Knowledge catalogue
arXiv:2610.20157v1 Announce Type: new Abstract: World Action Models (WAMs) typically adapt video foundation models to robotic control through joint video and action prediction. However, task-level ins
arXiv:2610.19560v1 Announce Type: cross Abstract: In long-horizon tool-calling tasks, LLM agents can fail not only because of tool-use errors, but also because of ineffective interaction with users. Y
arXiv:2610.20336v1 Announce Type: new Abstract: Scaling embodied robot learning requires large and diverse manipulation datasets, but collecting real-world demonstrations remains expensive in human ef
arXiv:2610.19579v1 Announce Type: new Abstract: State-of-the-art visuomotor policies are increasingly capable of executing complex tasks, yet can fail during deployment in ways they cannot predict or
arXiv:2610.18168v1 Announce Type: new Abstract: Accurate prediction of object trajectories is fundamental to mechanical understanding and robotic control. However, autoregressive models inherently suf
arXiv:2610.19080v1 Announce Type: new Abstract: Reinforcement learning (RL) has substantially improved the reasoning of autoregressive and discrete-diffusion language models. Language models that gene
arXiv:2610.17027v1 Announce Type: new Abstract: Detecting credit card fraud is notoriously difficult because fraudulent charges make up less than 0.2% of total transactions. Quantum Machine Learning (
arXiv:2610.18606v1 Announce Type: new Abstract: Large language models increasingly tackle tasks that require multi-step reasoning, where errors in intermediate claims can affect the final answer. Self
arXiv:2610.18047v1 Announce Type: new Abstract: In this paper, our goal is to represent text embeddings as sketches for three main reasons. First, to visualize text representation and to explore their
arXiv:2610.19919v1 Announce Type: new Abstract: Accurate electronic band structures are essential for predicting material properties, but high-fidelity calculations are computationally demanding. We i
arXiv:2610.18938v1 Announce Type: cross Abstract: Most symbolic regression methods optimize candidate equations, but improving an equation's fit does not establish whether its underlying scientific co
arXiv:2610.20353v1 Announce Type: new Abstract: Mixture-of-experts (MoE) language models activate only a few experts per token, yet all expert weights must be stored. Mixed-precision quantization redu
arXiv:2610.19393v1 Announce Type: new Abstract: Whole-body control plays a crucial role in enabling humanoid robots to execute expressive motions. Faithful execution requires preserving both the inten
arXiv:2610.14877v1 Announce Type: new Abstract: Experience-based learning enables large language model (LLM) agents to improve over time by reusing knowledge from past executions. However, existing me
arXiv:2610.20319v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are increasingly deployed in safety-critical domains, yet their susceptibility to backdoor attacks remains a fo
arXiv:2610.19352v1 Announce Type: new Abstract: Clinical assessments are often collected using heterogeneous assessment systems across centers and time, leading to records that mix different but relat
arXiv:2610.18780v1 Announce Type: new Abstract: Large language model multi-agent systems extend individual agents' capabilities through collaboration across specialized roles, tools, and workflows. Ye
arXiv:2610.19578v1 Announce Type: cross Abstract: Graph Collaborative Filtering models have achieved remarkable success in recommendation systems. However, they inherently suffer from a catastrophic r
arXiv:2610.18787v1 Announce Type: cross Abstract: Existing malware detection approaches heavily rely on handcrafted features derived from static or dynamic analysis of the executable files. These meth
arXiv:2610.19911v1 Announce Type: cross Abstract: Adversarial robustness distillation typically treats a robust teacher as a fixed knowledge source and transfers robustness to a compact student in a o
arXiv:2610.18105v1 Announce Type: new Abstract: Chain of Thought (CoT) monitoring is used by frontier labs to detect misbehaviour of AI systems. Yet, its reliability remains poorly understood. We attr
arXiv:2610.16876v1 Announce Type: new Abstract: Target-aware multimodal instruction selection requires useful examples and an appropriate balance of supervision. Sample-level rankings can overrepresen
arXiv:2608.01805v2 Announce Type: replace Abstract: LLM multi-agent systems suffer from Correctness Collapse, where high task-level accuracy conceals severe process-level failures. This is especially
arXiv:2603.02022v3 Announce Type: replace-cross Abstract: Speech Bandwidth Extension improves clarity and intelligibility by restoring/inferring appropriate high-frequency content for low-bandwidth sp
arXiv:2610.19880v1 Announce Type: new Abstract: Unsupervised temporal action localization (UTAL) aims to identify action categories and localize their temporal boundaries without annotation supervisio
arXiv:2610.19141v1 Announce Type: new Abstract: Learning a collective policy for state-based Cartesian manipulation across diverse robotic embodiments presents a fundamental challenge due to embodimen
arXiv:2610.18095v1 Announce Type: new Abstract: AI companions provide users with emotional support and ongoing companionship, but their sustained relational interactions also introduce distinct safety
arXiv:2610.20326v1 Announce Type: new Abstract: Integrated Sensing and Communication (ISAC) systems rely on accurate Direction-of-Arrival (DoA) estimation for effective beamforming and spectral-effici
arXiv:2610.18583v1 Announce Type: new Abstract: Sampling-based test-time scaling enhances LLM reasoning by aggregating multiple reasoning paths for the same query. Existing methods use answer frequenc
arXiv:2607.10201v2 Announce Type: replace-cross Abstract: Equitable renewable-energy planning is a sequential decision problem, but the decision variables available to a public planner differ sharply
arXiv:2610.19957v1 Announce Type: new Abstract: World action models (WAMs) jointly predict future visual states and robot actions, but processing dense future-frame tokens incurs substantial inference
arXiv:2610.19778v1 Announce Type: cross Abstract: Computational compressive imaging, such as single-pixel imaging and lensless imaging, is a promising imaging technique, enabling the reconstruction of
arXiv:2610.17541v1 Announce Type: cross Abstract: Safety verification of a Markov decision process (MDP) asks whether the probabilities of reaching several failure states jointly lie in a convex safe
arXiv:2610.14136v2 Announce Type: new Abstract: Iterative sampling in diffusion models can lead to error accumulation due to exposure bias. The denoiser is trained on real images interpolated with noi
arXiv:2610.17740v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to discover scientific equations from experimental data. Such a discovery system has two parts that m
arXiv:2610.20596v1 Announce Type: new Abstract: Consumer smart devices observe the same human motion from different body locations, yet existing inertial-localization benchmarks rarely provide synchro
arXiv:2610.16050v1 Announce Type: new Abstract: Personality assessment from asynchronous video interviews (AVIs) plays a crucial role in recruitment by enabling standardized and behaviorally grounded
arXiv:2610.20272v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated multilingual capabilities across diverse language generation and understanding tasks. Yet effective code-
arXiv:2610.19831v1 Announce Type: new Abstract: Although multivariate time series forecasting has broad application prospects, effectively incorporating exogenous variables remains challenging. Prevai
arXiv:2610.14763v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can reproduce sensitive, copyrighted, or unsafe information from their training data. Effective unlearning must
arXiv:2610.17124v1 Announce Type: cross Abstract: Full-duplex spoken language models (FD-SLMs) support simultaneous listening and speaking, but often lag behind half-duplex spoken language models (HD-
arXiv:2610.19013v1 Announce Type: new Abstract: Current scaling law formulations suggest that increasing the number of unique samples in a dataset improves model performance, while repeated exposure t
arXiv:2506.06121v3 Announce Type: replace Abstract: Large-scale itinerary planning is a variant of the traveling salesman problem, aiming to determine an optimal path that maximizes the collected poin
arXiv:2610.19053v1 Announce Type: new Abstract: Inference of phylogenetic trees from sequences plays a central role in understanding evolutionary relationships, with applications ranging from tracking
arXiv:2606.17029v2 Announce Type: replace Abstract: Deep research agents synthesize long-form reports by searching and reasoning over retrieved evidence. Reinforcement learning with rubric-based rewar
arXiv:2610.17197v1 Announce Type: cross Abstract: In the age of 'Software 3.0', when a complete program can be rewritten in minutes by an agent, the traditional approach of relying on code as the sole
arXiv:2610.14812v1 Announce Type: cross Abstract: Three-dimensional (3D) velocity model building (VMB) remains computationally challenging with conventional approaches and native 3D neural networks, l
arXiv:2610.17831v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for
arXiv:2610.19091v1 Announce Type: cross Abstract: Contact-rich inverse problems require efficient gradients and reliable enforcement of non-penetration. Existing differentiable methods provide fast si
arXiv:2610.18990v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has made diffusion model personalization affordable and widely shareable, but it also creates moderation risks when harmful
arXiv:2610.19743v1 Announce Type: new Abstract: Discriminative models are trained with task-specific objectives such as classification, detection, or representation learning, yet their practical utili
arXiv:2603.00578v2 Announce Type: replace-cross Abstract: Long chain-of-thought~(CoT) reasoning improves the problem-solving capability of large reasoning models~(LRMs), but often incurs substantial i
arXiv:2610.20124v1 Announce Type: new Abstract: Human mesh recovery (HMR) provides a geometric foundation for understanding human motion, but accurately reconstructing both body motion and fine hand a
arXiv:2606.15251v2 Announce Type: replace-cross Abstract: Accurate and interpretable motion prediction for heterogeneous traffic spaces, including pedestrians, bicycles, cars, and trucks, is essential
arXiv:2610.19302v1 Announce Type: new Abstract: On-policy distillation (OPD) provides dense token-level supervision on student rollouts, but this supervision can become unreliable when the student and
arXiv:2610.18635v1 Announce Type: cross Abstract: The development of a digital twin brings together many stakeholders and technical components. We have previously investigated the reporting of the con
arXiv:2610.17738v1 Announce Type: new Abstract: End-to-end (E2E) full-duplex spoken language models (FD-SLMs) enable simutanous listening and speaking continously, requiring models to take appropriate
arXiv:2603.13535v5 Announce Type: cross Abstract: Evaluating Ollivier-Ricci (OR) curvature on large-scale graphs is computationally prohibitive due to the necessity of solving an optimal transport pro
arXiv:2610.19804v1 Announce Type: new Abstract: We study finite stochastic partial monitoring (PM) games with side information, a paradigm of sequential learning with partial feedback. On each round t
arXiv:2610.17767v1 Announce Type: cross Abstract: Large reasoning models (LRMs) achieve strong performance by externalizing explicit reasoning traces before producing the answer, yet suffer from overt