Knowledge catalogue
arXiv:2609.27869v1 Announce Type: new Abstract: Long-horizon multimodal agents rely on specialized capabilities for perception, retrieval, reasoning, verification, and execution. Existing designs typi
arXiv:2609.27925v1 Announce Type: new Abstract: Language models can over-condition on irrelevant preceding text: predictions already supported by local context may still change when distant, unrelated
arXiv:2609.27473v1 Announce Type: new Abstract: PPG-to-vital-sign reconstruction turns a wrist-worn photoplethysmogram into clinical waveforms such as the ECG. Long-horizon multivariate time-series fo
arXiv:2609.27009v1 Announce Type: new Abstract: Large language models are increasingly applied to high-risk domains such as law, yet complex legal reasoning remains limited by two structural challenge
arXiv:2602.17818v2 Announce Type: replace Abstract: Speech enhancement performance degrades significantly in noisy environments, limiting the deployment of speech-controlled technologies in industrial
arXiv:2609.27747v1 Announce Type: cross Abstract: Vision-language-action models (VLA) promise human-steerable autonomous driving, but their training is bottlenecked by the scarcity of frames paired wi
arXiv:2609.26981v1 Announce Type: new Abstract: We describe deploying imaging AI at six hospitals through PACS-AI, an open self-hosted platform. The binding constraint is not model accuracy but infras
arXiv:2609.27032v1 Announce Type: new Abstract: Faithfulness is a central concern in legal text summarization, which motivates extractive approaches that select verbatim content traceable to its sourc
arXiv:2609.28078v1 Announce Type: new Abstract: Segmentation-based multi-object tracking (MOT) with foundation video models such as SAM2 offers strong localization quality, yet remains fragile in crow
arXiv:2609.28327v1 Announce Type: new Abstract: We present LightMIS, a scalable family of ultra-lightweight convolutional networks for 2D binary medical image segmentation without a learned stage-wise
arXiv:2609.28431v1 Announce Type: new Abstract: Dexterous manipulation demands long-term foresight and rapid reactive control. Vision-Language-Action (VLA) models, while proficient in high-level reaso
arXiv:2609.27741v1 Announce Type: new Abstract: In value-based reinforcement learning, improving the accuracy of policy evaluation has been shown to improve downstream policy optimization performance.
arXiv:2609.27964v1 Announce Type: new Abstract: Empirical scaling laws for autoregressive language models relate prediction loss to model size, data size, and optimization compute, but their theoretic
arXiv:2609.27246v1 Announce Type: cross Abstract: As embodied agents take on increasingly social and relational roles in VR, visual realism and embodiment alone may be insufficient; users must also pe
arXiv:2609.27303v1 Announce Type: new Abstract: Livestreams are long-lasting interactive environments where audiovisual content, viewer activity, host behavior, and platform signals evolve together, c
arXiv:2402.17937v2 Announce Type: replace Abstract: Mental health is a significant healthcare challenge, and cognitive behavioral therapy (CBT) is a widely used therapeutic method for treating anxiety
arXiv:2609.27092v1 Announce Type: cross Abstract: Many node-classification GNNs apply a linear classifier to a nonnegative mixture of local messages. An error can reflect either poor mixture weights o
arXiv:2609.28338v1 Announce Type: cross Abstract: Local geometric mixing localizes geometric mixing by requiring geometric convergence to equilibrium in total variation only over finitely many transit
arXiv:2609.27959v1 Announce Type: cross Abstract: We investigate a local spectral-complexity representation for perceptual image quality assessment (IQA) based on Shannon entropy of singular values co
arXiv:2505.04417v3 Announce Type: replace Abstract: Diffusion models are state-of-the-art tools for various generative tasks. Yet training these models involves estimating high-dimensional score funct
arXiv:2609.27220v1 Announce Type: new Abstract: Diffusion language models generate text through iterative denoising, exposing intermediate trajectories before final answers are produced. We identify a
arXiv:2609.28212v1 Announce Type: cross Abstract: Language modeling using Transformers has become commonplace despite their fixed computational depth and quadratic runtime with respect to input tokens
arXiv:2603.16932v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) typically process images at a native high-resolution, forcing a trade-off between accuracy and computational eff
arXiv:2609.27024v1 Announce Type: cross Abstract: Volatility forecasts play a central role in financial risk management because their overall level and day-to-day movements affect downstream decisions
arXiv:2608.24707v2 Announce Type: replace Abstract: While text-based hallucination detection is well studied, reference-free detection of factual alterations in speech remains underexplored, especiall
arXiv:2609.26839v1 Announce Type: cross Abstract: Post-hoc probability calibration is usually evaluated under an optimistic assumption: the held-out calibration labels are clean. In many AI deployment
arXiv:2609.27523v1 Announce Type: new Abstract: Breast image classification requires local detail and global tissue context, yet these cues can weaken as representations deepen. We present M3D-Net, a
arXiv:2609.26866v1 Announce Type: new Abstract: Tool-result caching reduces repeated execution in agent training, but also couples rollout randomness. We study a two-action model in which independent
arXiv:2609.27041v1 Announce Type: new Abstract: Mathematical reasoning benchmarks are typically organized by topic, but language models may organize their internal computation by reusable reasoning ap
arXiv:2609.27167v1 Announce Type: cross Abstract: Action-chunked visuomotor policies predict overlapping trajectories, so every executed action is covered by several predictions. Temporal ensembling s
arXiv:2609.27225v1 Announce Type: cross Abstract: Probabilistic question-answering systems -- whether large language models (LLMs) themselves, retrieval-augmented generation (RAG), or trained multi-ho
arXiv:2609.28256v1 Announce Type: cross Abstract: Vision-Language-Action models provide a strong foundation for general-purpose robot control, yet a vast majority of policies do not preserve and lever
arXiv:2609.28399v1 Announce Type: new Abstract: Language models typically construct attention values from contextual hidden states, even when some of their content may be reusable across contexts. We
arXiv:2609.27286v1 Announce Type: new Abstract: Long horizon language model agents continuously accumulate interaction history, increasing computational cost while making relevant information harder t
arXiv:2609.27247v1 Announce Type: new Abstract: A robot returning a block to its origin tray may encounter two task-consistent pasts that reconverge to the same current input but warrant different act
arXiv:2609.27739v1 Announce Type: new Abstract: We propose the Memory-Efficient Neural Operator (MENO) as a high-performance PDE neural solver based on the Manifold Function Encoder (MFE). MENO featur
arXiv:2603.16868v2 Announce Type: replace-cross Abstract: Monocular 3D scene reconstruction has recently seen significant progress. Powered by the modern neural architectures and large-scale data, rec
arXiv:2609.28358v1 Announce Type: cross Abstract: Microscaling quantization techniques are increasingly used to represent neural network parameters with 8 bits or fewer while preserving near-full prec
arXiv:2609.27142v1 Announce Type: new Abstract: Text-to-image retrieval with frozen dual encoders degrades when the query names a small, visually subordinate object in a cluttered scene: a single glob
arXiv:2609.28438v1 Announce Type: new Abstract: We study minimal-norm interpolation and ell_2-regularized logistic-loss minimization for binary classification by univariate two-layer ReLU networks. We
arXiv:2609.28344v1 Announce Type: cross Abstract: Audio-language models (ALMs) integrate acoustic perception with the knowledge encoded in language models, enabling contextual understanding of auditor
arXiv:2609.27349v1 Announce Type: new Abstract: Real-world molecular design remains challenging for large language model (LLM)-based agents. It requires them to interpret design contexts, satisfy mult
arXiv:2609.27380v1 Announce Type: new Abstract: Likelihood-based context compression can account for cross-context redundancy through sequential scoring, but this makes compression outcomes sensitive
arXiv:2609.28095v1 Announce Type: new Abstract: Recent advances in text-to-video generation have enabled high-fidelity visual synthesis, yet realistic motion remains challenging. Generated videos may
arXiv:2609.28325v1 Announce Type: new Abstract: Model Predictive Path Integral (MPPI) control relies on stochastic trajectory sampling, and its performance under limited rollout budgets depends strong
arXiv:2609.26872v1 Announce Type: new Abstract: Musculoskeletal (MSK) humanoids provide a physiologically grounded embodiment for studying full-body motor control, but their high-dimensional muscle ac
arXiv:2609.27311v1 Announce Type: cross Abstract: Command-and-control (C2) traffic increasingly hides within TLS, so defenders now apply machine learning to traffic metadata. Many studies assume that
arXiv:2609.28317v1 Announce Type: new Abstract: Turn-taking prediction is especially relevant for social robots that act as mediators in human-human interaction, where the expected action is often not
arXiv:2609.27005v1 Announce Type: new Abstract: We focus on the problem of rearranging multiple objects within a constrained workspace via pushing using a team of car-like robots. While the use of mul
arXiv:2609.27280v1 Announce Type: cross Abstract: We study multitask regression when coefficient sharing can differ by predictor. For a given predictor, many tasks may have the same coefficient while
arXiv:2609.28437v1 Announce Type: new Abstract: Online information is increasingly consumed in video format. Much of this comes in the form of *raw video*: continuous footage taken on a cell phone, wi
arXiv:2609.27590v1 Announce Type: new Abstract: Long-context evaluations often test whether a model can recover distant evidence, but recoverability does not guarantee behavioral influence. We test th
arXiv:2609.27086v1 Announce Type: new Abstract: NADI 2026 is the seventh edition of the Nuanced Arabic Dialect Identification (NADI) shared task series and the second dedicated to multidialectal Arabi
arXiv:2609.27218v1 Announce Type: new Abstract: Embodied navigation requires spatial representations that generalize across unseen environments, yet collecting large amounts of annotated data from rea
arXiv:2609.27526v1 Announce Type: new Abstract: Long-horizon navigation requires an agent to revise its intermediate objectives as evidence accumulates. Full visual histories are costly to process, wh
arXiv:2609.27372v1 Announce Type: cross Abstract: Benchmarks for full-duplex spoken dialogue models score turn-taking with binary fixed-window rules that reward immediate response or silence by comple
arXiv:2609.27728v1 Announce Type: new Abstract: We introduce NeuralSRNF, a novel framework for the statistical shape analysis and generation of genus-zero 3D and 4D objects that undergo nonrigid defor
arXiv:2609.26841v1 Announce Type: cross Abstract: High-capacity neural network models have achieved state-of-the-art performance across diverse classification tasks, yet they frequently operate as bla
arXiv:2607.14236v2 Announce Type: replace-cross Abstract: Pretrained vision-language-action (VLA) policies provide strong language-conditioned manipulation knowledge, but they remain largely vision-dr
arXiv:2609.27291v1 Announce Type: new Abstract: Neural network size is usually chosen before training, separating architecture selection from weight optimization. We introduce the Neurogenesis Network