a safety alignment failure in large language models where legitimate cybersecurity defenders are disproportionately denied assistance for critical tasks due to the presence of security-sensitive keywords, a problem exacerbated by explicit authorization attempts and current reliance on semantic similarity rather than intent reasoning.
The paper introduces CARE, an evidence-grounded agentic framework that enhances clinical accountability and reasoning accuracy in multi-modal medical AI by decomposing tasks into specialized modules for entity proposal, pixel-level localization, and evidence-based reasoning, thereby outperforming state-of-the-art models on medical VQA benchmarks.
The paper proposes SEED-SET, a Bayesian experimental design framework that combines objective evaluations and subjective stakeholder preferences via hierarchical Gaussian Processes to efficiently and interpretably benchmark the ethical alignment of autonomous systems in high-stakes domains.
The paper introduces BrandFusion, a novel multi-agent framework that enables seamless brand integration in text-to-video generation by combining an offline brand knowledge base with an online iterative refinement process to balance semantic fidelity, brand recognizability, and contextual naturalness.