Knowledge catalogue
arXiv:2608.16225v1 Announce Type: new Abstract: Standard Transformers have proven effective in point cloud object classification, but their performance in dense prediction tasks within complex scenes
arXiv:2608.16637v1 Announce Type: new Abstract: LLMs remain unreliable for long-horizon planning, often generating logically inconsistent or non-applicable plans. Recent hybrid methods instead transla
arXiv:2608.14708v1 Announce Type: new Abstract: Compressive sensing (CS) enables accurate signal reconstruction from sparse measurements and is widely applied in medical imaging, remote sensing, and i
arXiv:2512.03444v2 Announce Type: replace Abstract: Deep learning methods have significantly enhanced motion planning for robotic manipulators by leveraging prior experiences within planning datasets.
arXiv:2608.14701v1 Announce Type: new Abstract: Soft-biometric attributes such as gender, age, and ethnicity provide valuable ancillary evidence when full identity recognition is not feasible, support
arXiv:2608.15504v1 Announce Type: new Abstract: Encrypted traffic classification is vital for network security, yet real-world deployments are inherently sensitive to rare but high-loss errors such as
arXiv:2608.15230v1 Announce Type: new Abstract: Although recent trajectory prediction and end-to-end autonomous driving methods improve robustness in urban environments, they still lack meaningful con
arXiv:2608.14881v1 Announce Type: new Abstract: AI co-scientists that generate hypotheses, retrieve related work, design experiments, execute code, and draft full papers are beginning to change how re
arXiv:2608.16717v1 Announce Type: new Abstract: Video generation is rapidly evolving from single-shot clips to multi-shot narratives, where the human character serves as the core narrative anchor. How
arXiv:2608.15115v1 Announce Type: new Abstract: Adversarial examples generated on a surrogate deep neural network (DNN) can often successfully fool other black-box DNN models. This cross-model transfe
arXiv:2608.16419v1 Announce Type: cross Abstract: Large language models can describe mechanisms, yet scalable post-training still depends on costly, manually curated biological reasoning traces. Here
arXiv:2608.14676v1 Announce Type: cross Abstract: In 5G/6G wireless systems, accurate and timely channel estimation is critical to ensure reliable communication under complex, fast-changing radio cond
arXiv:2608.15285v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) is a natural way to adapt pretrained vision-language-action (VLA) policies, but most adapter designs apply temp
arXiv:2608.15314v1 Announce Type: new Abstract: Ultra-reliable low-latency communication (URLLC) requires precise identification of spatial regions where the signal-to-noise ratio (SNR) falls below an
arXiv:2608.16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation. As agents exchange information and jointly make decisions, their
arXiv:2608.15309v1 Announce Type: new Abstract: Continuous multimodal sensing now allows human physiology to be observed throughout daily life rather than only during occasional clinical visits. Howev
arXiv:2505.23863v3 Announce Type: replace-cross Abstract: Understanding chaotic dynamics is a fundamental problem across scientific disciplines, including climate science, neuroscience, and fluid dyna
arXiv:2608.16198v1 Announce Type: cross Abstract: Dermatology models face distribution shifts in teledermatology settings, where submitted images differ from the training data in lighting, angle, dist
arXiv:2510.07546v2 Announce Type: replace Abstract: We address the task of video style transfer with diffusion models, where the goal is to preserve the context of an input video while rendering it in
arXiv:2608.14658v1 Announce Type: cross Abstract: We introduce pico-type, a byte-level multi-head content classifier with approximately 1.5 million parameters that simultaneously predicts seven conten
arXiv:2608.14619v1 Announce Type: new Abstract: This work proposes a new interpretable neural operator framework, termed the Physics Informed Kernel Function Neural Operator (PIKFNO), which explicitly
arXiv:2608.15705v1 Announce Type: new Abstract: Controllable text-to-image diffusion models can often follow the global layout of spatial conditions, yet still violate fine-grained structures such as
arXiv:2608.16793v1 Announce Type: new Abstract: Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model.
arXiv:2608.15673v1 Announce Type: cross Abstract: Large language model guardrails can be viewed as policy-consistency problems: a system must determine which policy-relevant facts hold in a prompt-res
arXiv:2608.16229v1 Announce Type: new Abstract: Coordinated multi-agent exploration requires not only efficient individual coverage but also non-redundant coverage across agents over extended planning
arXiv:2608.15131v1 Announce Type: new Abstract: Digital platforms govern by changing rules: rankings, monetization thresholds, moderation standards, verification systems, disclosure requirements, appe
arXiv:2608.14606v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic survey respondents, but existing evaluations ask whether answers look plausible at the
arXiv:2608.15719v1 Announce Type: new Abstract: Osteoarthritis (OA) is a progressive chronic joint disease resulting in a breakdown of articular cartilage and bone when damaged joint tissues are not a
arXiv:2608.15931v1 Announce Type: new Abstract: We present PLSQLBench, to our knowledge the first benchmark for evaluating whether LLMs can write executable PL/SQL programs, with correctness measured
arXiv:2608.16049v1 Announce Type: cross Abstract: Social robots are being developed for homes, schools, and other environments where they will interact with diverse users. While Human-Robot Interactio
arXiv:2608.16407v1 Announce Type: cross Abstract: Point-of-interest (POI) recommendation models based on graph neural networks achieve strong performance by propagating collaborative signals over user
arXiv:2311.02629v5 Announce Type: replace Abstract: We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks
arXiv:2608.16831v1 Announce Type: new Abstract: Generative pretraining established reusable task representations; later work on language-based task conditioning and in-context learning showed that a f
arXiv:2608.14741v1 Announce Type: cross Abstract: We introduce PolyComp, a procedurally generated and verified benchmark that stresses visual recognition and compositional spatial reasoning. In each p
arXiv:2608.16276v1 Announce Type: cross Abstract: Debate is a structured form of persuasive communication that trains argument construction, rebuttal, oral delivery, and audience awareness. These skil
arXiv:2608.15471v1 Announce Type: cross Abstract: The population structure of an inbred population of 781 people on Norfolk Island in the Pacific, 318 of which are descendants of the original Mutineer
arXiv:2608.15583v1 Announce Type: new Abstract: While Text-to-Image (T2I) diffusion models have achieved remarkable success, precise spatial and orientational control in multi-object scenes remains a
arXiv:2608.14667v1 Announce Type: new Abstract: Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous cap
arXiv:2608.14568v1 Announce Type: new Abstract: As Artificial Intelligence (AI) systems become deeply integrated into critical global infrastructure, the urgency for robust governance frameworks has i
arXiv:2608.14565v1 Announce Type: new Abstract: AI safety research has mainly focused on two areas: technical alignment (ensuring AI systems produce human-aligned outputs) and the regulation of genera
arXiv:2608.14569v1 Announce Type: new Abstract: Neural solvers for constraint satisfaction problems have achieved remarkable in-distribution accuracy, yet they suffer from a fundamental limitation per
arXiv:2608.14566v1 Announce Type: new Abstract: Recent work on evaluating the moral competence of large language models (LLMs) has focused primarily on what we call the moral value problem, i.e., whet
arXiv:2608.14598v1 Announce Type: new Abstract: Medical AI has rapidly improved its ability to perform diagnostic and prognostic tasks that lead to treatment decisions. But understanding of treatment
arXiv:2608.14571v1 Announce Type: new Abstract: With soaring submission counts, stricter reciprocal review policies, widespread adoption of platforms like OpenReview, and without the offsetting pressu
arXiv:2608.16289v1 Announce Type: new Abstract: Automated e-commerce poster design requires both high-quality poster generation and flexible editing of existing designs. However, most existing methods
arXiv:2608.15917v1 Announce Type: cross Abstract: Large-scale pre-training has made robot policy fine-tuning increasingly data-efficient, but this progress has largely been driven by datasets and embo
arXiv:2608.16323v1 Announce Type: cross Abstract: The spread of misinformation on social networks poses a significant challenge to online communities and society at large. Not all users contribute equ
arXiv:2608.15362v1 Announce Type: cross Abstract: We propose a methodology based on the standard ReLU Deep Neural Networks (DNN) to make predictions and quantify their uncertainty. Classically, people
arXiv:2608.15810v1 Announce Type: new Abstract: Runtime compression of serving state trades quality for capacity with no priced guarantee: systems adapt precision on load signals with no soundness sta
arXiv:2608.16003v1 Announce Type: new Abstract: Automated checking pipelines increasingly place one language model as the checker and another (or the same one) as the fixer. We ask whether that wiring
arXiv:2603.08590v3 Announce Type: replace Abstract: Text-to-motion generation has advanced with larger corpora and stronger generators, yet many models still rely on holistic frame- or clip-level late
arXiv:2608.14724v1 Announce Type: cross Abstract: The rapid advancement of intelligent transportation systems and autonomous driving relies heavily on multi-modal urban traffic datasets. However, cura
arXiv:2509.10691v3 Announce Type: replace-cross Abstract: Decentralized federated learning enables collaborative model training without a central server, but shared model updates can still leak sensit
arXiv:2602.08261v2 Announce Type: replace Abstract: Auto-bidding systems strive to maximize marketing value while maintaining high compliance with efficiency constraints, such as Target Cost-Per-Actio
arXiv:2608.16565v1 Announce Type: new Abstract: This cumulative habilitation thesis studies probabilistic circuits (PCs) as a powerful and tractable framework for reasoning and learning under uncertai
arXiv:2608.15112v1 Announce Type: new Abstract: Solving the time-dependent Schrodinger equation (TDSE) via traditional numerical methods is computationally intensive. Transformer models offer a compel
arXiv:2608.16213v1 Announce Type: new Abstract: Intelligence is constituted by extit{process} (iterative activity through which output emerges), not in the output itself. Generative AI (GenAI) is trai
arXiv:2508.08517v2 Announce Type: replace-cross Abstract: Surrogate modeling for systems with high-dimensional quantities of interest remains challenging, particularly when training data are costly to
arXiv:2608.15104v1 Announce Type: new Abstract: Point cloud completion is inherently ill-posed due to severe sparsity and ambiguity in partial observations. Existing multi-view methods alleviate this
arXiv:2507.09562v2 Announce Type: replace-cross Abstract: The Segment Anything Model (SAM) has transformed image segmentation by introducing a prompt-based paradigm that enables strong zero-shot gener