Knowledge catalogue
arXiv:2605.11748v1 Announce Type: new Abstract: Bronchoscopy is routinely conducted in pulmonary clinics and intensive care units, but navigating the complex branching of the respiratory tract remains
arXiv:2605.12339v1 Announce Type: new Abstract: Aligning language models for both helpfulness and safety typically requires complex pipelines-separate reward and cost models, online reinforcement lear
arXiv:2605.11723v1 Announce Type: new Abstract: In this paper, we propose Concentrate and Concentrate (CaC), a coarse-to-fine anomaly reward model based on Vision-Language Models. During inference, it
arXiv:2605.12266v1 Announce Type: new Abstract: Graph-based machine learning has emerged as a promising approach for manufacturability analysis by learning directly from CAD models represented as Boun
arXiv:2511.12034v2 Announce Type: replace Abstract: Multimodal representation learning harmonizes distinct modalities by aligning them into a unified latent space. Recent research generalizes traditio
arXiv:2605.11442v1 Announce Type: cross Abstract: Large Language Model (LLM) agents have emerged as key intermediaries, orchestrating complex interactions between human users and a wide range of digit
arXiv:2605.11300v1 Announce Type: new Abstract: Vision state space models inherit the efficiency and long-range modeling ability of Mamba-style selective scans. However, their performance depends crit
arXiv:2604.03061v2 Announce Type: replace Abstract: Recent advances in generative AI raise the question of whether general-purpose image editing models can serve as unified solutions for image restora
arXiv:2605.12028v1 Announce Type: new Abstract: We describe our system for SemEval-2026 Task 8 (MTRAGEval), participating in Task A (Retrieval) across four English-language domains. Our approach emplo
arXiv:2605.11705v1 Announce Type: new Abstract: The training of large multimodal models fundamentally relies on massive image-text datasets, which inevitably incur prohibitive computational overhead.
arXiv:2605.11186v1 Announce Type: new Abstract: Auto-regressive decoding in Large Language Models (LLMs) is inherently memory-bound: every generation step requires loading the model weights and interm
arXiv:2605.11373v1 Announce Type: cross Abstract: The trustworthiness of AI decision-making systems is increasingly important. A key feature of such systems is the ability to provide recommendations f
arXiv:2605.11365v1 Announce Type: cross Abstract: Automated systems built on artificial intelligence (AI) are increasingly deployed across high-stakes domains, raising critical concerns about fairness
arXiv:2605.11362v1 Announce Type: new Abstract: In the data-driven era, large-scale datasets are routinely collected and analyzed using machine learning (ML) and artificial intelligence (AI) to inform
arXiv:2605.12496v1 Announce Type: new Abstract: Autoregressive video generation aims at real-time, open-ended synthesis. Yet, cinematic storytelling is not merely the endless extension of a single sce
arXiv:2602.09368v2 Announce Type: replace Abstract: Gradient-based methods can efficiently optimize controllers by leveraging differentiable simulation and physical priors. However, contact-rich manip
arXiv:2505.05665v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently demonstrated success in decision-making tasks including planning, control, and prediction, but thei
arXiv:2605.11533v1 Announce Type: new Abstract: Clinical check-up reports are multimodal documents that combine page layouts, tables, numerical biomarkers, abnormality flags, imaging findings, and dom
arXiv:2605.11304v1 Announce Type: new Abstract: Chest radiograph interpretation requires temporal reasoning over prior and current studies, yet most vision-language models are trained on static image-
arXiv:2605.11779v1 Announce Type: new Abstract: Multiword expressions (MWEs) are a heterogeneous set with a glaring need for classifications. Designing a satisfactory classification involves choosing
arXiv:2605.11960v1 Announce Type: new Abstract: Vision Large Language Models (VLLMs) have achieved remarkable success in modern text-rich visual understanding. However, their perceptual robustness in
arXiv:2605.11335v1 Announce Type: cross Abstract: Layerwise offloading reduces the GPU memory footprint of large diffusion transformer (DiT) inference by prefetching upcoming layers from host memory,
arXiv:2601.21944v2 Announce Type: replace Abstract: The widespread adoption of deep learning models in computer vision has intensified concerns about interpretability. Despite strong performance, thes
arXiv:2605.11143v1 Announce Type: new Abstract: Reasoning benchmarks measure clinical performance on clean inputs. We evaluate the step before reasoning: retrieval over real EHR notes, where negation,
arXiv:2605.12053v1 Announce Type: new Abstract: This paper addresses the Motion Execution Gap, the disconnect between high-level symbolic task descriptions using semantic constraints and executable ro
arXiv:2605.11939v1 Announce Type: new Abstract: Prompt learning has emerged as an efficient alternative to fine-tuning pre-trained vision-language models (VLMs). Despite its promise, current methods s
arXiv:2510.03206v2 Announce Type: replace-cross Abstract: Diffusion language models, especially masked discrete diffusion models, have achieved great success recently. While there are some theoretical
arXiv:2605.12227v1 Announce Type: new Abstract: Adapting large language models (LLMs) to long-context tasks requires post-training methods that remain accurate and coherent over thousands of tokens. E
arXiv:2605.11691v1 Announce Type: new Abstract: Partial differential equations (PDEs) govern diverse physical phenomena, yet high-fidelity numerical solutions are computationally expensive and Machine
arXiv:2605.11296v1 Announce Type: new Abstract: We introduce Phantom Twist, a type of single-propeller UAV designed to achieve low visibility through high-speed spinning and the exploitation of motion
arXiv:2509.20899v3 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) enable interpretable image classification by structuring predictions around human-understandable concepts, but exte
arXiv:2605.11862v1 Announce Type: new Abstract: Named Entity Recognition for person names is an important but non-trivial task in information extraction. This article uses a tool that compares the con
arXiv:2605.11850v1 Announce Type: cross Abstract: In this work, we develop proximal preconditioned gradient methods with a focus on spectral gradient methods providing a proximal extension to the Muon
arXiv:2605.12370v1 Announce Type: new Abstract: While Large Language Models (LLMs) are widely used in open-domain Question Answering (QA), their ability to handle inferential questions-where answers m
arXiv:2508.14780v2 Announce Type: replace Abstract: Compression-based dissimilarities (CD) offer a flexible and domain-agnostic means of measuring similarity by identifying implicit information throug
arXiv:2605.11202v1 Announce Type: cross Abstract: LLM inference and serving systems have become security-critical infrastructure; however, many of their most concerning failures arise from the serving
arXiv:2605.12320v1 Announce Type: new Abstract: Learning robust representations of polyp tracklets is key to enabling multiple AI-assisted colonoscopy applications, from polyp characterization to auto
arXiv:2605.11135v1 Announce Type: cross Abstract: Generative agents have proven to be powerful assistants in a wide variety of contexts. Given this success, users are now deploying agents with minimal
arXiv:2605.12071v1 Announce Type: new Abstract: Fully actuated multirotor platforms decouple translational force generation from vehicle attitude, enabling independent control of position and orientat
arXiv:2605.11519v1 Announce Type: cross Abstract: Using offline datasets to evaluate conversational agents often fails to cover rare scenarios or to support testing new policies. This has motivated th
arXiv:2605.11972v1 Announce Type: new Abstract: Collisions at non-line-of-sight (NLOS) intersections remain a major safety concern because drivers have limited visibility of approaching traffic. V2X b
arXiv:2605.11485v1 Announce Type: new Abstract: Imitation learning powered by generative models has proven effective for modeling complex single-agent behaviors. However, teaching multi-agent systems,
arXiv:2605.11159v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to automatically infer missing facts in multi-relational data by mapping entities and relations into continuous re
arXiv:2605.12177v1 Announce Type: new Abstract: [Abridged] Production LLM deployments receive feedback from a non-random fraction of users: thumbs sit mostly in the tails of the satisfaction distribut
arXiv:2605.11165v1 Announce Type: new Abstract: Federated learning (FL) in heterogeneous environments remains challenging because client models often differ in both architecture and data distribution.
arXiv:2605.11311v1 Announce Type: cross Abstract: Diffusion models typically generate image batches from independent Gaussian initial noises. We argue that this independence assumption is only one cho
arXiv:2603.28488v2 Announce Type: replace Abstract: Large language models (LLMs) remain unreliable for high-stakes claim verification due to hallucinations and shallow reasoning. While retrieval-augme
arXiv:2605.12501v1 Announce Type: new Abstract: Computer-use agents (CUAs) automate on-screen work, as illustrated by GPT-5.4 and Claude. Yet their reliability on complex, low-frequency interactions i
arXiv:2605.11784v1 Announce Type: cross Abstract: Full-vehicle crash simulations are computationally expensive, limiting their use in iterative design exploration. This work investigates learned hybri
arXiv:2605.12145v1 Announce Type: new Abstract: Multimodal learning seeks to integrate information across diverse sensory sources, yet current approaches struggle to balance cross-modal generalizabili
arXiv:2605.11504v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have enabled agentic systems for complex, multi-step tasks; cybersecurity is emerging as a prominent app
arXiv:2605.11260v1 Announce Type: new Abstract: Knowledge distillation is a key technique for transferring the capabilities of large language models (LLMs) into smaller, more efficient student models.
arXiv:2509.14933v3 Announce Type: replace Abstract: Time series forecasting is essential in various domains. Compared to relying solely on endogenous variables (i.e., target variables), considering ex
arXiv:2512.24985v4 Announce Type: replace Abstract: Vision Language Models (VLMs) are increasingly adopted as central reasoning modules for embodied agents. Existing benchmarks evaluate their capabili
arXiv:2605.11711v1 Announce Type: new Abstract: Model-based representations recently stand out as a promising framework that embeds latent dynamics information into the representations for downstream
arXiv:2605.11145v1 Announce Type: cross Abstract: Collaborative filtering (CF) models based on graph neural networks (GNNs) achieve strong performance in recommender systems by propagating user-item s
arXiv:2605.11153v1 Announce Type: new Abstract: We decompose an evolutionary mixture-of-LoRA system on a from-scratch ~150M-parameter widened-D substrate (D=1536, V=32000; D/V approx 0.048; the 'widen
arXiv:2605.11764v1 Announce Type: new Abstract: Machine-learning predictors of biochemical activity often exhibit large random-split-to-leave-one-target-out generalisation gaps that have been document
arXiv:2605.11237v1 Announce Type: new Abstract: Despite the burgeoning body of work on distribution shifts, provenance shift-where the relationship between data source and label changes at deployment-
arXiv:2605.11189v1 Announce Type: new Abstract: Accurately modeling and designing protein complex structures is a central problem in computational structural biology, with broad implications for under