Knowledge catalogue
arXiv:2609.37226v1 Announce Type: cross Abstract: Answering questions and completing tasks over large document collections often requires connecting evidence spread across multiple documents, such as
arXiv:2609.37157v1 Announce Type: new Abstract: Learner simulation aims to reproduce how a particular learner behaves on new tasks. Although Large Language Models (LLMs) can generate increasingly fine
arXiv:2609.38116v1 Announce Type: new Abstract: Long-tailed 3D object detection is treated as a class-frequency problem, but LiDAR supervision quality depends on object observability: similar frequenc
arXiv:2609.36056v1 Announce Type: new Abstract: In urban low-altitude flight, buildings reshape ambient wind into spatially varying 3D flow, making unmanned aerial vehicle (UAV) energy depend on local
arXiv:2609.37454v1 Announce Type: new Abstract: Underwriters in commercial Property and Casualty (P&C) insurance spend 30 to 40% of their time on administrative work rather than risk judgment, and a s
arXiv:2609.35924v1 Announce Type: new Abstract: Discrete diffusion models generate sequences by iteratively resolving multiple tokens in parallel, offering a flexible alternative to left-to-right gene
arXiv:2609.36134v1 Announce Type: new Abstract: Functional Kolmogorov-Arnold Networks (FunKAN) achieve state-of-the-art accuracy on MRI Gibbs artifact removal and anatomical segmentation, but their 11
arXiv:2609.36788v1 Announce Type: new Abstract: Incorporating rich task-relevant context, such as domain knowledge and external observations, is a key capability yet remains challenging for Bayesian o
arXiv:2609.38177v1 Announce Type: cross Abstract: Reasoning about the 3D world from multi-view images remains a fundamental challenge for Multimodal Large Language Models (MLLMs). While modern MLLMs h
arXiv:2609.37187v1 Announce Type: new Abstract: Language-guided navigation requires connecting partial observations to a persistent spatial reference and learning how actions change that representatio
arXiv:2601.11433v2 Announce Type: replace Abstract: Deep Differentiable Logic Gate Networks (LGNs) and Lookup Table Networks (LUTNs) are demonstrated to be suitable for the automatic classification of
arXiv:2609.36546v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student model on its self-generated trajectories with dense token-level teacher feedback. However, naive OPD may
arXiv:2609.35938v1 Announce Type: new Abstract: This paper proposes an interpretable neural operator framework, the Kernel Operator Network (KernelOnet), which incorporates kernel functions explicitly
arXiv:2609.37988v1 Announce Type: new Abstract: As the context size of text processed with an LLM grows, the size of KV caches can outstrip the memory allocated for the original model weights. This im
arXiv:2609.37497v1 Announce Type: new Abstract: Modern transformer models excel at capturing semantic relationships through sentence embeddings, yet their ability to perform pragmatic reasoning remain
arXiv:2609.37017v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) increasingly use latent collaboration to avoid the information loss and repeated encoding-decoding overhead of natur
arXiv:2609.38027v1 Announce Type: new Abstract: In recent years, the performance of large language models (LLMs) on reasoning tasks has been remarkable, even surpassing human capabilities on various b
arXiv:2609.37586v1 Announce Type: cross Abstract: Continual audio deepfake detection requires learning newly emerging deepfake methods while retaining discrimination of previously encountered speech.
arXiv:2609.37044v1 Announce Type: new Abstract: Explicit intermediate reasoning gives large language models (LLMs) a stronger problem-solving mode. We study learning from this think-mode advantage via
arXiv:2609.35884v1 Announce Type: new Abstract: Divergence-free vector fields are fundamental state variables in incompressible flows and many PDE systems. Redundant parameterizations, including Neura
arXiv:2609.37392v1 Announce Type: new Abstract: Modeling the temporal evolution of macroscopic properties of complex systems is an important scientific task. To predict this evolution without full mic
arXiv:2609.37122v1 Announce Type: new Abstract: Triangular transport maps provide a flexible approach to sampling-based probabilistic modeling, including density estimation, generative modeling, and B
arXiv:2609.37932v1 Announce Type: new Abstract: Systems operating in dynamic environments require timely updates to sustain performance. For resource-intensive systems such as machine learning models
arXiv:2609.37127v1 Announce Type: new Abstract: Establishing unbranding as a critical practice to prevent visual logos from acquiring negative connotations is standard in image generation. Large Langu
arXiv:2609.36490v1 Announce Type: cross Abstract: Latent space monitors aim to detect undesired behaviors in LLM agents by inspecting an agent's internal activations rather than its outputs. However,
arXiv:2609.38154v1 Announce Type: new Abstract: Video diffusion models are increasingly developed into specialized models for diverse downstream tasks, and this development often includes a distillati
arXiv:2609.36108v1 Announce Type: cross Abstract: Tabular foundation models using in-context learning have recently surpassed gradient-boosted trees on predictive tabular tasks. However, recent mechan
arXiv:2508.13009v5 Announce Type: replace Abstract: Recent advances in interactive video generations have demonstrated diffusion model's potential as world models by capturing complex physical dynamic
arXiv:2609.36581v1 Announce Type: new Abstract: Query-agnostic streaming video understanding requires vision-language models to continuously compress an indefinitely growing visual stream into a bound
arXiv:2609.36435v1 Announce Type: new Abstract: An assistant that serves the same user over a long horizon has to answer from what that user has revealed: which preferences still hold, which were revi
arXiv:2609.37304v1 Announce Type: new Abstract: Large reasoning models improve performance on challenging problems by allocating additional computation before answering, but longer reasoning does not
arXiv:2609.36810v1 Announce Type: new Abstract: Video world models aim to predict future content from an observed scene while following prescribed camera motion. Real-world scene evolution is determin
arXiv:2609.36301v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) layers are central to frontier language models, and recent architectures push toward more and smaller experts. In this regime
arXiv:2609.36832v1 Announce Type: new Abstract: Text-to-video (T2V) diffusion models can generate realistic depictions of actions such as kicking, stabbing, and shooting, raising safety concerns that
arXiv:2410.14839v5 Announce Type: replace-cross Abstract: We study the dynamic pricing problem faced by a broker seeking to learn prices for a large number of credit market securities, such as corpora
arXiv:2609.37148v1 Announce Type: cross Abstract: Many of the qualities that matter most in how people learn and grow, how someone regulates their emotions, reflects on a setback, or stays aware of ot
arXiv:2609.37994v1 Announce Type: cross Abstract: The classical information bottleneck (IB) measures the relevance of a representation U of X to a target Y by I(U;Y), which does not directly character
arXiv:2609.37793v1 Announce Type: new Abstract: World-Action Models (WAMs) couple visual dynamics with action prediction, bringing the rich priors of pretrained video models to robotic manipulation. H
arXiv:2609.36687v1 Announce Type: cross Abstract: Neural codecs encode continuous signals into compact sequences of discrete tokens, providing an interface for efficient transmission, storage, and tok
arXiv:2609.38098v1 Announce Type: new Abstract: Current vision-language models (VLMs) encode visual information in dense hidden states where object identity, spatial layout, and local attributes are i
arXiv:2609.35833v1 Announce Type: new Abstract: Running a language model on edge hardware provides private and low-latency reasoning without a network connection, and yet the small models that fit on
arXiv:2609.37052v1 Announce Type: new Abstract: Omnimodal large language models (Omni-LLMs) encode audio and visual streams into temporally interleaved token sequences for multimodal reasoning. Howeve
arXiv:2609.37773v1 Announce Type: new Abstract: Artificial Intelligence Virtual Cells (AIVCs) are envisioned as scientific agents that simulate cellular responses, explain underlying mechanisms, and s
arXiv:2602.13540v2 Announce Type: replace-cross Abstract: Accurate confidence estimation is critical for reliable use of large language models (LLMs). Prior work on LLM calibration largely focuses on
Taiwan-based neocloud GMI Cloud Inc. wants to capitalize on what it says is an “unprecedented” demand for artificial intelligence computing power after raising a massive 663 million in funding from tw
OpenAI's New Agent Stack: Superhuman Computer Use, Dots, Decisions API, UltraFast, & the AI Cloud https://www.latent.space/p/devday-2026 @OpenAI’s @AriX and @nikunjhanda explain why Computer Use is al
arXiv:2609.36374v1 Announce Type: new Abstract: Feed-forward 3D reconstruction models have achieved impressive performance by scaling model and dataset size, but their cost excludes most research grou
arXiv:2609.36521v2 Announce Type: new Abstract: Physical-field reconstruction and forecasting depend on both measurement density and spatial layout, yet evaluation under a single observation pattern d
arXiv:2609.37609v1 Announce Type: new Abstract: Magnetohydrodynamics (MHD) is central to plasma modeling in astrophysics, space science, fusion, and engineering, but resolving multiscale MHD dynamics
arXiv:2609.37970v1 Announce Type: cross Abstract: World-action models (WAMs) jointly predict how a scene will evolve and how an agent should act, however joint generation alone does not necessarily im
arXiv:2609.37907v1 Announce Type: new Abstract: Video games offer scalable environments for studying perception and control in embodied agents.Abundant online gameplay videos could supply demonstratio
arXiv:2609.38054v1 Announce Type: cross Abstract: We present Pow3R-SLAM, a real-time RGB-D simultaneous localization and mapping (SLAM) system that uses Pow3R for tracking and mapping. Inspired by MAS
arXiv:2609.36642v1 Announce Type: new Abstract: Language-model agents are usually trained by reinforcement learning from one reward per episode, and privileged self-distillation enriches it by letting
arXiv:2609.36216v1 Announce Type: new Abstract: Neural operators are typically trained in a supervised fashion, which requires a dataset to be generated with a classical solver. Training them physics-
arXiv:2609.36872v1 Announce Type: new Abstract: Flow-matching policies can represent complex robot behaviors but remain susceptible to local errors under distribution shift at deployment. Many reinfor
arXiv:2609.38149v1 Announce Type: new Abstract: Transformer language models (LMs) are feed-forward: deep-layer representations are never fed back to shallower layers, and the only pathway for informat
arXiv:2609.36159v1 Announce Type: new Abstract: Large language models can reason in continuous space instead of decoded text, by recurring on their own hidden states or by passing those states between
arXiv:2609.37470v1 Announce Type: new Abstract: A probability used for a decision should refer to the same event across equivalent requests. We introduce probability contracts, a benchmark connecting
arXiv:2609.38095v1 Announce Type: new Abstract: Backpropagation (BP) dominates deep learning but imposes a massive memory tax. For example, training OPT-30B with Adam requires approx 600GB of GPU memo
arXiv:2609.35549v2 Announce Type: replace Abstract: Rare-disease diagnosis is a long-tail reasoning problem: phenotypes are incomplete, individual disorders are sparsely documented, and relevant evide