Knowledge catalogue
arXiv:2604.06373v1 Announce Type: cross Abstract: New generation of AI coding tools, including AI-powered IDEs equipped with agentic capabilities, can generate code within the context of the project.
arXiv:2511.08409v4 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) frequently suffer from unfaithfulness, generating reasoning chains that drift from visual evidence or contr
arXiv:2604.06448v1 Announce Type: cross Abstract: Prime Video regularly conducts load tests to simulate the viewer traffic spikes seen during live events such as Thursday Night Football as well as vid
arXiv:2603.09677v3 Announce Type: replace Abstract: Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the O
arXiv:2604.06881v1 Announce Type: new Abstract: Neural operators have emerged as powerful surrogates for dynamical systems due to their grid-invariant properties and computational efficiency. However,
MLX is an open-source machine learning framework developed by Apple ML researcher Awni Hannun and colleagues, announced on December 5, 2023, and designed specifically for Apple Silicon hardware. Th...
arXiv:2604.06267v1 Announce Type: cross Abstract: Multimodal variational autoencoders (VAEs) have emerged as a powerful framework for survival risk modeling in multiple myeloma by integrating heteroge
arXiv:2604.07944v1 Announce Type: new Abstract: Large language models (LLMs) have recently demonstrated strong potential for autonomous vehicle motion planning by reformulating trajectory prediction a
arXiv:2604.08031v1 Announce Type: cross Abstract: Most Human-Machine Interaction (HMI) research overlooks the maneuvering needs of passengers in autonomous driving (AD). Natural language offers an int
arXiv:2512.07755v3 Announce Type: replace-cross Abstract: Recent studies have demonstrated the success of deep learning in solving forward and inverse problems in engineering and scientific computing
arXiv:2604.05704v2 Announce Type: replace Abstract: Multimodal Sentiment Analysis (MSA) aims to infer human sentiment from textual, acoustic, and visual signals. In real-world scenarios, however, mult
arXiv:2510.12710v3 Announce Type: replace Abstract: Pre-trained Vision-Language-Action (VLA) models represent a major leap towards general-purpose robots, yet efficiently adapting them to novel, speci
arXiv:2604.07644v1 Announce Type: new Abstract: We present GPU-SLS, a GPU-parallelized framework for safe, robust nonlinear model predictive control (MPC) that scales to high-dimensional uncertain rob
arXiv:2604.08366v1 Announce Type: cross Abstract: Large-scale deep learning models for physical AI applications depend on diverse training data collection efforts. These models and correspondingly, th
arXiv:2604.06811v1 Announce Type: cross Abstract: Skill-based agent systems tackle complex tasks by composing reusable skills, improving modularity and scalability while introducing a largely unexamin
arXiv:2604.07028v1 Announce Type: cross Abstract: Strategic interaction in adversarial domains such as law, diplomacy, and negotiation is mediated by language, yet most game-theoretic models abstract
arXiv:2010.00638v2 Announce Type: replace-cross Abstract: Generative models for tabular data have evolved rapidly beyond Generative Adversarial Networks (GANs). While GANs pioneered synthetic tabular
arXiv:2604.08121v1 Announce Type: new Abstract: Unified multimodal models integrating visual understanding and generation face a fundamental challenge: visual generation incurs substantially higher co
arXiv:2604.06644v1 Announce Type: cross Abstract: As deep learning inference is increasingly deployed in shared and cloud-based settings, a growing concern is input repurposing, in which data submitte
Highlights: 👉 Configurable thinking mode for step-by-step reasoning 👉 Multimodal understanding with text and image input, including document parsing and OCR 👉 Native function calling with structured t
arXiv:2610.22341v1 Announce Type: cross Abstract: We introduce 3DCodeVerse, a research ecosystem that enables coding agents to create editable and verifiable 3D content at scale. 3DCodeVerse comprises
arXiv:2610.21624v1 Announce Type: new Abstract: Real-world tasks are often complex and noisy, posing substantial challenges for LLM agents. Yet existing evaluations lack a systematic framework for joi
arXiv:2606.21550v2 Announce Type: replace Abstract: Alignment from human feedback uses human judgments about model outputs to steer the behavior of language models after pretraining. When those judgme
arXiv:2610.21011v1 Announce Type: new Abstract: Identifying individual dogs from barks in real-world videos remains challenging and largely unexplored. Solving this problem would enable long-term beha
arXiv:2610.21290v1 Announce Type: new Abstract: Multi-agent reasoning with large language models (LLMs) raises a central question for collective decision-making: when agents disagree, whose decision s
arXiv:2610.20668v1 Announce Type: cross Abstract: We introduce CascadeLedger, a counterfactual execution-audit framework for budgeted language-model cascades. The protocol enforces call-before-read se
arXiv:2610.22119v1 Announce Type: new Abstract: We propose Compliant GMT (C-GMT), a modular framework for full-body compliant responses in general motion tracking. As humanoid robots move into real wo
arXiv:2610.21910v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) advance, they are increasingly expected to perform spatiotemporal reasoning over motion, actions, and view changes beyo
arXiv:2610.21525v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown impressive capabilities across many tasks, including processing large amounts of text, following instructions, a
arXiv:2610.22201v1 Announce Type: cross Abstract: We present a scalable framework for solving multi-step tasks through a decentralized market of self-interested agents. Prior theoretical work demonstr
arXiv:2610.20615v1 Announce Type: new Abstract: Adaptive optimization workflows in machine learning, federated learning, quantum machine learning, and hybrid classical quantum systems often embed cont
arXiv:2601.10471v3 Announce Type: replace Abstract: We present DeFlow, a decoupled offline RL framework that leverages flow matching to faithfully capture complex behavior manifolds. Optimizing genera
arXiv:2610.21668v1 Announce Type: new Abstract: We introduce Edit4D, an editable text-to-4D scene generation framework that constructs 4D scenes from separate background and dynamic character represen
arXiv:2610.21604v1 Announce Type: new Abstract: Scientific inquiry involves iterative cycles of exploration, hypothesis generation, and reasoning over observations to uncover relationships in complex
arXiv:2610.21948v1 Announce Type: cross Abstract: As multimodal AI systems are becoming our everyday thought partners, the shift towards studying how users transfer control to these systems becomes es
arXiv:2610.22371v1 Announce Type: new Abstract: We introduce Gen4P, a unified framework for recovering 4D geometry and motion from monocular video. We show that a pretrained video generator can be rep
arXiv:2610.21183v1 Announce Type: cross Abstract: Surface reflectance products derived from optical satellite may exhibit systematic inconsistencies across acquisition dates, because atmospheric corre
arXiv:2610.21466v1 Announce Type: new Abstract: Ontologies are widely used to represent and organize complex knowledge in domains such as biomedicine and healthcare. However, constructing reliable ont
arXiv:2610.21808v1 Announce Type: new Abstract: Weight quantization below four bits shrinks memory and bandwidth at a perplexity that looks acceptable, but often at the cost of factual knowledge and t
arXiv:2610.21061v1 Announce Type: cross Abstract: Discrete diffusion models have become a powerful framework for modeling complex discrete distributions across a range of applications. While theoretic
arXiv:2610.21518v1 Announce Type: new Abstract: Understanding how the human brain constructs meaning from real-world language remains a fundamental challenge, as semantics depend on context that varie
arXiv:2610.20968v1 Announce Type: cross Abstract: Executing heavy-duty physical manipulation in unpredictable environments remains a major challenge for quadrupedal robots lacking dedicated manipulato
arXiv:2610.20894v1 Announce Type: cross Abstract: This paper investigates the statistical behavior of Stochastic Gradient Descent (SGD) with constant step size under the framework of iterated random f
arXiv:2610.20860v1 Announce Type: cross Abstract: We propose the Time-partitioning Gaussian Process Mixture Model (TPGPMM), a Bayesian nonparametric framework for identifying persistent regimes and mo
arXiv:2610.21395v1 Announce Type: cross Abstract: Recent studies have reported promising results from quantum machine learning in drug discovery, healthcare, materials science and cybersecurity. Resea
arXiv:2610.18355v2 Announce Type: new Abstract: We introduce ABot Embodied Reasoning (ABot-ER), a unified embodied reasoning foundation model that breaks the tension between breadth and depth in embod
arXiv:2610.19404v1 Announce Type: cross Abstract: Reticular chemistry allows metal-organic frameworks (MOFs) to be designed from their building units, yet their synthesis remains constrained by sparse
arXiv:2610.19692v1 Announce Type: new Abstract: Gaussian diffusion models offer a practical framework for generative modeling. Denoising Levy Probabilistic Models (DLPM) introduce heavy-tailed perturb
arXiv:2509.20645v4 Announce Type: replace Abstract: Modern ML research increasingly faces an evaluation bottleneck: building benchmarks and running experiments is costly, slowing iteration. We ask how
arXiv:2402.05203v3 Announce Type: replace Abstract: We introduce Bellman Conformal Inference (BCI), a framework that wraps around any time series forecasting models and provides calibrated prediction
arXiv:2610.19367v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for multi-turn agentic tasks that require interactions with users and environments over extended sequ
arXiv:2610.20251v1 Announce Type: new Abstract: Boundary delineation of anatomical structures in prostate MRI is often compromised by regional ambiguity. Automated segmentation methods relying on glob
arXiv:2610.19610v1 Announce Type: new Abstract: Industrial anomaly detection (AD) is bottlenecked by the scarcity of anomalous samples, making high-fidelity anomaly synthesis crucial for improving dow
arXiv:2610.17670v1 Announce Type: new Abstract: Cowork agents navigate dependencies across tools and files while adapting to changing user requirements and edits to shared workspaces. Scaling such env
arXiv:2610.19849v1 Announce Type: new Abstract: We propose DeltaStreamer, a novel agentic framework for streaming video understanding inspired by how humans observe, remember, and investigate. Streami
arXiv:2610.19364v1 Announce Type: new Abstract: Dynamic graph learning models how entities and their relations evolve from historical observations to predict future graph states. Existing methods do n
arXiv:2610.17672v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for human mobility generation by modeling individual routines, activities, and mobility prefere
arXiv:2610.20305v1 Announce Type: cross Abstract: Homogeneous turbulent flows exhibit recurring multiscale structures across spatially separated regions, suggesting that a single flow realization may
arXiv:2606.17648v3 Announce Type: replace Abstract: Standard accuracy metrics cannot explain why LLMs handle variable tracking but fail on semantically equivalent loops. We study an internal lifecycle
arXiv:2603.26515v2 Announce Type: replace-cross Abstract: Efficient and robust turn-taking detection remains a significant challenge in industrial-grade voice AI agents. Existing systems often rely on