Search: “routing”

arXiv:2610.06274v1 Announce Type: new Abstract: Many agent runtimes give the agent a tool for editing its own configuration. Some of that configuration grants abilities, such as enabling a tool. Other

arXiv:2610.03289v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance across vision-language tasks, yet the internal mechanisms by which visual and textua

arXiv:2610.02431v1 Announce Type: new Abstract: Deep neural networks achieve high accuracy through layered numerical transformations, yet their decisions remain difficult to audit because decision evi

arXiv:2610.03314v1 Announce Type: cross Abstract: Flow- and diffusion-based generative models have recently emerged as flexible and highly efficient forecasting models for dynamical systems. When comb

arXiv:2610.02267v1 Announce Type: new Abstract: Agent harnesses make many small, typed decisions per task: which model to call, which tool to use, whether retrieved text is relevant, whether an input

arXiv:2610.03709v1 Announce Type: cross Abstract: We study the minimization of sums of smooth strongly convex functions over undirected graphs, with each function held by one agent and communication r

arXiv:2610.02293v1 Announce Type: new Abstract: HakemBench is a Turkish benchmark of typed decisions, in which the model under test reads a text, a question and a fixed set of options and returns a pr

arXiv:2610.02224v1 Announce Type: new Abstract: Rapid and reliable identification of pharmaceutical residues is important for safeguarding public health, ensuring food safety, and enabling practical R

arXiv:2610.03414v1 Announce Type: cross Abstract: Consistency models (CMs) have become a leading approach for generating high-quality samples in few steps. However, adding steps can improve or degrade

arXiv:2610.02300v1 Announce Type: new Abstract: Training-free safeguards for text-to-image generation often rely on a reusable safety signal, such as an unsafe direction or global toxic subspace, appl

arXiv:2610.03161v1 Announce Type: cross Abstract: Feature selection for imbalanced classification tasks such as credit card fraud and consumer default detection requires balancing predictive relevance

arXiv:2610.02381v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on the responses it generates. Existing LLM multi-teacher OPD transfers what specialists predict through t

arXiv:2610.03454v1 Announce Type: cross Abstract: Recent progress in multimodal, high-dimensional learning has enabled foundation models to process heterogeneous, large-scale data. However, at test ti

arXiv:2610.03316v1 Announce Type: new Abstract: Designing effective heuristics for diverse combinatorial optimization problems requires substantial expertise and repeated search. Large language models

arXiv:2610.02388v1 Announce Type: new Abstract: Existing 3D large language models (LLMs) compromise on two fronts: they compress shapes into latent codebook indices or coordinate text, which removes s

arXiv:2610.02717v1 Announce Type: new Abstract: A key challenge in bringing embodied intelligence into the real world is transferring capabilities from simulation to reality and enabling agents to con

arXiv:2610.02784v1 Announce Type: new Abstract: Tactile sensing provides essential contact information for robotic manipulation, yet incorporating it into pretrained vision-language-action (VLA) model

arXiv:2610.03195v1 Announce Type: new Abstract: As LLM agents decide on users' behalf which product to buy, which hotel to book, or which paper to cite, a preference for items from certain sources (th

arXiv:2610.03265v1 Announce Type: cross Abstract: Large-scale pre-training has improved the generalization of neural operators across diverse PDEs. However, existing PDE foundation models still strugg

arXiv:2610.02819v1 Announce Type: new Abstract: Improving joint audio-visual reasoning in Omni Large Language Models typically incurs substantial data construction and training costs. Our diagnostics

arXiv:2610.02417v1 Announce Type: new Abstract: Advances in experimental instrumentation and automation generate increasingly rich datasets, but turning experimental observations into microscopic unde

arXiv:2610.03036v1 Announce Type: cross Abstract: We present WebFovea, a vision-based web agent that placed 2nd in the WebRetriever Challenge 2026 with a final score of 57.0 out of 100. The challenge

arXiv:2610.03713v1 Announce Type: cross Abstract: Continual learning treats degradation on previously seen data as evidence of failure, a convention inherited from settings with a stationary predictio

arXiv:2610.03465v1 Announce Type: cross Abstract: K-fold cross-validation (CV) is widely used as evidence of out-of-sample performance, although folds are neither independent experiments nor equally i

arXiv:2610.02636v1 Announce Type: cross Abstract: Quantum Fourier sampling may help audit the spectral learnability of delay-based physical unclonable functions (PUFs). We ask whether that promise sur

arXiv:2610.02508v1 Announce Type: new Abstract: World action models (WAMs) have emerged as a promising paradigm for robotic control by jointly predicting future visual dynamics and actions from an ini

arXiv:2610.01618v1 Announce Type: new Abstract: Agent evaluations increasingly go beyond a single success rate, reporting metrics such as cost, consistency, and robustness. Yet they typically treat th

arXiv:2610.01397v1 Announce Type: new Abstract: Dynamic humanoid motions such as flips risk hardware damage due to suboptimal policies, disturbances or sim-to-real gaps. A motion tracking policy offer

arXiv:2610.01124v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models are often pretrained with a fixed channel vocabulary or a limited set of montages, making transfer diffic

arXiv:2610.01921v1 Announce Type: cross Abstract: Cross-lingual contrastive learning has been a core component of multilingual encoder training, but the ability to explicitly align representations is

arXiv:2610.00208v1 Announce Type: cross Abstract: This paper studies decentralized safe path following for multiple quadrotors on intersecting paths, where safety requires both collision avoidance and

arXiv:2610.01751v1 Announce Type: new Abstract: Adaptive model search is path dependent: once a challenger is adopted, it becomes the reference from which later candidates are generated. A statistical

arXiv:2610.00383v1 Announce Type: new Abstract: Modern text-to-image (T2I) systems can be improved without modifying generator parameters by adapting the external system around frozen generators. Howe

arXiv:2511.06311v2 Announce Type: replace Abstract: We present a tactile sensing method enabled by the mechanical compliance of soft robots; an externally attachable photoreflective module reads surfa

arXiv:2610.01882v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) provides a powerful framework for learning coordinated behaviors through interactions with the environment.

arXiv:2610.00053v1 Announce Type: new Abstract: Four-bit floating-point (FP4) Tensor Cores accelerate matrix multiplication, but scale computation, operand packing, layout construction, and saved back

arXiv:2610.00978v1 Announce Type: cross Abstract: Time-series anomaly detection (TSAD) is difficult to generalize across datasets because heterogeneous temporal dynamics imply different notions of nor

arXiv:2610.02110v1 Announce Type: new Abstract: Transparent and specular surfaces pose a serious challenge to LiDAR-based SLAM and navigation because laser returns may pass through glass, leaving coll

arXiv:2610.01223v1 Announce Type: cross Abstract: Time-series anomaly detection trades off predictive accuracy, computational efficiency, and interpretability. We use a large language model not as the

arXiv:2610.01230v1 Announce Type: new Abstract: Efficient long-context inference is essential for large language models (LLMs), yet it poses a severe computational bottleneck. Hash-based retrieval off

arXiv:2610.02074v1 Announce Type: new Abstract: Privacy-preserving machine learning presents significant deployment challenges on the cloud for intelligent systems with confidential data. Fully Homomo

arXiv:2610.01231v1 Announce Type: cross Abstract: Generative artificial intelligence has reduced the cost of producing plausible symbolic artefacts, leading recent organisation scholarship to identify

arXiv:2610.01373v1 Announce Type: new Abstract: World models allow agents to plan in latent space by choosing a sequence of actions that most reduces the distance to a given goal state. Thus, planning

arXiv:2610.01236v1 Announce Type: new Abstract: Collaboration between a small language model (SLM) and a large language model (LLM) offers an opportunity to combine the efficiency of smaller models wi

arXiv:2610.01426v1 Announce Type: new Abstract: Prescribing the speed of gradient flow on the risk itself, by the dynamics ot w=-u(E(w))nabla E(w)/abs{nabla E(w)}^{2}, makes the risk e(t)=E(w(t)) obey

arXiv:2610.02126v1 Announce Type: cross Abstract: We explore catastrophic forgetting in the context of large pre-trained models. By considering forgetting as a geometric problem in the input space of