Knowledge catalogue
arXiv:2507.17389v2 Announce Type: replace-cross Abstract: Recent advances in code large language models (CodeLLMs) have made them indispensable tools in modern software engineering. However, these mod
arXiv:2610.00052v1 Announce Type: cross Abstract: We evaluate six language-model configurations on requests for six distinct random integers from 1-49. Across 1,200 attempted calls using four English
arXiv:2610.00008v1 Announce Type: cross Abstract: Sim-to-real research pursues physics fidelity as a primary objective: simulators are judged by how closely they reproduce real-world contact dynamics.
arXiv:2610.01054v1 Announce Type: cross Abstract: In-context learning (ICL) enables language models to perform new tasks from demonstrations without weight updates. However, every ICL inference requir
arXiv:2610.02070v1 Announce Type: new Abstract: Memory-augmented large language models must decide which memories to retain, and recent systems do so by estimating each memory's effect on task perform
arXiv:2606.15396v2 Announce Type: replace-cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns. While existing LLM safety g
arXiv:2610.02019v1 Announce Type: cross Abstract: The rapid growth of video-based social media has increased users' exposure to harmful content, creating a need for reliable automated video safety det
arXiv:2610.01963v1 Announce Type: new Abstract: Emergency department (ED) triage is a high-stakes prioritization task in which demographic, socioeconomic, and system-context information may improperly
arXiv:2610.00499v1 Announce Type: new Abstract: As diffusion large language models (dLLMs) become more capable, they are moving from research settings to real-world extit{serving}, where request manag
arXiv:2610.01955v1 Announce Type: new Abstract: Outcome-based reinforcement learning can train language models to forecast real-world events, but prior forecasting work either freezes research context
arXiv:2610.01133v1 Announce Type: cross Abstract: Scaling reasoning typically spends more compute on reinforcement learning (RL) or on inference. We show that a completed RL training history can yield
arXiv:2606.19483v2 Announce Type: replace Abstract: Vision Foundation Models (VFMs) with Vision Transformer (ViT) backbones, such as DINOv2, have become essential for downstream tasks like object reco
arXiv:2610.00024v1 Announce Type: new Abstract: Across three vision-language model architectures (LLaVA-1.5-7B, Qwen2.5-VL-7B, InternVL3-8B), we report a universal negative finding for mid-layer inter
arXiv:2511.06311v2 Announce Type: replace Abstract: We present a tactile sensing method enabled by the mechanical compliance of soft robots; an externally attachable photoreflective module reads surfa
arXiv:2603.08961v2 Announce Type: replace Abstract: Maintaining balance under external hand forces is critical for humanoid bimanual manipulation, where interaction forces propagate through the kinema
arXiv:2610.01967v1 Announce Type: new Abstract: As large language models (LLMs) keep growing in size and complexity, their training frameworks evolve at a rapid pace as well. Therefore, continuous int
arXiv:2610.00858v1 Announce Type: new Abstract: Background and Objective: Clinicians expect recurrence risk to climb with cancer severity. In a UK multicentre trial, an unconstrained XGBoost model lea
arXiv:2610.01872v1 Announce Type: cross Abstract: While the literature on blockchain-assisted intrusion detection and prevention systems (IDS/IPS) for Internet of Things (IoT) and Industrial Internet
arXiv:2610.00015v1 Announce Type: new Abstract: Large-language-model agents can propose and execute actions, but proposal, authority, dispatch, verified external effect, and serving promotion are diff
arXiv:2607.16956v2 Announce Type: replace Abstract: Social navigation requires the robot to reason and respond in complex real-world environments. While recent works attempt to incorporate human-level
arXiv:2610.00978v1 Announce Type: cross Abstract: Time-series anomaly detection (TSAD) is difficult to generalize across datasets because heterogeneous temporal dynamics imply different notions of nor
arXiv:2610.00821v1 Announce Type: new Abstract: Simulation can expand scarce real demonstrations for co-training, yet how world fidelity and similarity to human behavior affect policy performance rema
arXiv:2610.01023v1 Announce Type: cross Abstract: Coding agents can return plausible patches that omit required behavior. These failures are hard to review because long traces and confident summaries
Enterprise AI agents need persistent memory to execute complex, multi-day workflows and long-horizon tasks. In this blog, we examine how a 2-tier memory architecture using Memorystore for Valkey for s
arXiv:2610.01231v1 Announce Type: cross Abstract: Generative artificial intelligence has reduced the cost of producing plausible symbolic artefacts, leading recent organisation scholarship to identify
arXiv:2603.16065v3 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has shown strong potential for improving robotic manipulation policies, yet its practical use remains bottlenecked
arXiv:2610.01364v1 Announce Type: cross Abstract: Factories are shifting toward smaller lot sizes with high product customization, requiring frequent re-programming of flexible and reconfigurable auto
arXiv:2610.01936v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable fluency across many tasks but remain limited by their static, parameter bound knowledge and th
arXiv:2610.01284v1 Announce Type: cross Abstract: Model validation estimates the performance of a complete learning procedure on new data. However, an invalid split can produce an optimistic and stabl
arXiv:2510.06105v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly shaping how information is created and disseminated, from companies using them to craft persuasive adv
arXiv:2610.00367v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models enable parameter scaling with limited per-token computation by activating only a small subset of experts for each token,
arXiv:2610.00557v1 Announce Type: cross Abstract: Autonomous red team agents increasingly stress-test AI-enabled cyber defenses by planning strategy and executing multistage attacks. Reinforcement lea
arXiv:2610.01413v1 Announce Type: cross Abstract: Reinforcement learning (RL) algorithms frequently compare probability distributions, such as state visitation distributions induced by policies and ex
arXiv:2610.00912v1 Announce Type: new Abstract: Large language models can translate business descriptions into optimization models, but executable code may misrepresent constraints or objectives. A so
arXiv:2610.01349v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents turn generated text into real side effects, so poisoned tool metadata, retrieved pages, memory, and reusa
arXiv:2610.01143v1 Announce Type: cross Abstract: Despite strong mean accuracy, tabular foundation models (TFMs) can perform poorly on underrepresented groups under subpopulation shift, where group pr
arXiv:2512.05990v2 Announce Type: replace Abstract: Memory consolidation determines both what a learner can do now and which changes remain implementable later. We develop a finite-model synthesis of
arXiv:2610.00065v1 Announce Type: cross Abstract: Legible planning is the creation of plans that best disambiguate their goals from a set of other candidates from an observer's perspective. In this pa
arXiv:2610.01781v1 Announce Type: new Abstract: Reinforcement learning (RL) for reachability specifications is fundamental to sequential decision-making. Prior work establishes asymptotic convergence
arXiv:2610.02204v1 Announce Type: cross Abstract: Building reliable robot capabilities across diverse tasks requires substantial human effort to develop and maintain skills, design rewards, and integr
arXiv:2610.01178v1 Announce Type: new Abstract: Manipulation failures can leave scenes in states from which a task policy cannot recover. Learning corrective behaviors requires scalable failure explor
arXiv:2610.02033v1 Announce Type: new Abstract: Individual mobility trajectories support urban analysis and location-based services, yet most trajectory generators require observations from their depl
arXiv:2610.00873v1 Announce Type: cross Abstract: In many industrial applications, 1) tabular data is scarce and imbalanced and thus requires synthetic expansion; 2) input distributions drift between
arXiv:2610.00715v1 Announce Type: new Abstract: Preference-based alignment methods typically optimize against a single preference model, and can therefore be brittle when pairwise preferences are unce
arXiv:2610.00067v1 Announce Type: new Abstract: Reliable visual inspection is essential for quality assurance in aero-engine blade manufacturing, where defect appearance may vary across production lin
arXiv:2610.01278v1 Announce Type: new Abstract: Alzheimer's disease (AD) diagnosis requires sequential evidence acquisition under heterogeneous test costs and patient burden. Fixed-modality predictors
The Adjudicated Query pattern pairs the Amazon Quick chat agent with a bounded MCP server over a deterministic rules engine to deliver provably complete, defensible compliance answers. This post walks
arXiv:2610.00638v1 Announce Type: new Abstract: World action models improve robotic manipulation by conditioning actions on predicted futures, yet existing tactile variants largely inherit video-gener
arXiv:2610.01768v1 Announce Type: cross Abstract: With the increasing capabilities of Large-Language-Models (LLMs) and LLM-based agents, users are increasingly using them to solve everyday problems, s
arXiv:2610.01647v1 Announce Type: cross Abstract: In this article, we present our vision and recent advancements toward creating a decentralized system capable of learning from real-time data within b
arXiv:2610.01887v1 Announce Type: cross Abstract: Dynamic resource assignment, the real-time allocation of task streams to heterogeneous processing nodes, is the backbone of modern computing infrastru
arXiv:2610.01959v1 Announce Type: cross Abstract: Path finding and multi-robot motion planning require trajectories that are smooth, goal-directed, and collision-free in environments with complex geom
arXiv:2610.00812v1 Announce Type: cross Abstract: Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamics
arXiv:2610.00350v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) offer an energy-efficient approach to processing event-camera data, yet out-of-distribution (OOD) detection remains chall
arXiv:2505.08894v2 Announce Type: replace-cross Abstract: Large language model (LLM) chatbots are increasingly reaching users through messaging platforms (e.g. WhatsApp). However, these systems remain
arXiv:2609.33153v2 Announce Type: replace-cross Abstract: Reported improvements from tools and reusable skills in large language model agents refer to different comparisons. This critical narrative re
arXiv:2610.00432v1 Announce Type: cross Abstract: Trellis-coded quantization enables high-dimensional compression of large language model (LLM) weights at ultra-low bit widths without the exponentiall
arXiv:2609.38421v1 Announce Type: new Abstract: Dynamical Systems (DS) are reactive motion policies representing vector fields trained with theoretical guarantees of stability and convergence. To ensu
arXiv:2609.38864v1 Announce Type: new Abstract: Embodied tasks demand accurate, flexible, and semantically rich 3D scene representations. 3D semantic occupancy is well suited to this requirement, as i
arXiv:2609.39478v1 Announce Type: new Abstract: This study systematically evaluates self-evaluation-based uncertainty quantification across different language models of varying sizes on question-answe