Knowledge catalogue
arXiv:2607.29045v1 Announce Type: new Abstract: Emotional video captioning (EVC) aims to describe a video with both factual correctness and affective expressiveness. It requires a model to perceive su
arXiv:2607.29494v1 Announce Type: new Abstract: On-policy distillation (OPD) provides dense teacher supervision along student-generated trajectories, but its online rollout process incurs substantial
arXiv:2607.28986v1 Announce Type: cross Abstract: Zero-shot image captioning (ZIC) describes images without paired image-caption supervision during captioner training, relying on text-only corpora and
arXiv:2607.28952v1 Announce Type: new Abstract: Humanoid and quadrupedal robots have the potential to revolutionize the way we work, interact, and coexist with intelligent machines. To understand thei
arXiv:2607.28890v1 Announce Type: cross Abstract: Evaluations of LLM-assisted qualitative coding almost universally measure model performance as agreement with human coders, a practice that presumes h
arXiv:2607.28854v1 Announce Type: new Abstract: Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models. Ma
Amazon Bedrock now supports automatic Automated Reasoning policy refinement. The refinement engine diagnoses failing tests and proposes formal-logic fixes for rule issues and language issues, and you
arXiv:2607.29464v1 Announce Type: new Abstract: Different Length Alignment Sewing (DLAS), which involves stretching the shorter fabric to match the longer one and sewing them together in a straight li
arXiv:2607.29405v1 Announce Type: new Abstract: Agentic AI systems act through multi-step trajectories that combine planning, tool use, memory, interaction, and adaptation. This behavior stretches val
arXiv:2607.28980v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) have recently emerged as a promising paradigm for enabling knowledge transfer across diverse domains. Unlike traditional
arXiv:2607.29402v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems synergize retrieval mechanisms with generative language models to enhance the accuracy and relevance of r
arXiv:2607.29190v1 Announce Type: new Abstract: Tool-using LLM agents act on typed tool returns, records pairing provenance and categorical fields with numerical values. Runtime permission gates gener
arXiv:2607.29082v1 Announce Type: new Abstract: Child malnutrition remains a major public health challenge in low- and middle-income countries, particularly in South Asia, where early identification o
arXiv:2607.28636v1 Announce Type: new Abstract: LLMs increasingly serve as automated judges, but their judgments remain vulnerable to cognitive biases. Existing mitigations mostly rely on prompt-drive
arXiv:2604.05150v2 Announce Type: replace-cross Abstract: We study compiled AI, a paradigm in which large language models generate executable code artifacts during a compilation phase, after which wor
arXiv:2607.28647v1 Announce Type: cross Abstract: This paper presents ConnectED, a human-centered AI system that supports the full instructional lifecycle in Vietnamese education by linking curriculum
arXiv:2607.29593v1 Announce Type: new Abstract: This paper studies the policy gradient update for a multi-arm bandit problem in diffusion environment that is described by a stochastic differential equ
arXiv:2509.22874v2 Announce Type: replace Abstract: Deformable image registration (DIR) is central to medical image analysis, supporting spatial alignment for longitudinal studies and multi-modal fusi
arXiv:2607.28904v1 Announce Type: cross Abstract: This paper presents a speculative Human-Computer Interaction design proposal for encouraging geopolitical reflexivity amongst tech workers at geopolit
arXiv:2603.13415v2 Announce Type: replace Abstract: Valence-arousal (VA) estimation is crucial for capturing the nuanced nature of human emotions in naturalistic environments. While pre-trained vision
arXiv:2607.28771v1 Announce Type: new Abstract: Medical foundation models (FMs) are increasingly used for brain MRI analysis. However, their evaluation remains dominated by high-resource datasets, lea
arXiv:2607.28907v1 Announce Type: cross Abstract: Deep joint source--channel coding (Deep JSCC) enables visual semantic transmission by mapping inputs directly to channel symbols and task outputs, but
arXiv:2607.29202v1 Announce Type: new Abstract: With growing privacy and portability concerns, source-free domain adaptation requires only a source pre-trained model and an unlabeled target domain, al
arXiv:2607.29246v1 Announce Type: new Abstract: Modern large language models (LLMs) are expected not just to answer correctly, but to adapt their behavior to different human values and use cases. As a
arXiv:2607.29112v1 Announce Type: cross Abstract: Audio-visual speech recognition (AVSR) relies on effective fusion of audio and visual modalities, yet existing approaches treat cross-modal interactio
arXiv:2607.29491v1 Announce Type: cross Abstract: Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circu
arXiv:2501.04426v2 Announce Type: replace-cross Abstract: Offline diversity maximization under imitation constraints can transform demonstration data into a set of distinct behavioral policies, improv
arXiv:2603.08390v3 Announce Type: replace-cross Abstract: Bimanual articulated-object interaction generation requires a model to capture the evolution of object articulation, coordination between the
arXiv:2607.29568v1 Announce Type: new Abstract: Multi-phase Contrast-Enhanced Computed Tomography (CECT) plays a central role in the diagnosis and characterization of focal lesions by capturing tempor
arXiv:2607.29441v1 Announce Type: new Abstract: Many real-world systems rely on predictive models to inform decisions, and fairness concerns arise in both the prediction and decision stages. We introd
arXiv:2602.02685v3 Announce Type: replace Abstract: Decentralized Diffusion Models (DDMs) route denoising through experts trained independently on disjoint data clusters, which can strongly disagree i
arXiv:2607.28945v1 Announce Type: new Abstract: Synthetic tabular data is increasingly used in privacy-preserving data sharing, data augmentation, and to mitigate downstream classifier bias. State-of-
fascinating comments on possible slowdown – but probably a red herring. - Anthropic and OpenAI presumably aren’t colluding to slowdown, they are asking governments to build a framework such that a slo
arXiv:2607.29079v1 Announce Type: new Abstract: Training-free acceleration makes diffusion-based multimodal large language models (dMLLMs) more deployable, but it may silently change generated content
arXiv:2607.29182v1 Announce Type: cross Abstract: Transcranial focused ultrasound (tFUS) is a non-invasive technique that delivers focused acoustic energy through the skull for neuromodulation and the
arXiv:2607.29132v1 Announce Type: new Abstract: Surgical gauze is an essential part of surgical procedures, primarily used for controlling bleeding and absorbing bodily fluids. The post-surgical reten
arXiv:2510.06945v2 Announce Type: replace Abstract: Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evalua
arXiv:2607.28698v1 Announce Type: new Abstract: Flow matching assumes fully observed training data, which many real-world applications rarely provide. We propose Missing-Data Flow Matching, which trea
arXiv:2607.28881v1 Announce Type: new Abstract: As more responsibility is placed upon AI systems, it becomes increasingly important to guarantee that these systems are aligned with humanity. A common
Gary Marcus warns that, with increasingly sophisticated generative‑AI tools, future generations may struggle to tell what is real from what is fabricated. Matt Stoller adds that generative AI will tra
arXiv:2607.29213v1 Announce Type: cross Abstract: Modern recommender systems in food delivery increasingly leverage multimodal signals, including images, text, and user interaction histories, to enhan
arXiv:2607.28916v1 Announce Type: cross Abstract: Multistep credit assignment is critical for sample-efficient reinforcement learning, yet managing off-policy bias in Q-learning remains a fundamental
arXiv:2607.29638v1 Announce Type: new Abstract: Multi-page document visual question answering requires locating sparse evidence at both the page and region levels. Existing approaches typically emphas
arXiv:2607.29513v1 Announce Type: new Abstract: Generating safe corridors is essential for collision-free robotic motion planning, yet most existing methods rely on predefined reference paths, which b
arXiv:2607.28674v1 Announce Type: new Abstract: Understanding how computational effort is allocated across individual chain-of-thought (CoT) reasoning steps remains an open challenge: existing interpr
arXiv:2607.28849v1 Announce Type: cross Abstract: Bilevel reinforcement learning (RL) is an important framework within the literature of RL that can be used to formalize various categories of problems
arXiv:2606.14142v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world kn
arXiv:2510.17136v2 Announce Type: replace Abstract: The generation of high-quality, diverse, and prompt-aligned images is a central goal in image-generating diffusion models. The popular classifier-fr
arXiv:2601.20533v2 Announce Type: replace-cross Abstract: Survival analysis has become a standard approach for modelling time to default by time-varying covariates in credit risk. Unlike most existing
arXiv:2512.13677v2 Announce Type: replace Abstract: In this paper, we present JoVA, a streamlined framework that unifies joint video-audio generation and editing. While existing methods often rely on
arXiv:2603.09956v2 Announce Type: replace Abstract: We present the KinoDynamic Motion Retargeting (KDMR) framework, a novel approach for humanoid locomotion that models the retargeting process as a mu
arXiv:2607.29185v1 Announce Type: new Abstract: Reward models (RMs) are central to aligning large language models with human preferences via reinforcement learning. Although traditional scalar RMs ena
arXiv:2607.28638v1 Announce Type: new Abstract: As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights. Viewed
arXiv:2607.29320v1 Announce Type: new Abstract: Graphical user interface (GUI) agents based on large language models are increasingly deployed across mobile, web, and desktop environments. However, ex
arXiv:2607.29093v1 Announce Type: cross Abstract: Metasignal is an open-source Python package for signal detection theory (SDT) and metacognitive measurement. It implements the 17 metacognitive measur
arXiv:2607.28737v1 Announce Type: cross Abstract: We investigate imitation learning through the lens of third-person observation and propose a framework for mirror learning: acquiring actionable polic
arXiv:2607.28681v1 Announce Type: cross Abstract: Functional magnetic resonance imaging (fMRI) is a widely used technique for studying the brain. Recent methods that utilize graph neural networks (GNN
arXiv:2607.29203v1 Announce Type: new Abstract: Deploying drone swarms to track a dynamic target in cluttered environments presents severe computational and safety challenges. We propose MROPE, a hier
arXiv:2607.29207v1 Announce Type: new Abstract: Multi-modal object Re-Identification (ReID) aims to retrieve target instances by leveraging complementary information across modalities. However, existi
arXiv:2607.29531v1 Announce Type: new Abstract: Cross-site identification of major depressive disorder (MDD) from resting-state functional magnetic resonance imaging (rs-fMRI) is hindered by inter-sit