Knowledge catalogue
arXiv:2610.14246v1 Announce Type: new Abstract: Variance-based methods recover a Gaussian linear structural equation model (SEM) with equal or suitably constrained error variances, either by recursive
arXiv:2610.16419v1 Announce Type: cross Abstract: Classifier-Free Guidance (CFG) with guidance scale gamma is commonly motivated by the omega-powered conditional distribution obtained by setting omega
arXiv:2610.16884v1 Announce Type: new Abstract: Continuous symmetry discovery is typically formulated as a separate estimation problem for each function or dataset. We instead investigate whether the
arXiv:2610.18456v1 Announce Type: new Abstract: AI-based cellular perturbation prediction is essential to accelerate drug discovery and therapeutic identification. However, perturbation-specific signa
arXiv:2610.16522v1 Announce Type: cross Abstract: Safe robot navigation in unknown, dynamic environments requires more than geometric collision checking: a robot still far from an obstacle may be unab
arXiv:2610.17750v1 Announce Type: new Abstract: Robotic assistance for human mobility could benefit care recipients and reduce caregiver burden during physically demanding tasks such as Sit-to-Stand (
arXiv:2510.27114v2 Announce Type: replace-cross Abstract: Generative BC policies model expert action distributions well, but they do not explicitly reason about what scene a candidate action would pro
arXiv:2610.15486v1 Announce Type: new Abstract: Robotic systems for lunar surface operations must act autonomously and preserve their own integrity in an environment where failures are unrecoverable a
arXiv:2603.01786v2 Announce Type: replace Abstract: In this paper, we present a novel learning framework for finding shortest paths in graphs utilizing Generative Flow Networks (GFlowNets). First, we
arXiv:2610.18725v1 Announce Type: new Abstract: A transformer trained on sequences whose source parameters are drawn afresh each time can learn only what the sequences share: the source family and its
arXiv:2610.16299v1 Announce Type: new Abstract: Video anomaly understanding (VAU) aims to generate language-level interpretations of anomalous events in long videos, requiring visual evidence that sup
arXiv:2606.12475v2 Announce Type: replace Abstract: Human-robot collaboration (HRC) combines the complementary strengths of humans and robots to improve task efficiency. However, many existing collabo
arXiv:2610.16948v1 Announce Type: new Abstract: Convex relaxations make nonconvex optimization tractable by replacing hard nonconvex problems with convex surrogates that give valid bounds. Yet their d
arXiv:2610.16966v1 Announce Type: new Abstract: Symbolic regression (SR) seeks explicit equations that explain observed data, and recent methods increasingly employ large language models (LLMs) and ag
arXiv:2610.17399v1 Announce Type: new Abstract: Continuous monitoring of generative AI systems requires detecting distribution shifts in the unstructured data they ingest and generate. Existing monito
arXiv:2610.14456v1 Announce Type: new Abstract: Looped transformers reuse a single block across iterations, raising a natural question: how far can a block trained at one depth be safely extrapolated
arXiv:2610.14302v1 Announce Type: new Abstract: Humans and animals do not learn vision from a fixed set of data: they move, interact, and learn from the visual consequences of their actions. Motivated
arXiv:2610.14268v1 Announce Type: cross Abstract: The scalability and performance of edge federated learning are fundamentally constrained by synchronization bottlenecks. Clients operate under heterog
arXiv:2610.14351v1 Announce Type: new Abstract: World-action models inherit broad visual and physical priors from video pre-training. However, standard within-video next-frame prediction allows them t
arXiv:2610.18730v1 Announce Type: new Abstract: Manufacturers often need to characterize a new material, process, or product before enough operating data are available to train reliable predictive mod
arXiv:2610.15267v1 Announce Type: new Abstract: Joint-embedding predictive architectures (JEPAs) learn world models directly in latent space, but unconstrained autoregressive predictors may contract t
arXiv:2610.18072v1 Announce Type: new Abstract: We introduce LensErase, a general-purpose mask-based Erase Anything foundation model designed to cover a broad spectrum of real-world erase scenarios. L
arXiv:2610.15362v1 Announce Type: new Abstract: Object Navigation in unseen environments remains a fundamental challenge in embodied AI, as it requires agents to generalize visual and spatial reasonin
arXiv:2610.15612v1 Announce Type: new Abstract: Accurate lesion segmentation in mammography is important for computer-aided breast cancer diagnosis, but dense pixel-level annotations remain costly and
arXiv:2601.12243v3 Announce Type: replace Abstract: Video summarization transforms long videos into concise representations that are easier to document and analyze, especially in high-stakes domains s
arXiv:2603.03172v2 Announce Type: replace Abstract: Certified machine unlearning aims to provably remove the influence of a deletion set U from a model trained on a dataset S, by producing an unlearne
arXiv:2610.18352v1 Announce Type: new Abstract: A robot following a multi-step instruction needs to act on the right objects in the right order, even as the scene changes. It must also maintain awaren
arXiv:2610.14889v1 Announce Type: new Abstract: Bilateral teleoperation with force feedback is becoming increasingly important, and the quality of external-torque estimation is critical to this proces
arXiv:2610.15171v1 Announce Type: new Abstract: Canonicalization resolves the rotational and permutation ambiguity of 3D point clouds by selecting a unique orbit representative, maintaining their geom
arXiv:2610.18843v1 Announce Type: new Abstract: Robot policies are expected to generalize across deployment environments, including variations in the robot itself: cameras are bumped and remounted, ca
arXiv:2610.17232v1 Announce Type: new Abstract: Navigating humanoid robots through dynamic, non-convex environments requires tightly coupling onboard perception with footstep planning and dynamic loco
arXiv:2509.13334v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning improves LLM accuracy on complex tasks, but CoTs are often unfaithful; that is, the model does not rely on th
arXiv:2610.18166v1 Announce Type: cross Abstract: Randomly initialised neural networks of width m converge to Gaussian processes as m o infty, with two types of quantitative bounds characterising this
arXiv:2610.18482v1 Announce Type: cross Abstract: Quantum computing promises new capabilities beyond classical computation. Mid-circuit measurements (MCMs) enable error correction and outcome-dependen
arXiv:2610.18243v1 Announce Type: new Abstract: Given the growing global burden of liver tumors and diffuse liver diseases, non-contrast computed tomography (NCCT) is increasingly used for opportunist
arXiv:2610.14726v1 Announce Type: new Abstract: Latent diffusion models enable high-quality video generation, but video VAE encoding and decoding remain computationally expensive. Existing methods use
arXiv:2610.15899v1 Announce Type: cross Abstract: Large language model serving is increasingly limited not only by model size and GPU speed, but also by scheduling during autoregressive decoding, wher
arXiv:2610.16030v1 Announce Type: cross Abstract: Expectiles are tail-sensitive distributional summaries with important applications in financial risk measurement and forecasting, yet statistical theo
arXiv:2610.18560v1 Announce Type: cross Abstract: We prove that the last iterate of Optimistic Follow-The-Regularized-Leader (OFTRL) converges at a linear rate to a Nash equilibrium in multiplayer har
arXiv:2610.14703v1 Announce Type: new Abstract: On-device inference imposes constraints on model storage and operand traffic and thus benefits from compressed operand representations, which mandate th
arXiv:2610.18195v1 Announce Type: cross Abstract: For sensing-aided communications, single-endpoint sensing offers only a partial view of propagation, motivating link-centric sensing across both endpo
arXiv:2610.17914v1 Announce Type: new Abstract: Post-hoc linearization is an attractive way to accelerate pretrained Video Diffusion Transformers (Video DiTs), yet existing methods largely treat the q
arXiv:2610.16313v1 Announce Type: new Abstract: Vision-language-action (VLA) models have advanced robotic manipulation by connecting visual perception and language instructions to action generation. H
arXiv:2610.15379v1 Announce Type: cross Abstract: Speaker anonymization (SA) aims to conceal speaker identity while preserving linguistic content and emotion, but existing streaming systems often lose
arXiv:2610.18572v1 Announce Type: cross Abstract: Automated post-training transforms pre-trained large language models (LLMs) into models capable of handling diverse domains by automatically construct
arXiv:2610.14612v1 Announce Type: new Abstract: Chiplet technology is a key approach for extending Moore's Law. However, its design process is constrained by two primary challenges: the scarcity of ea
arXiv:2610.14550v1 Announce Type: cross Abstract: Dynamic Product Advertising (DPA) retrieval systems are a core component of modern e-commerce advertising; however, their evaluation remains challengi
arXiv:2610.17018v1 Announce Type: new Abstract: Standard procedure in machine learning is to randomly split train and test data, emulating IID draws from one underlying data distribution. In practice,
arXiv:2610.16579v1 Announce Type: cross Abstract: Code-switching (CS) speech, characterized by language alternation within a single utterance, is prevalent in multilingual communities and poses challe
arXiv:2606.15610v2 Announce Type: replace-cross Abstract: Passing an exact-clone null gate does not entail passing a matched target-selection utility gate. We call a non-tie preference between exact d
arXiv:2610.17246v1 Announce Type: new Abstract: Generative discovery in science requires coupling high-level scientific reasoning with the ability to generate samples respecting the structure of a des
arXiv:2610.15840v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language model (LLM) agents remains challenging in long-horizon tasks with sparse outcome rewards, where assigning
arXiv:2610.18604v1 Announce Type: new Abstract: 3D part assembly aims to reconstruct a complete shape from a set of unassembled parts by predicting a rigid SE(3) transformation for each part. Because
arXiv:2610.14596v1 Announce Type: new Abstract: Navigating cluttered environments while carrying objects remains a challenging problem for quadruped robots. Compared with works that focused on load-aw
arXiv:2610.17099v1 Announce Type: cross Abstract: Local learning reduces cross-layer training dependencies, but an efficient accelerator must still retain operands, accumulate gradients, and select qu
arXiv:2610.17930v1 Announce Type: new Abstract: Unknown payloads alter robot dynamics and make precise contact execution difficult when foothold margins are small. We present LOAD, a perceptive locomo
arXiv:2606.15896v2 Announce Type: replace-cross Abstract: Learning-based quadrupedal locomotion typically relies on complex reward formulations that entangle task specification, operational limits, ga
arXiv:2610.18289v1 Announce Type: new Abstract: Autoregressive video diffusion models have emerged as a foundation for long-horizon world rollout. However, when extended far beyond their training wind
arXiv:2610.17494v1 Announce Type: new Abstract: Learning management system traces are clustered to identify participation profiles, but unequal observation windows may cause profiles to reflect durati
arXiv:2610.16224v1 Announce Type: new Abstract: Text-to-image models have made remarkable progress in visual fidelity and instruction following, yet complex compositional generation remains challengin