Knowledge catalogue
arXiv:2605.11989v1 Announce Type: new Abstract: Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by
arXiv:2605.12197v1 Announce Type: new Abstract: Leveraging Graph Neural Networks (GNNs) as graph encoders and aligning the resulting representations with Large Language Models (LLMs) through alignment
arXiv:2605.11224v1 Announce Type: new Abstract: Existing medical-agent benchmarks deliver imaging as pre-selected samples, never as an environment the agent must navigate. We introduce ABRA, a radiolo
arXiv:2605.10956v1 Announce Type: cross Abstract: Machine-learned surrogate modeling of advection may accelerate geoscientific models, but existing approaches have either achieved limited speedup or h
arXiv:2605.11009v1 Announce Type: new Abstract: Long-horizon, sparse-reward tasks pose a fundamental challenge for reinforcement learning, since single-step TD learning suffers from bootstrapping erro
arXiv:2602.06339v2 Announce Type: replace Abstract: Robot Foundation Models, such as VLAs, promise end-to-end generative robot policies with broad generalization. Yet it remains unclear whether they f
arXiv:2506.02215v5 Announce Type: replace Abstract: Collision avoidance -- involving a rapid threat detection and quick execution of the appropriate evasive maneuver -- is a critical aspect of driving
arXiv:2605.11398v1 Announce Type: cross Abstract: We introduce AcuityBench, a benchmark for evaluating whether language models identify the appropriate urgency of care from user medical presentations.
arXiv:2605.11581v1 Announce Type: new Abstract: When large language models (LLMs) serve real-time inference in commercial online advertising systems, end-to-end latency must be strictly bounded to the
arXiv:2605.11490v1 Announce Type: new Abstract: Making calibrated online predictions is a central challenge in modern AI systems. Much of the existing literature focuses on fully adversarial environme
arXiv:2211.03524v2 Announce Type: replace Abstract: Modern Review Helpfulness Prediction systems are dependent upon multiple modalities, typically texts and images. Unfortunately, those contemporary a
arXiv:2605.11191v1 Announce Type: cross Abstract: Adaptive experimentation under unknown network interference requires solving two coupled problems: (i) learning the underlying dynamics of interferenc
arXiv:2605.11880v1 Announce Type: new Abstract: TD(lambda) in value-based MARL algorithms or the Temporal Difference critic learning in Actor-Critic-based (AC-based) algorithms synergistically integra
arXiv:2605.11458v1 Announce Type: cross Abstract: On-policy self-distillation has become a strong recipe for LLM reasoning, where a privileged teacher supervises the student's own rollouts while condi
arXiv:2605.11222v1 Announce Type: new Abstract: Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (PTQ)
arXiv:2506.02084v2 Announce Type: replace Abstract: We address the problem of generating simulated, yet realistic, time-series data from a causal model with the same observational and interventional d
arXiv:2605.03629v1 Announce Type: cross Abstract: Distributed quantum algorithms offer a promising pathway to scale variational quantum algorithms beyond the constraints of noisy intermediate-scale qu
arXiv:2605.10987v1 Announce Type: new Abstract: Modern machine learning deployments increasingly compose specialized models into dynamic inference pipelines, where upstream components produce intermed
arXiv:2605.12375v1 Announce Type: new Abstract: Accurate crop yield forecasting in commercial soft fruit production is constrained by the data available in typical commercial farm records, which lack
arXiv:2605.11436v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed on long-horizon tasks in partially observable environments, where they must act while inferring a
arXiv:2605.11732v1 Announce Type: cross Abstract: In this paper, we present AgentDisCo, a novel Disentangled and Collaborative agentic architecture that formulates deep research as an adversarial opti
arXiv:2605.11026v1 Announce Type: cross Abstract: Defenses against indirect prompt injection (IPI) in tool-using LLM agents share two structural weaknesses. First, they all attempt to prevent attacks
arXiv:2511.22663v5 Announce Type: replace Abstract: Unified multimodal models for image generation and understanding represent a significant step toward AGI and have attracted widespread attention fro
arXiv:2605.12416v1 Announce Type: new Abstract: Generative policies based on expressive model classes, such as diffusion and flow matching, are well-suited to complex control problems with highly mult
arXiv:2605.11727v1 Announce Type: cross Abstract: Vision-language models typically reason over post-ISP RGB images, although RGB rendering can clip, suppress, or quantize sensor evidence before infere
arXiv:2605.10949v1 Announce Type: cross Abstract: Malaria, childhood acute respiratory infection, and child undernutrition together account for over two million deaths annually in children under five,
arXiv:2605.12495v1 Announce Type: new Abstract: In this paper, we propose AlphaGRPO, a novel framework that applies Group Relative Policy Optimization (GRPO) to AR-Diffusion Unified Multimodal Models
arXiv:2605.11378v1 Announce Type: new Abstract: Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive
arXiv:2601.21351v3 Announce Type: replace Abstract: Attentio-FFN disaggregation (AFD) is an emerging architecture for LLM decoding that separates state-heavy, KV-cache-dominated Attention computation
arXiv:2605.12069v1 Announce Type: new Abstract: Zero-shot anomaly detection aims to identify defects in unseen categories without target-specific training. Existing methods usually apply the same feat
arXiv:2605.11609v1 Announce Type: cross Abstract: On-policy self-distillation, where a student is pulled toward a copy of itself conditioned on privileged context (e.g., a verified solution or feedbac
arXiv:2605.12430v1 Announce Type: new Abstract: Segmentation models in automated optical inspection of wire-bonded semiconductors are typically device-specific and must be re-trained when new devices
arXiv:2505.17907v2 Announce Type: replace-cross Abstract: Properties of Fisher information matrices of 2-layer neural ReLU networks with random hidden weights are studied. For these networks, it is kn
arXiv:2605.12301v1 Announce Type: new Abstract: Operator learning has been highly successful for continuous mappings between infinite-dimensional spaces, such as PDE solution operators. However, many
arXiv:2605.12025v1 Announce Type: new Abstract: Neural operators provide a framework for learning solution operators of partial differential equations (PDEs), enabling efficient surrogate modeling for
arXiv:2510.18071v2 Announce Type: replace-cross Abstract: Matching-adjusted indirect comparison (MAIC) has been increasingly employed in health technology assessments (HTA). By reweighting subjects fr
arXiv:2605.11324v1 Announce Type: new Abstract: We study the fixed-budget max-min action identification problem in depth-2 max-min trees, an important special case of Monte Carlo Tree Search. A learne
arXiv:2605.11091v1 Announce Type: new Abstract: Automated ASD screening tools remain limited by single-architecture evaluations, axis-restricted assessment, and near-exclusive focus on adult cohorts,
arXiv:2605.11119v1 Announce Type: new Abstract: Indoor infrastructure inspection, such as tunnels and industrial facilities, requires systematic surface coverage to ensure that all inspection targets
arXiv:2605.11968v1 Announce Type: cross Abstract: Modern Earth System Models (ESMs) operate on horizontal scales far larger than typical cloud features, requiring stochastic subcolumn generators to re
arXiv:2604.04894v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning ability of large language models (LLMs), but it often
arXiv:2605.12364v1 Announce Type: cross Abstract: Agentic AI governance is a critical component of agentic AI infrastructure ensuring that agents follow their owner's communication and interaction pol
arXiv:2512.12165v3 Announce Type: replace Abstract: Understanding camera motion is a fundamental problem in embodied perception and 3D scene understanding. While visual methods have advanced rapidly,
arXiv:2605.11694v1 Announce Type: new Abstract: We study policy optimization for infinite-horizon, discounted constrained Markov decision processes (CMDPs). While existing theoretical guarantees typic
arXiv:2605.11518v1 Announce Type: cross Abstract: Effectively configuring scalable large language model (LLM) experiments, spanning architecture design, hyperparameter tuning, and beyond, is crucial f
arXiv:2601.05752v3 Announce Type: replace Abstract: We introduce AutoMonitor-Bench, the first benchmark designed to systematically evaluate the reliability of LLM-based misbehavior monitors across div
arXiv:2605.12316v1 Announce Type: new Abstract: We study the fundamental and timely problem of learning long sequences in autoregressive modeling and next-token prediction under model misspecification
arXiv:2508.05269v2 Announce Type: replace Abstract: Understanding dynamic outdoor environments requires capturing complex object interactions and their evolution over time. LiDAR-based 4D point clouds
arXiv:2605.11014v1 Announce Type: new Abstract: Fair comparison between diffusion-based OOD detectors is challenging, as conclusions can vary with backbone choice, corruption parameterization, and tes
arXiv:2605.12074v1 Announce Type: new Abstract: Scene understanding is central to general physical intelligence, and video is a primary modality for capturing both state and temporal dynamics of a sce
arXiv:2412.11875v3 Announce Type: replace-cross Abstract: Surrogate models are often used as computationally efficient approximations to complex simulation models, enabling tasks such as solving inver
arXiv:2412.05225v3 Announce Type: replace Abstract: Large Language Models (LLMs) based on transformers achieve cutting-edge results on a variety of applications. However, their enormous size and proce
arXiv:2605.11387v1 Announce Type: new Abstract: We address the problem of fine-tuning pre-trained generative policies with reinforcement learning (RL) while preserving the multimodality of their actio
arXiv:2605.12483v1 Announce Type: new Abstract: In settings where labeled verifiable training data is the binding constraint, each checked example should be allocated carefully. The standard practice
arXiv:2605.12413v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) show strong visual perception, yet remain limited in reasoning about space under changing viewpoints. We study
arXiv:2605.11221v1 Announce Type: cross Abstract: Predictive models in biomedicine depend on structured assay data locked in the text, tables, and supplements of primary publications. This bottleneck
arXiv:2605.11438v1 Announce Type: new Abstract: Medical imaging research has spent a decade getting very good at one thing: producing per-voxel masks. Masks tell us size, volume, and location, and a d
arXiv:2605.11857v1 Announce Type: new Abstract: Federated fine-tuning of large language models is commonly formulated as a parameter aggregation problem. However, even parameter-efficient methods requ
arXiv:2501.16931v2 Announce Type: replace Abstract: Machine learning models are often evaluated using point estimates of performance metrics such as accuracy, F1 score, or mean squared error. Such sum
arXiv:2605.11904v1 Announce Type: new Abstract: The Nearest Class Mean (NCM) classifier is widely favored in Class-Incremental Learning (CIL) for its superior resistance to catastrophic forgetting com