Knowledge catalogue
arXiv:2608.01629v1 Announce Type: new Abstract: Large language models (LLMs) increasingly evaluate human writing in high-stakes domains such as hiring and academic assessment, putting non-native speak
arXiv:2608.00537v1 Announce Type: new Abstract: Latent Flow Models have revolutionized compressed-space image synthesis, yet their application to high-fidelity inverse problems remains bottlenecked. I
arXiv:2608.02110v1 Announce Type: new Abstract: Executing long-horizon tool invocations in real-world environments is severely challenged by dynamic user intent noise. Existing methods attempt robustn
arXiv:2608.00175v1 Announce Type: new Abstract: Deep reinforcement learning (RL) agents achieve strong performance by optimizing scalar reward functions. However, once deployed, the policies of these
arXiv:2608.02087v1 Announce Type: cross Abstract: Post-training Large Language Models (LLMs) with Reinforcement Learning (RL) has become an important tool for improving model capabilities, but the LLM
arXiv:2606.13485v3 Announce Type: replace-cross Abstract: Safe rehabilitation is an interaction-dynamics problem: the controller must regulate a prescribed motion while absorbing involuntary spasm, vo
arXiv:2608.01780v1 Announce Type: new Abstract: Text-to-image (T2I) generation models are increasingly embedded in applications such as media content creation and education, raising concerns about how
arXiv:2608.02018v1 Announce Type: new Abstract: Computer-use agents (CUAs), which empower large language models to autonomously operate operating systems and the web, are increasingly vulnerable to in
arXiv:2608.01879v1 Announce Type: new Abstract: Tabular data generation supports analysis and decision-making when target-domain data are scarce, yet collecting complete target samples is often costly
arXiv:2608.01599v1 Announce Type: new Abstract: Volatility forecasts are commonly evaluated with aggregate accuracy metrics such as RMSE and MAE, but these metrics can hide conditional failures that m
arXiv:2608.01804v1 Announce Type: new Abstract: Post-training large language models (LLMs) via reinforcement learning (RL) has significantly advanced code generation capabilities. To bypass the heavy
arXiv:2608.02031v1 Announce Type: cross Abstract: This paper investigates collaborative mobile edge computing (MEC) servers for large language model (LLM) inference under soft deadline constraints. In
arXiv:2608.00625v1 Announce Type: new Abstract: Motion planning in dynamic environments is a fundamental problem in robotics, aiming to generate safe and efficient paths, trajectories, or control acti
arXiv:2608.01151v1 Announce Type: cross Abstract: In this paper, we consider stochastic optimal control problems with infinite-horizon joint chance constraints. By means of an appropriate state augmen
arXiv:2608.00231v1 Announce Type: new Abstract: Volumetric CT vision-language pretraining learns 3D representations from scan-report pairs, but global and anatomy-aware objectives supervise only corre
arXiv:2608.00908v1 Announce Type: cross Abstract: Modern network policy control maps intent to sequential placement-control decisions. Bellman-style policy optimization primarily asks which action to
arXiv:2608.02257v1 Announce Type: new Abstract: Mobile manipulation is a key capability for embodied intelligence, enabling robots to accomplish complex multi-stage tasks in open-world environments. H
arXiv:2608.00279v1 Announce Type: cross Abstract: Recent radiology-adapted vision-language models have achieved strong performance on standard report generation benchmarks, yet their robustness and ge
arXiv:2608.00123v1 Announce Type: new Abstract: LLM-native advertising embeds sponsored content directly into model-generated responses, shifting the unit of sale from a fixed slot to a moment within
arXiv:2510.20449v2 Announce Type: replace Abstract: Instruction tuning is crucial for aligning Large Language Models (LLMs), yet the quality of instruction-following data varies significantly. While h
arXiv:2509.14431v2 Announce Type: replace Abstract: Multi-agent reinforcement learning (MARL) policies for swarm control often learn inefficiently and generalize poorly across coordinate frames, team
arXiv:2608.00272v1 Announce Type: new Abstract: This paper proposes a localization framework that uses spatiotemporal fields governed by partial differential equations (PDEs) as localization signature
arXiv:2608.01953v1 Announce Type: new Abstract: On-policy distillation (OPD) provides teacher supervision on states visited by the student, reducing the distribution gap between training and inference
arXiv:2608.00743v1 Announce Type: new Abstract: Multimodal large language models have advanced visual understanding, yet perception-intensive reasoning remains challenging. Recent latent visual reason
arXiv:2608.01128v1 Announce Type: cross Abstract: In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks. For time-critical rescue m
arXiv:2608.00195v1 Announce Type: cross Abstract: High-resolution 3D segmentation of hip and shoulder anatomy from CT and MRI is essential for surgical planning, yet frozen segmentation models often f
arXiv:2608.01012v1 Announce Type: new Abstract: Uncommon and off-guideline cases are difficult for clinical decision support, because physicians must make a series of management decisions under diagno
arXiv:2608.01563v1 Announce Type: new Abstract: Training and deployed inference often cross export, conversion, and platform-specific runtime boundaries. Meganeura asks whether one compact native comp
arXiv:2603.18494v2 Announce Type: replace Abstract: Memory-augmented robotic policies are essential in handling memory-dependent tasks. However, existing approaches typically rely on simply extending
arXiv:2608.00973v1 Announce Type: new Abstract: Text-to-image (T2I) systems typically have prompt-level safety filters before the generator to block unsafe requests, yet such systems remain vulnerable
arXiv:2608.01635v1 Announce Type: new Abstract: Despite the progress of multimodal large language models (MLLMs), they continue to exhibit deficiencies in visual perception. Following visual instructi
arXiv:2606.15782v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-language understanding and natural-language response
arXiv:2604.02817v2 Announce Type: replace Abstract: Despite advancements in generating visually stunning content, video diffusion models (VDMs) often yield physically inconsistent results due to pixel
arXiv:2608.00975v1 Announce Type: new Abstract: Large vision--language models (VLMs) can reason step by step about complex visual scenes, but this open-ended, autoregressive chain-of-thought (CoT) app
arXiv:2608.01775v1 Announce Type: new Abstract: Compound flooding in managed coastal systems is influenced by hydrological conditions and water-management activity observed across multiple monitoring
arXiv:2603.14686v2 Announce Type: replace Abstract: Human-Object Interaction (HOI) video reenactment aims to transfer the interaction dynamics of a source video to a novel target object while preservi
arXiv:2608.00533v1 Announce Type: new Abstract: Large Language Models have achieved substantial progress in reasoning capabilities. Yet in low-resource native settings, many suffer from cross-lingual
arXiv:2511.19849v2 Announce Type: replace-cross Abstract: Recurrence objectives, where a target region must be visited infinitely often, are a fundamental class of specifications for Markov decision p
arXiv:2608.00320v1 Announce Type: new Abstract: Autonomous spacecraft swarms must plan fuel-efficient, collision-free maneuvers in increasingly congested orbits, yet classical trajectory optimization
arXiv:2608.00752v1 Announce Type: new Abstract: Clinical acquisition in cardiac magnetic resonance (CMR) imaging involves obtaining cross-sectional planes of the heart along the radial and longitudina
arXiv:2608.01868v1 Announce Type: cross Abstract: WOPR is a social-simulation environment for studying how organizations make high-stakes decisions, built on a deterministic, replay-validated rules en
arXiv:2602.13362v2 Announce Type: replace-cross Abstract: A key challenge in probabilistic regression is ensuring that predictive distributions accurately reflect true empirical uncertainty. Minimizin
arXiv:2608.00048v1 Announce Type: cross Abstract: Electroencephalography (EEG) generation is essential for alleviating data scarcity and enabling large scale neural modeling in brain computer interfac
arXiv:2607.18295v2 Announce Type: replace Abstract: We study whether alignment schemes that reshape a base model's output distribution, combined with bounded safety filters, can drive the probability
arXiv:2608.00978v1 Announce Type: cross Abstract: Flow Matching trains continuous-time generative models by regressing the velocity field of a probability path between a simple source distribution and
arXiv:2608.02509v1 Announce Type: cross Abstract: Sequential decision-making in real-world applications often involves uncertainty about the environment's model. Uncertain Markov decision processes (U
arXiv:2608.00775v1 Announce Type: cross Abstract: ORCESTRA is a mixed-reality system for programming robot digital twins through no-code waypoint teaching and language-guided control. In a passthrough
arXiv:2603.11804v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) adapted to remote sensing rely heavily on domain-specific image-text supervision, yet high-quality annotations for sat
arXiv:2608.01458v1 Announce Type: new Abstract: Large language models are being extensively used to simulate individual user behavior, yet faithfully representing a population requires capturing the s
arXiv:2608.00855v1 Announce Type: cross Abstract: Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but o
arXiv:2608.01072v1 Announce Type: new Abstract: Autoregressive rigging models such as UniRig and SkinTokens perform well on articulated characters, but their ability to generalize to plant structures
arXiv:2608.01133v1 Announce Type: new Abstract: Evaluating Multi-Agent Reinforcement Learning (MARL) policies in autonomous driving fundamentally relies on extrinsic statistical indicators (e.g., rewa
arXiv:2608.00489v1 Announce Type: new Abstract: Single-photon avalanche diode (SPAD) cameras are promising for low-light and high-dynamic-range intensity imaging, but their practical use is limited by
arXiv:2608.02545v1 Announce Type: new Abstract: Reachability analysis for visuomotor policies is difficult because large visual encoders make end-to-end set propagation computationally expensive and e
arXiv:2608.00687v1 Announce Type: new Abstract: LiDAR point clouds provide explicit, deterministic physical boundaries critical for collaborative safety-critical perception. However, wireless channels
arXiv:2602.21429v3 Announce Type: replace Abstract: Flow-based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distribut
arXiv:2608.00730v1 Announce Type: new Abstract: Viscous stains, characterized by high viscosity and complex rheological properties, remain a major challenge for robotic surface cleaning. Conventional
arXiv:2508.08645v3 Announce Type: replace Abstract: As multimodal large language models advance rapidly, the automation of mobile tasks has become increasingly feasible through the use of mobile-use a
arXiv:2608.00147v1 Announce Type: new Abstract: Vision-language pretraining learns rich medical image representations from radiology reports, but previous model variants commonly operate within a sing
arXiv:2608.01332v1 Announce Type: cross Abstract: Scenario-tree model predictive control (MPC) represents future information by a rooted tree and optimizes a nonanticipative policy over that tree. Num