Knowledge catalogue
arXiv:2601.22328v2 Announce Type: replace Abstract: Real-world scientific systems are rarely observed through complete, regularly sampled state trajectories. Instead, measurements are often partial, n
arXiv:2512.00470v4 Announce Type: replace Abstract: Diffusion models have demonstrated strong capabilities for modeling human-like driving behaviors in autonomous driving, but their iterative sampling
arXiv:2606.00573v1 Announce Type: new Abstract: Vision-language models (VLMs) deliver strong multimodal reasoning capabilities, but their large computational cost and high parameter counts make deploy
arXiv:2511.07910v2 Announce Type: replace Abstract: Large Language Models (LLMs) achieve excellent performance in natural language reasoning tasks through pre-training on vast unstructured text, enabl
arXiv:2606.00260v1 Announce Type: new Abstract: Human Activity Recognition (HAR) from ambient sensors enables smart-home applications such as health monitoring and assisted living. In realistic deploy
arXiv:2511.20639v3 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligenc
arXiv:2606.00776v1 Announce Type: new Abstract: Fast and accurate prediction of crystal properties is a central challenge in new materials design. Graph neural networks and Transformer-based models ha
arXiv:2606.01432v1 Announce Type: new Abstract: Accurate modeling of leaf spectral reflectance from physiological and biochemical traits is essential for advancing remote sensing applications in plant
arXiv:2606.01047v1 Announce Type: new Abstract: Robotic manipulation requires the effective integration of heterogeneous inputs, including visual observations, language instructions, and trajectory re
arXiv:2602.03619v2 Announce Type: replace Abstract: Nowadays, developing reliable DeepResearch-style long-form report generation remains challenging, as training and evaluation lack verifiable reward
arXiv:2606.00547v1 Announce Type: new Abstract: Interactive text-to-SQL agents solve database tasks through multi-turn interactions involving schema exploration, query execution, feedback interpretati
arXiv:2602.08689v2 Announce Type: replace Abstract: Diffusion models generate samples through an iterative denoising process guided by a pretrained neural network. Once the denoiser is fixed, the samp
arXiv:2603.26028v2 Announce Type: replace Abstract: Medical Visual Question Answering (MedVQA) models often exhibit limited generalization due to reliance on dataset-specific correlations, such as rec
arXiv:2606.02132v1 Announce Type: new Abstract: Agentic reinforcement learning can induce tool abuse, where models overuse external tools even for queries solvable by internal reasoning. Existing appr
arXiv:2606.02465v1 Announce Type: cross Abstract: Reasoning language models (RLMs) achieve strong performance on complex reasoning tasks, but still exhibit substantial multilingual reasoning gaps, lar
arXiv:2405.14782v3 Announce Type: replace Abstract: Reliable evaluation of language models (LMs) remains an open challenge. Re- searchers and engineers face methodological issues such as the sensitivi
arXiv:2602.23881v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to propose candidate t
arXiv:2603.09403v2 Announce Type: replace Abstract: Validating evaluation metrics for NLG typically relies on expensive and time-consuming human annotations, which predominantly exist only for English
arXiv:2606.00718v1 Announce Type: new Abstract: While Large Language Models (LLMs) have recently shown promise in Automated Heuristic Design (AHD), existing methods typically generate and evolve heuri
arXiv:2606.02438v1 Announce Type: new Abstract: Learned heuristics have recently become a competitive alternative to traditional domain-independent heuristics for satisficing planning. Existing approa
arXiv:2602.16953v3 Announce Type: replace Abstract: Execution-aware LLM agents offer a promising paradigm for learning from tool feedback, but such feedback can be expensive and slow to obtain, making
arXiv:2606.00031v1 Announce Type: cross Abstract: Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support ea
arXiv:2606.00684v1 Announce Type: cross Abstract: We address the problem of out-of-distribution (OOD) detection for target observations embedded in a subspace of the high dimensional data space. Using
arXiv:2606.01128v1 Announce Type: new Abstract: Communication overhead is a crucial bottleneck in scalable distributed learning. While existing methods aim to efficiently utilize data points, such as
arXiv:2606.00345v1 Announce Type: new Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings. However, predictive modeling
arXiv:2603.00171v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) are shifting towards 'Thinking with Images' by actively exploring image details. While effective, lar
arXiv:2606.00636v1 Announce Type: cross Abstract: This tech note describes the architecture and execution results of the LPDDR5X-PIM simulator, developed by Samsung Electronics. Based on the latest re
arXiv:2602.01053v2 Announce Type: replace Abstract: Role specialization in multi-LLM agent systems is often realized via multi-LoRA, where agents share a pretrained backbone and differ only by lightwe
arXiv:2312.03644v3 Announce Type: replace Abstract: Offline Multi-agent Reinforcement Learning (MARL) is valuable in scenarios where online interaction is impractical or risky. While independent learn
arXiv:2606.00033v1 Announce Type: cross Abstract: While mechanistic interpretability (MI) has produced important insights into neural network internals, the field has yet to establish a standardized s
arXiv:2606.00985v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown remarkable progress for mobile manipulation, but their performance on long-horizon tasks remains poor. Th
arXiv:2601.14230v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) are emerging as promising socio-collaborative companions for emotional and cognitive support. However, existing syst
arXiv:2606.02463v1 Announce Type: cross Abstract: In 3D environments, Embodied Agents answer spatially relevant questions through reasoning from a mixture of modalities including natural language, RGB
arXiv:2606.02288v1 Announce Type: new Abstract: Massive activation spikes in Large Language Models (LLMs) severely degrade quantization by stretching dynamic ranges. While prior hypotheses characteriz
arXiv:2408.01653v4 Announce Type: replace Abstract: Omnidirectional depth estimation presents a significant challenge due to the inherent distortions in panoramic images. Despite notable advancements,
arXiv:2606.00049v1 Announce Type: cross Abstract: Large language models (LLMs) are widely recognised for their applications in natural language generation and are increasingly used for code generation
arXiv:2601.23220v2 Announce Type: replace-cross Abstract: Despite recent Multimodal Large Language Models (MLLMs)' linguistic prowess in medical diagnosis, we find even state-of-the-art MLLMs suffer f
arXiv:2606.01028v1 Announce Type: new Abstract: Medical treatment recommendation poses several challenges to reinforcement learning (RL): patient physiology evolves in continuous time, measurements an
arXiv:2606.00610v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging extern
arXiv:2606.00619v1 Announce Type: cross Abstract: Long-horizon autonomous agents require memory systems to retain historical information, track evolving states, and reuse relevant knowledge beyond fin
arXiv:2606.01060v1 Announce Type: cross Abstract: Preference alignment has substantially improved the observable behavior of large language models, yet it remains unclear what alignment changes intern
arXiv:2606.00651v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures scale Large Language Models (LLMs) efficiently, enabling greater capacity with reduced computational cost by dy
arXiv:2606.00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems. However, their reliance on rig
arXiv:2512.17605v2 Announce Type: replace-cross Abstract: Robust mammography registration is essential for clinically relevant applications like tracking disease progression in breast tissue. However,
arXiv:2606.01891v1 Announce Type: cross Abstract: Mid-surface abstraction is essential for finite element analysis of thin-walled CAD models. Existing face pairing-based methods rely on handcrafted ge
arXiv:2606.01063v1 Announce Type: new Abstract: Theory of Mind (ToM) enables an agent to reason about another actor's beliefs, goals, and intentions, which is essential for human-centered embodied ass
arXiv:2606.00240v1 Announce Type: new Abstract: Effective real-world assistance requires AI agents with robust Theory of Mind (ToM): inferring human mental states from their behavior. Despite recent a
arXiv:2606.02363v1 Announce Type: new Abstract: We study sequential decision-making in partially observable environments against strategic, adaptive opponents, modeled as partially observable Markov g
arXiv:2606.01655v1 Announce Type: cross Abstract: The Bayesian paradigm offers principled tools for sequential decision-making under uncertainty, but its reliance on a probabilistic model for all para
arXiv:2606.02578v1 Announce Type: cross Abstract: Recent multimodal large language models have demonstrated strong reasoning ability, yet their reliability as automated evaluators remains limited by a
arXiv:2602.10623v2 Announce Type: replace-cross Abstract: Reward models learned from human preferences are central to aligning large language models (LLMs) via reinforcement learning from human feedba
arXiv:2606.01993v1 Announce Type: cross Abstract: Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks. However, such knowledge is often multimoda
arXiv:2606.01640v1 Announce Type: new Abstract: Human mobility generation aims to synthesize realistic trip chains for target populations based on individual features. Existing paradigms, including de
arXiv:2507.09029v5 Announce Type: replace-cross Abstract: Pre-training large neural networks at scale imposes heavy memory demands on accelerators and often requires costly communication. We introduce
arXiv:2606.02552v1 Announce Type: cross Abstract: Despite advances in depth estimation, flying points remain a persistent failure mode: near object boundaries, depth estimators often predict spurious
arXiv:2606.00304v1 Announce Type: new Abstract: Graph anomaly detection methods aim to distinguish anomalous nodes. While prior methods characterize anomalies through increased variation in the spectr
arXiv:2606.00844v1 Announce Type: cross Abstract: Bounding-box regression is a fundamental component of object detection, playing a critical role in precise object localization. Existing Intersection-
arXiv:2606.00708v1 Announce Type: new Abstract: Automated data science is a structured model-selection problem. A solution must choose data transformations, feature representations, architecture, trai
arXiv:2606.00107v1 Announce Type: cross Abstract: Electrocardiography (ECG) remains central to cardiovascular screening, yet interpretation remains largely manual and episodic. Clinical practice relie
arXiv:2606.01518v1 Announce Type: new Abstract: Motion generation for rigged shapes is vital for scalable 4D asset production. However, template-based methods are limited by specific topologies and fa