Knowledge catalogue
arXiv:2609.34071v1 Announce Type: new Abstract: Diffusion and flow-matching models can produce high-quality posterior samples for inverse problems, but typically require tens to thousands of network e
arXiv:2512.15493v2 Announce Type: replace Abstract: Physical systems are naturally parameterized in terms of geometric entities and their transformations. While exact equivariance can be a powerful in
arXiv:2609.32197v1 Announce Type: cross Abstract: Diffusion Transformers have become the dominant architecture for video generation. Their substantial computational cost motivates scaling inference ac
arXiv:2609.35410v1 Announce Type: new Abstract: Spectral super-resolution of multispectral satellite images can enable high temporal- and spatial-resolution hyperspectral satellite imagery at a modest
arXiv:2609.34153v1 Announce Type: new Abstract: Proteins change shape as they function, yet most inverse folding models predict amino acid sequences from a single, fixed backbone. A central challenge
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arXiv:2609.33761v1 Announce Type: new Abstract: Generative models for multivariate weather series are routinely evaluated with pooled distributional metrics computed after marginal calibration. We sho
arXiv:2609.34756v1 Announce Type: cross Abstract: Adapting pretrained models to downstream tasks with limited data has become a central paradigm in modern deep learning. Yet, despite its widespread pr
arXiv:2609.32864v1 Announce Type: new Abstract: Neural surrogates are trained to predict 3D turbulent flows in place of direct numerical simulation (DNS). For chaotic flows, the goal is short-term poi
arXiv:2609.33203v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have established themselves as a scalable backbone for high-fidelity image synthesis. However, unlike U-Net based diffusio
arXiv:2609.34598v1 Announce Type: new Abstract: Video temporal grounding (VTG) aims to localize the video interval corresponding to a language query. Recent large vision-language models (LVLMs) show g
arXiv:2609.32944v1 Announce Type: new Abstract: Wildlife-vehicle collisions remain a significant road safety concern, particularly during nighttime and low-visibility conditions when RGB-based percept
arXiv:2609.34002v1 Announce Type: new Abstract: Decoding brain states requires models that capture both the evolution of neural activity and interactions among groups of brain regions. Existing EEG me
arXiv:2506.19459v2 Announce Type: replace Abstract: Causal discovery has shifted from data-centric methods to hybrid strategies that integrate semantic knowledge from experts or large language models
arXiv:2609.35219v1 Announce Type: new Abstract: Relational deep learning models database rows and foreign-key links as a heterogeneous graph for prediction from record attributes and relational contex
arXiv:2609.32759v1 Announce Type: new Abstract: We introduce the Extender, a log-structured variant of the standard Transformer architecture. In a standard Transformer, each layer communicates with su
arXiv:2609.32384v1 Announce Type: new Abstract: Real-world time series are inherently non-stationary, with trends, periodic patterns, and uncertainty evolving over time. While the Fourier domain offer
arXiv:2609.32665v1 Announce Type: cross Abstract: ELBO-based reinforcement learning offers a sampler-agnostic approach to fine-tuning flow matching models with reward feedback. Timestep weighting in E
arXiv:2609.33125v1 Announce Type: new Abstract: Co-training offers a straightforward way to build a multi-task vision-language-action (VLA) policy, but can fall short of the performance achieved by tr
arXiv:2609.32502v1 Announce Type: cross Abstract: De novo 3D molecular generation jointly models molecular size, topology, and geometry. Most methods pre-sample molecular size and generate Cartesian c
arXiv:2609.34447v1 Announce Type: cross Abstract: On-policy distillation (OPD) is becoming an important component of large language model (LLM) post-training for transferring the reasoning capability
arXiv:2609.34458v1 Announce Type: cross Abstract: Classical statistical theory is insufficient to explain the successes of general-purpose AI models, because it depends on handcrafted inductive biases
arXiv:2609.35763v1 Announce Type: new Abstract: Distributional training provides collective supervision for one-step visual generation by matching real and generated features in frozen representation
arXiv:2609.35641v1 Announce Type: cross Abstract: Precise instruction following in image generation, such as satisfying object counts and spatial relations, remains an open challenge at least in part
arXiv:2609.33935v1 Announce Type: new Abstract: Adapting video models to new tasks typically requires dedicated data curation and fine-tuning. While visual analogy provides a training-free alternative
arXiv:2609.33104v1 Announce Type: new Abstract: While action-conditioned video prediction provides an intuitive world model for robotics, purely data-driven predictors often suffer from compounding er
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arXiv:2609.31760v1 Announce Type: cross Abstract: Can a robot improve itself the way coding agents now improve software? We built an agentic system to find out. It watches a robot fail, works out whic
arXiv:2609.34221v1 Announce Type: new Abstract: Despite rapid progress, world models still lack explicit, persistent structural memory, making it difficult to preserve consistent world structure durin
arXiv:2609.32457v1 Announce Type: new Abstract: Transformers process information strictly forward through depth, preventing deeper computation from revisiting and refining earlier representations. To
arXiv:2609.34010v1 Announce Type: cross Abstract: We present ZeroBot, a real2sim framework for learning a robot manipulation task from scratch in minutes under challenging conditions: zero human demon
arXiv:2609.30414v1 Announce Type: cross Abstract: Cognitive psychology has studied how people encode, use, and learn concepts that describe categories, and how they represent, recognize, and acquire c
arXiv:2609.31184v1 Announce Type: new Abstract: LLM-as-a-judge has become the de facto standard for scalable, subjective evaluation, yet current leaderboards compensate for systematic measurement bias
arXiv:2411.04707v4 Announce Type: replace Abstract: Deep neural networks achieve strong performance on complex tasks but are often regarded as 'black boxes,' which limits their adoption in domains whe
arXiv:2609.31166v1 Announce Type: cross Abstract: Recommender systems have traditionally been developed for platforms. However, this has given rise to many phenomena that may be advantageous for platf
Maxwell Zeff / Wall Street Journal: AI research leaders at OpenAI, Anthropic, Microsoft, and Meta warn of an impending “intelligence explosion” and call for oversight into automated AI research — Lead
arXiv:2609.30569v1 Announce Type: new Abstract: CryoEM map interpretation requires features that are spatially localized, consistent across samples, and informative across spatial scales. Most deep le
For decades, the observability industry has answered one basic question: Is it running? Agentic artificial intelligence breaks that model. An agent can return a clean response, meet its latency target
arXiv:2609.31431v1 Announce Type: new Abstract: Accurate segmentation of axons in 3D microscopy data is important for analyzing white-matter organization, but dense ground truth labels are expensive t
arXiv:2609.30747v1 Announce Type: cross Abstract: As artificial intelligence (AI) becomes embedded in everyday life, its environmental footprint, particularly water consumption remains largely invisib
Deploy a text-to-speech model on Amazon SageMaker AI with the AWS vLLM-Omni Deep Learning Container and stream generated speech over a persistent bidirectional connection. This Part 1 tutorial deploys
arXiv:2609.31540v1 Announce Type: cross Abstract: Sending every network-automation input to a third-party frontier LLM exports sensitive artifacts such as production configurations, topologies, and lo
arXiv:2609.31062v1 Announce Type: new Abstract: Patient-facing AI assistants promise valuable support to patients, but incoming queries can pose medical risks. To create guardrails, we work with oncol
arXiv:2609.31381v1 Announce Type: new Abstract: Context projection replaces older tool observations with compact, addressable excerpts, reducing repeated input while potentially adding evidence-retrie
arXiv:2609.30523v1 Announce Type: new Abstract: Large language model (LLM)-powered multi-robot systems are vulnerable to semantic manipulation: an accepted false world-state claim can trigger a fleet-
arXiv:2609.30714v1 Announce Type: new Abstract: Agentic AI systems are increasingly being explored in medical imaging to improve throughput and reduce clinician workload; however, safe deployment rema
arXiv:2512.08996v2 Announce Type: replace-cross Abstract: Radiotherapy planning is a highly complex process that often varies significantly across institutions and individual planners. Most existing d
arXiv:2609.31215v1 Announce Type: new Abstract: Large language models (LLMs) as a judge enable scalable evaluation, but their judgments can be sensitive to response order and, even after removing such
arXiv:2305.15394v3 Announce Type: replace Abstract: Decision trees are interpretable models that are well-suited to non-linear learning problems. Much work has been done on extending decision tree lea
arXiv:2609.31130v1 Announce Type: new Abstract: We empirically investigate Question Under Discussion based modelling in naturalistic dialogue by studying whether the salience of generated potential qu
arXiv:2609.31112v1 Announce Type: new Abstract: Vision-language models (VLMs) enable open-vocabulary reasoning for robot manipulation, but their high inference latency limits responsiveness in dynamic
arXiv:2609.31047v1 Announce Type: cross Abstract: Agentic LLM workflows decide their execution paths at runtime. Downstream computation may be predictable, or may have run before, yet it cannot begin
arXiv:2609.30882v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to assess long-form medical videos, but their effectiveness may depend on whether transcripts are pro
arXiv:2401.04282v2 Announce Type: replace Abstract: We introduce a model-agnostic framework for constrained post-hoc error correction in binary classifiers. Given a frozen base classifier, the method
arXiv:2609.30417v1 Announce Type: new Abstract: As electric vehicle (EV) adoption increases, ensuring efficient and well-distributed charging infrastructure has become a critical challenge. While many
arXiv:2609.30581v1 Announce Type: cross Abstract: Encrypted training relies on keeping server-side updates low-degree. This constraint traditionally excludes models whose weights inhabit a compact Lie
arXiv:2609.30572v1 Announce Type: new Abstract: Large language models are often post-trained on expert demonstrations using cross-entropy (CE), even when the downstream objective is not to imitate the
arXiv:2609.30535v1 Announce Type: new Abstract: Children in multilingual communities often code-switch, using multiple languages in a single utterance. Can we induce cross-lingual alignment in languag
arXiv:2609.31128v1 Announce Type: new Abstract: Frequency-based adversarial attacks have recently grown popular by exploiting spectral sensitivities shared across neural architectures. Unlike spatial
arXiv:2609.30543v1 Announce Type: new Abstract: As robots transition from structured factory settings into homes, they are required to interact with an ever-increasing variety of objects. Many tasks r