Knowledge catalogue
arXiv:2610.05087v1 Announce Type: new Abstract: Contrastive Reinforcement Learning (CRL) learns representations that estimate goal reachability, yet its policy remains conditioned on raw goals and the
arXiv:2503.23835v2 Announce Type: replace Abstract: Grasp-based manipulation tasks are fundamental to robots interacting with their environments, yet gripper state ambiguity significantly reduces the
arXiv:2610.05950v1 Announce Type: new Abstract: LLM-driven evolution enables iterative model development, but two practical goals remain underexplored: finding model designs that transfer across relat
arXiv:2610.05071v1 Announce Type: new Abstract: Learning population dynamics from unpaired temporal marginals is an ill-posed inverse problem that requires structural assumptions on the underlying dyn
arXiv:2610.05572v1 Announce Type: new Abstract: Predicting whether an LLM agent will fail has emerged as a promising direction for supporting intervention during execution. Recent approaches report st
arXiv:2603.04359v2 Announce Type: replace Abstract: Quantization can drastically increase the efficiency of large language and vision models, but typically incurs an accuracy drop. Recently, function-
arXiv:2610.04775v1 Announce Type: new Abstract: In this paper, we present a distributed cascade force control system (DCFC) for multiple robots with the aim of pushing a rigid object towards a desired
arXiv:2610.05378v1 Announce Type: new Abstract: Collaborative learning typically exchanges model parameters: federated clients communicate updates, while independently adapted foundation models are co
arXiv:2610.03833v1 Announce Type: cross Abstract: A novel discovery is one which is both useful and surprising: a generative model's output is a useful discovery if it has a low probability of being g
arXiv:2505.19058v2 Announce Type: replace Abstract: We propose a novel distributionally robust Q-learning algorithm for the non-tabular case accounting for continuous state spaces where the state tran
arXiv:2610.04933v1 Announce Type: cross Abstract: Scaling robot data and model capacity has improved Vision-Language-Action (VLA) policies, but further progress is constrained by the high cost of robo
arXiv:2601.23027v2 Announce Type: replace Abstract: Long chain-of-thought reasoning (Long CoT) is now fundamental to state-of-the-art LLMs, especially in mathematical reasoning. However, LLM generatio
arXiv:2610.04764v1 Announce Type: new Abstract: This letter proposes DNPush, a Distributed Nonparallel Pushing force controller for cooperative object transport without inter-robot communication. Each
arXiv:2610.04792v1 Announce Type: cross Abstract: Missing modality remains a longstanding challenge in multimodal learning. Existing methods typically address this issue through modality recovery or a
arXiv:2610.03765v1 Announce Type: new Abstract: Discrete motion tokenizers encode motion as atomic units and are widely used for co-speech gesture generation. It remains unclear which motion propertie
arXiv:2610.05297v1 Announce Type: new Abstract: We ask whether a neural network can learn a structure-preserving map from a compressed latent space to a Hilbert-space representation. Using an 8-dimens
arXiv:2610.04278v1 Announce Type: new Abstract: Deep remaining-useful-life (RUL) models on NASA C-MAPSS are now routine, and so are heatmaps that colour sensors and timesteps. A heatmap that looks mec
arXiv:2610.06204v1 Announce Type: cross Abstract: LLM agents are starting to own the full customer experience. Soon, LLMs may be selling and buying on behalf of companies and customers respectively. S
arXiv:2610.06078v1 Announce Type: new Abstract: This paper asks whether VLA generalization is grounded in a global understanding of objects' physical properties that enables policies to adapt their mo
arXiv:2610.05379v1 Announce Type: new Abstract: Model performance monitoring is a standard practice in machine learning deployments. Detection performance is tracked continuously, and model decay is e
arXiv:2610.04636v1 Announce Type: cross Abstract: Increasing architectural complexity is often assumed to improve health misinformation detection, yet reported gains are difficult to interpret when st
arXiv:2610.05541v1 Announce Type: new Abstract: Mechanistic interpretability has emerged as the primary means to understand safety behavior of LLMs. However, existing tools primarily focus on the acti
arXiv:2610.05615v1 Announce Type: cross Abstract: Multi-agent debate (MAD) has emerged as an effective paradigm to improve the reasoning capabilities of large language models (LLMs) and is increasingl
arXiv:2610.04540v1 Announce Type: new Abstract: We introduce DreamFormer, a model-based agent that acquires language-conditioned, multi-task skills by imitating expert demonstrations within the latent
arXiv:2610.04494v1 Announce Type: cross Abstract: Testing deep reinforcement learning (DRL) agents in cyber-physical systems aims to uncover diverse failures before deployment, but each execution can
arXiv:2610.04819v1 Announce Type: new Abstract: One-step real-world image super-resolution (Real-ISR) offers efficient inference, but recovering realistic and perceptually rich details often relies on
arXiv:2610.05839v1 Announce Type: cross Abstract: Robotic ultrasound couples a high-rate contact-force loop with slower, delayed image feedback, so image-guided ultrasound probe rotation can perturb c
arXiv:2610.04138v1 Announce Type: new Abstract: Account takeover (ATO) fraud is a growing threat to digital banking, requiring effective detection while minimizing friction for legitimate customers. P
arXiv:2610.06327v1 Announce Type: cross Abstract: Vision-based autonomous navigation for low-cost robots remains a fundamental challenge, primarily due to the significant gap between simulated trainin
arXiv:2610.04087v1 Announce Type: new Abstract: Agentic workflows are increasingly used across domains such as technology, finance, and enterprise operations. As these agents become more widely deploy
arXiv:2610.04489v1 Announce Type: cross Abstract: Understanding parameter functions helps elucidate the internal mechanisms of large language models (LLMs). However, how to connect parameters from dif
arXiv:2610.05529v1 Announce Type: new Abstract: We present DynaMesh, a dynamic texture generation method for 3D meshes. Given a textureless shape and a text prompt describing an effect, our method pro
arXiv:2610.04137v1 Announce Type: new Abstract: Agent harnesses specify the roles, instructions, tools, and communication structure used to solve a task, and the right harness depends on the query. Be
arXiv:2610.06329v1 Announce Type: new Abstract: Modern LLM training increasingly relies on heterogeneous data sources spanning different domains, tasks, preference distributions, and difficulty levels
arXiv:2610.04044v1 Announce Type: new Abstract: The quadratic attention cost of Vision Transformers (ViTs) forces a trade-off between spatial resolution and rollout horizon, particularly for fine-scal
arXiv:2610.03942v1 Announce Type: new Abstract: When using diffusion models to target image restoration problems, diffusion inversion is typically employed to retain relevant image information from th
arXiv:2610.05796v1 Announce Type: new Abstract: Optimisation-based parking planners usually impose collision constraints only at the time samples, so a vehicle corner can cut an obstacle between sampl
arXiv:2603.07904v3 Announce Type: replace Abstract: Vision-language-action (VLA) models achieve favorable task performance, yet runtime errors in closed-loop execution evolve with alternating updates
arXiv:2610.05048v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) provides dense token-level supervision without a second model: one network acts as teacher with the reference solutio
arXiv:2610.04554v1 Announce Type: new Abstract: Recovering detailed geometry from high-resolution images is critical for precise perception of the surroundings and objects. However, existing methods w
arXiv:2610.04758v1 Announce Type: new Abstract: Machine-learning classification is now used across medicine, the social sciences, economics, education and engineering, very often by researchers who do
arXiv:2610.05603v1 Announce Type: new Abstract: Echocardiography is the most widely used cardiac imaging modality, yet interpretation demands integrating visual evidence across global anatomy, localiz
arXiv:2610.04953v1 Announce Type: new Abstract: Euclidean combinatorial optimization problems (ECOPs), such as the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP), poss
arXiv:2507.20335v2 Announce Type: replace Abstract: Educational language models should pursue three complementary objectives: helpfulness through responsible guidance, personalization to learner needs
arXiv:2508.14086v4 Announce Type: replace Abstract: Electroencephalography (EEG) is a critical tool for monitoring brain activity and diagnosing neurological disorders such as epilepsy. However, learn
arXiv:2610.03736v1 Announce Type: cross Abstract: Probabilistic downscaling must represent kilometer-scale structure left unresolved by a deterministic regional prediction. We introduce an analog-init
arXiv:2610.05397v1 Announce Type: new Abstract: Generative modeling of graph-structured data is crucial for tasks ranging from drug discovery to social network simulation. Among these models, denoisin
arXiv:2408.16803v2 Announce Type: replace Abstract: Transformers have gained widespread acclaim for their versatility in handling diverse data structures, yet their application to log data remains und
arXiv:2610.06420v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative training of models across institutions without centralizing sensitive data, making it well-suited for priv
arXiv:2610.06147v1 Announce Type: new Abstract: Vision-language models (VLMs) achieve strong zero-shot transferability but remain vulnerable to target-domain shifts at inference time. Test-time adapta
arXiv:2511.19935v3 Announce Type: replace Abstract: Deploying domain-specialized large language models on resource-constrained hardware motivates reducing both adaptation cost and the number of retain
arXiv:2610.04506v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly evaluated for egocentric and cross-view video reasoning, yet existing benchmarks largely focus on semanti
arXiv:2607.09701v2 Announce Type: replace Abstract: The enduring vision of general-purpose robots serving humanity hinges fundamentally on policy steerability. However, prevailing paradigms of learnin
arXiv:2610.05013v1 Announce Type: new Abstract: Lifelong physical agents must reason over extended interactions where past events continue to shape the world long after they disappear from view. Beyon
arXiv:2610.05607v1 Announce Type: new Abstract: Emergency obstacle avoidance requires a mobile robot to brake or change its heading within the available distance. This paper investigates the dependenc
arXiv:2610.04726v1 Announce Type: new Abstract: Full actuation is commonly valued for independent position and attitude tracking. This work establishes a complementary principle: when only a position
arXiv:2610.06450v1 Announce Type: new Abstract: Foundation models (FMs) are increasingly being developed for general time series and physiological signals, yet their transferability to downstream phys
arXiv:2610.05235v1 Announce Type: new Abstract: Surface electromyography (sEMG) is a low-power, cost-effective biosignal for hand-pose estimation and gesture classification. In this work, we examine w
arXiv:2610.06545v1 Announce Type: new Abstract: We present Empirical Variational Autoencoder, a general generative framework for continuous-valued (i.e., non-vector-quantized) sequences. EVA is based
arXiv:2610.06235v1 Announce Type: cross Abstract: Instruction following is central to language-conditioned robot policies: language should determine what to do when the same scene permits multiple val