Knowledge catalogue
deep agents just shipped a round of multimodal upgrades! 1. broader file support on OpenAI Responses: docx, pptx, xlsx, csv, json, markdown, not just pdfs 2. opt-in binary offloading to keep big files
arXiv:2610.03419v1 Announce Type: new Abstract: In this work we formulate ultrasound multistatic recovery from arbitrary transmit sequences as a Bayesian inference problem. To that end, we train a dee
arXiv:2607.19060v2 Announce Type: replace-cross Abstract: Fast prediction of the response of adhesive soft viscoelastic contacts represents a current challenge in soft robotics and for gripping and ma
DeepSeek V4.1 Flash is now available for training on both the Dedicated Training API and Managed Training surfaces! It's a strong base for agentic coding, terminal automation, and tool use. It's also
arXiv:2610.02494v1 Announce Type: new Abstract: Automatic horizon tracking is a foundational task in 3D seismic interpretation. Most existing deep learning approaches formulate it as dense semantic se
arXiv:2610.02691v1 Announce Type: new Abstract: Interactive world simulators can provide scalable environments for robot planning, policy training, and evaluation by predicting action consequences whi
arXiv:2602.16543v2 Announce Type: replace Abstract: Safe reinforcement learning (Safe RL) learns robotic controllers that optimize task rewards under safety constraints, yet observation perturbations
arXiv:2512.19905v3 Announce Type: replace-cross Abstract: Recent developments in large language models have shown advantages in reallocating a notable share of computational resource from training tim
arXiv:2610.02584v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) models combine learned routing with selection from a large expert pool. We isolate the contribution of dynamic expert co
arXiv:2610.03617v1 Announce Type: new Abstract: Image-text alignment is a core problem in computer vision with applications in caption evaluation, hallucination detection, data curation, and the bench
arXiv:2610.03626v1 Announce Type: new Abstract: The recent wave of one-step generative models, which compress the multi-step trajectory of diffusion via either distillation or learned flow maps, has r
arXiv:2610.03439v1 Announce Type: new Abstract: Event cameras hold excellent dynamic properties, showing great potential for monocular depth estimation (MDE). However, existing methods mainly improve
arXiv:2610.02351v1 Announce Type: new Abstract: ReAct-based agents typically rely on a single LLM policy to propose actions, interact with the environment, and decide when a task is complete. This cou
arXiv:2610.02631v1 Announce Type: new Abstract: Post-deployment feedback from users can be a cost-effective, scalable, and representative means to monitor and improve generative AI systems and feature
arXiv:2610.02320v1 Announce Type: cross Abstract: Computer-use agents need to reliably ground action targets in complex desktop scenes, where multiple applications, overlapping windows, and visually s
arXiv:2610.03498v1 Announce Type: cross Abstract: Adversarial patches can disrupt Vision-Language-Action (VLA) models by manipulating visual observations, leading to failures in robot control. However
arXiv:2610.03278v1 Announce Type: new Abstract: As dexterous hands proliferate, collecting data and training policies separately for every morphology becomes increasingly impractical. Scalable cross-e
arXiv:2610.02990v1 Announce Type: new Abstract: Real-world interaction networks are inherently dynamic: edges form and dissolve as node behavior shifts over time. Most snapshot-based contrastive metho
arXiv:2510.23463v4 Announce Type: replace Abstract: Federated Learning (FL) is a distributed learning paradigm that preserves privacy by eliminating the need to exchange raw data during training. In i
arXiv:2610.02716v1 Announce Type: new Abstract: Objective perturbation adds a random linear term to a regularized empirical risk and releases the exact perturbed minimizer. We study the finite computa
arXiv:2610.02292v1 Announce Type: cross Abstract: Human activity recognition (HAR) is increasingly important for healthcare, well-being, and daily monitoring ap- plications, for which detecting alimen
arXiv:2606.16610v2 Announce Type: replace-cross Abstract: Diffusion Flow Matching (DFM) has recently emerged as a versatile framework for generative modeling, yet its theoretical convergence propertie
arXiv:2610.02960v1 Announce Type: new Abstract: Deep learning has shown remarkable success in the data-driven modeling of dynamical systems. Much of its success is attributed not to the flexibility of
arXiv:2610.02928v1 Announce Type: cross Abstract: Invariant suites and runtime monitors increasingly gate agent deployment decisions, and the evidence offered for any particular suite is almost always
arXiv:2606.17454v3 Announce Type: replace Abstract: AI agent performance is not just a modeling problem, it is fundamentally a systems problem. The advanced capabilities of models are realized through
arXiv:2610.02574v1 Announce Type: new Abstract: High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple mol
In this project, I distilled the Stockfish value function into a ResNet/ViT model using 1 billion positions from the Gigafish dataset. The 3.9 billion position dataset is available on huggingface: htt
arXiv:2610.02659v1 Announce Type: cross Abstract: Modern state space models (SSMs), such as Mamba2, provide a compelling alternative to transformers by combining linear-time sequence modeling with rec
arXiv:2610.02868v1 Announce Type: cross Abstract: Learning robust survival models under distribution shift is an important but challenging problem in many applications. In heterogeneous populations, a
arXiv:2610.03529v1 Announce Type: cross Abstract: Distillation has become a core primitive of large language model training, but its properties are not yet well understood. We take an entropic perspec
arXiv:2610.02866v1 Announce Type: new Abstract: Financial time-series pretraining typically learns from masked observations, contrastive relations, or future outcomes---yet existing objectives struggl
arXiv:2610.02844v1 Announce Type: new Abstract: Ensuring the safe and reliable deployment of large language models (LLMs) remains a fundamental challenge. Existing safety alignment approaches either i
arXiv:2606.18537v2 Announce Type: replace Abstract: Humans often acquire new skills by observing others, since observed behaviors implicitly reveal how to act reasonably in an environment. However, ob
arXiv:2610.03639v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to support legal practice, education, and research, yet their reliability in national legal systems o
arXiv:2610.02907v1 Announce Type: new Abstract: Residual networks execute every block for every input, yet their functional contributions need not be input independent. We formulate a trained ResNet a
arXiv:2610.02353v1 Announce Type: cross Abstract: Personalized large language models often require a complete adaptation state for each user. However, this paradigm scales poorly as the user populatio
arXiv:2610.03154v1 Announce Type: new Abstract: Video generation models produce strikingly realistic sequences and are increasingly proposed as world models, yet recent benchmarks reveal pronounced de
Does telling your agent to plan ahead actually help? It's standard practice to add a 'plan ahead' or 'think about the other players' instruction to an agent's prompt. In auctions and matching markets,
Amazon Quick doesn't offer a direct console path to downgrade a user from Admin or Author to Reader. This post walks through two reliable methods: a manual delete-and-recreate approach and an AWS CLI
arXiv:2610.03530v1 Announce Type: new Abstract: LiDAR-based quadrotor navigation in cluttered environments remains challenging under external disturbances, particularly when obstacle-aware motion gene
arXiv:2610.03390v1 Announce Type: cross Abstract: Few-step neural text-to-speech models often rely on short- ened diffusion or flow-matching schedules, or on distillation from pretrained multi-step te
arXiv:2610.02344v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) are prone to representation collapse, typically mitigated through empirical heuristics. We develop an e
arXiv:2610.03491v1 Announce Type: new Abstract: Most long-term time-series forecasting models map the look-back window directly to the full horizon in a single pass. While efficient, this design does
arXiv:2610.03543v1 Announce Type: new Abstract: Streaming video generation has benefited from distribution matching distillation (DMD), which matches the joint distribution of video frames to a video
arXiv:2610.03020v1 Announce Type: new Abstract: Long-term agents must remember not only what is true about a user, but also how a particular agent should work with that user as their shared history ev
arXiv:2610.02951v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures scale language models efficiently by activating only a few experts per token, but the saving is confined to com
arXiv:2610.02762v1 Announce Type: new Abstract: Dynamic LLM routers promise to cut inference costs by sending each query to the cheapest model that can answer it correctly. We analyze six commercial r
arXiv:2610.02882v1 Announce Type: cross Abstract: Large-scale foundation models achieve strong performance across diverse tasks, but their size makes inference costly, largely due to dense matrix mult
arXiv:2610.02298v1 Announce Type: cross Abstract: 3D editing methods are usually tested on a single edit, yet an asset is built through a long sequence of revisions, each of which must implement the r
arXiv:2610.02284v1 Announce Type: new Abstract: Temporal context may aid automated bat-species classification, but the contribution of specific features remains unclear. We investigated whether variat
arXiv:2610.02795v1 Announce Type: new Abstract: Deep graph learning models deployed in real-world systems often need to cope with non-stationary environments, where the underlying graph distribution d
arXiv:2610.02410v1 Announce Type: cross Abstract: Implicit neural representations (INRs) provide a flexible framework for modeling high-dimensional continuous fields, but their training is often ineff
arXiv:2610.03509v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning allows humans to inspect how large language models reach their answers, and oversee model behaviour. This reasoning com
arXiv:2610.03444v1 Announce Type: cross Abstract: Molecular optical absorption spectroscopy provides a direct probe of electronic structure and is widely used for molecular identification, interpretat
arXiv:2610.03248v1 Announce Type: new Abstract: Panoramic images provide a complete 360-degree field of view, enabling comprehensive scene understanding for embodied perception. However, heterogeneous
arXiv:2610.03109v1 Announce Type: new Abstract: While representation similarity across independently trained language models is well-documented, how internal mechanics such as attention behave across
arXiv:2610.02538v1 Announce Type: cross Abstract: Inference-time control steers a pretrained generative model towards a target distribution without retraining. We study tilted targets pi_0propto G_0,p
arXiv:2610.02403v1 Announce Type: cross Abstract: Energy saving has become a critical challenge in 5G and beyond networks. The rapid growth of connected devices has increased the overall network energ
arXiv:2610.02949v1 Announce Type: new Abstract: Instruction tuning has become a common paradigm for applying large language models (LLMs) to biomedical named entity recognition (BioNER). However, exis
arXiv:2509.20810v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit strong reasoning capabilities in complex tasks. However, they still struggle with hallucinations and factual er