Knowledge catalogue
arXiv:2604.05190v2 Announce Type: replace-cross Abstract: Screening patients for enrollment is a well-known, labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures.
arXiv:2505.20414v2 Announce Type: replace-cross Abstract: Motion forecasts of road users (i.e., agents) vary in complexity depending on the number of agents, scene constraints, and interactions. In pa
arXiv:2604.00809v2 Announce Type: replace Abstract: Building on existing approaches, we revisit Human-in-the-Loop Object Retrieval, a task that consists of iteratively retrieving images containing obj
arXiv:2604.26130v1 Announce Type: cross Abstract: Every RLHF-trained language model is shaped by a reward model, yet the mechanistic interpretability toolkit -- logit lens, direct logit attribution, a
arXiv:2604.24966v1 Announce Type: cross Abstract: Frontier AI companies first deploy their most advanced models internally, for weeks or months of safety testing, evaluation, and iteration, before a p
arXiv:2603.07955v3 Announce Type: replace-cross Abstract: We develop a reinforcement learning pipeline for simplifying knot diagrams. A trained agent learns move proposals and a value heuristic for na
arXiv:2604.26496v1 Announce Type: new Abstract: Adversarial Training (AT) is one of the most effective methods for developing robust deep neural networks (DNNs). However, AT faces a trade-off problem
arXiv:2508.12672v4 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative model training across multiple clients without sharing private data. We consider FL scenarios wh
arXiv:2604.26128v1 Announce Type: cross Abstract: We consider learning from labeled data collected across multiple environments, where the data distribution may vary across these environments. This pr
arXiv:2509.07523v4 Announce Type: replace Abstract: Detecting rare events and anomalies in large-scale signals is essential in fields such as astronomy, physical simulations, and biomedical science. I
arXiv:2604.26833v1 Announce Type: cross Abstract: This paper presents a hierarchical decision-making framework for unmanned aerial vehicle (UAV) missions motivated by search-and-rescue (SAR) scenarios
arXiv:2604.26899v1 Announce Type: cross Abstract: Safe navigation in cluttered environments is an important challenge for autonomous systems. Robots navigating through obstacle ridden scenarios need t
arXiv:2604.26506v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly integrated into academic peer review, their vulnerability to adversarial prompts -- adversarial instruc
arXiv:2601.04389v2 Announce Type: replace-cross Abstract: Current safety evaluations of large language models (LLMs) create a dangerous illusion of universal protection by aggregating harms under gene
arXiv:2604.26630v1 Announce Type: new Abstract: Effective mental health counseling is a complex, theory-driven process requiring the simultaneous integration of psychological frameworks, real-time dis
arXiv:2604.26116v1 Announce Type: new Abstract: Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server whil
arXiv:2604.25936v1 Announce Type: cross Abstract: Implicit neural representations are powerful for geometric modeling, but their practical use is often limited by the high computational cost of networ
arXiv:2604.26645v1 Announce Type: new Abstract: AI-for-Science (AI4Science) is increasingly transforming scientific discovery by embedding machine learning models into prediction, simulation, and hypo
arXiv:2603.12249v2 Announce Type: replace-cross Abstract: Constructing scientific multimodal document reasoning datasets for foundation model training involves an inherent trade-off among scale, faith
arXiv:2512.18583v2 Announce Type: replace Abstract: Adversarial Imitation Learning (AIL) is a dominant framework in imitation learning that infers rewards from expert demonstrations to guide policy op
arXiv:2604.26883v1 Announce Type: new Abstract: Synthesizing a target concept from a single reference image is challenging in diffusion-based personalized text-to-image generation, particularly for st
arXiv:2604.26368v1 Announce Type: new Abstract: In several applications it is desired to have 3D models not only from the outdoor spaces but also from inside the building. In the context of First Resp
arXiv:2604.26394v1 Announce Type: cross Abstract: Recent advances in large language models and agentic frameworks have enabled virtual customer assistants (VCAs) for complex support. We present SecMat
arXiv:2604.26221v1 Announce Type: cross Abstract: Open-vocabulary semantic segmentation (OVSS) in remote sensing images is a promising task that employs textual descriptions for identifying undefined
arXiv:2604.26940v1 Announce Type: new Abstract: Small language models (SLMs) offer computational efficiency for scalable deployment, yet they often fall short of the reasoning power exhibited by their
arXiv:2510.20956v2 Announce Type: replace-cross Abstract: We discover a novel and surprising phenomenon of unintentional misalignment in reasoning language models (RLMs), which we call self-jailbreaki
arXiv:2502.14912v2 Announce Type: replace Abstract: We present a framework for generating universal semantic embeddings of chemical elements to advance materials inference and discovery. This framewor
arXiv:2604.26262v1 Announce Type: new Abstract: Modern scene reconstruction methods, such as 3D Gaussian Splatting, enable photo-realistic novel view synthesis at real-time speeds. However, their adop
arXiv:2604.26818v1 Announce Type: new Abstract: This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels
arXiv:2604.26375v1 Announce Type: cross Abstract: We describe our system for SemEval-2026 Task 6 (CLARITY: Unmasking Political Question Evasions), which classifies English political interview response
arXiv:2604.26355v1 Announce Type: new Abstract: Reasoning in Large Language Models incurs significant inference-time compute, yet the token-level information structure of reasoning traces remains unde
arXiv:2604.26057v1 Announce Type: cross Abstract: Supervised contrastive learning (SupCon) is widely used to shape representations, but has seen limited targeted study for audio deepfake detection. Ex
arXiv:2511.13285v2 Announce Type: replace Abstract: Artistic design, particularly poster design, often demands rapid yet precise modification of textual content while preserving visual harmony and typ
arXiv:2604.26620v1 Announce Type: new Abstract: Depth ambiguity and joint uncertainty are the two main obstacles in obtaining accurate human pose predictions by 2D-to-3D lifting methods proposed in th
arXiv:2604.25932v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly integrated into educational settings, understanding their potential biases is critical. This study ex
arXiv:2604.25937v1 Announce Type: cross Abstract: Recent advancements in Text-to-Song generation have enabled realistic musical content production, yet existing evaluation benchmarks lack the professi
arXiv:2511.03691v2 Announce Type: replace Abstract: Conventional fluid-driven soft grippers typically depend on external sources, which limit portability and long-term autonomy. This work introduces a
arXiv:2604.26132v1 Announce Type: cross Abstract: We aim to learn a sparse and connected graph from sparse data, where the number of observations K can be substantially smaller than the signal dimensi
arXiv:2604.26409v1 Announce Type: new Abstract: Sparse Autoencoders (SAEs) have demonstrated significant success in interpreting Large Language Models (LLMs) by decomposing dense representations into
arXiv:2604.26341v1 Announce Type: new Abstract: Recent unified image generation models have achieved remarkable success by employing MLLMs for semantic understanding and diffusion backbones for image
arXiv:2604.26133v1 Announce Type: new Abstract: Informal settlements face disproportionate exposure to climate-related health hazards. However, existing methodologies lack systematic approaches to lin
arXiv:2604.25925v1 Announce Type: new Abstract: Autoregressive language models suffer from high inference latency due to their sequential decoding nature. Speculative decoding (SD) mitigates this by e
arXiv:2604.25938v1 Announce Type: cross Abstract: Speech Emotion Recognition (SER) is the use of machines to detect the emotional state of humans based on the speech, which is gaining importance in na
arXiv:2604.26374v1 Announce Type: new Abstract: In multi-agent systems, should limited resources be concentrated into a few capable agents or distributed among many simpler ones? This work formulates
arXiv:2604.26388v1 Announce Type: cross Abstract: Federated Split Learning has been identified as an efficient approach to address the computational resource constraints of clients in classical federa
arXiv:2604.26626v1 Announce Type: new Abstract: Approximating collision-free space is fundamental to robot planning in complex environments. Convex geometric representations, such as polytopes and ell
arXiv:2604.26582v1 Announce Type: cross Abstract: Reliable celestial attitude determination is a critical requirement for autonomous spacecraft navigation, yet traditional 'Lost-in-Space' (LIS) algori
arXiv:2604.26500v1 Announce Type: new Abstract: LLMs and speech assistants are increasingly used for task-oriented interactions, yet their evaluation often relies on controlled scenarios that fail to
arXiv:2604.26848v1 Announce Type: new Abstract: Robotic manipulation critically requires reasoning about future spatial-temporal interactions, yet existing VLA policies and world-model-enhanced polici
arXiv:2604.26614v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved impressive progress on general multimodal tasks, yet they remain brittle on dial-based measuremen
arXiv:2604.26422v1 Announce Type: cross Abstract: Accurate end-to-end tail-latency forecasting is critical for proactive SLO management in microservice systems. However, modeling long-range dependency
arXiv:2604.26897v1 Announce Type: new Abstract: Origami-inspired robotic grippers have shown promising potential for object manipulation tasks due to their compact volume and mechanical flexibility. H
arXiv:2604.26898v1 Announce Type: cross Abstract: We prove pathwise convergence of the layerwise evolution of tokens in a finite-depth, finite-width transformer model with MultiLayer Perceptron (MLP)
arXiv:2604.26243v1 Announce Type: cross Abstract: Achieving realistic human-like conversation for virtual characters requires not only a simple memorization and recall of past events, but also the str
arXiv:2508.01875v4 Announce Type: replace Abstract: Real-time streaming video understanding in domains such as autonomous driving and intelligent surveillance poses challenges beyond conventional offl
arXiv:2511.07689v2 Announce Type: replace-cross Abstract: Evaluating the factual consistency of abstractive text summarization remains a significant challenge, particularly for long documents, where c
arXiv:2604.26157v1 Announce Type: cross Abstract: Structural generalization in semantic parsing requires systems to apply learned compositional rules to novel structural combinations. Existing approac
arXiv:2510.21828v2 Announce Type: replace-cross Abstract: Understanding and reasoning with abstractive information from the visual modality presents significant challenges for current multi-modal larg
arXiv:2601.06160v2 Announce Type: replace Abstract: Large Language Models (LLMs) often suffer from ''Reasoning Collapse'' on challenging mathematical reasoning tasks, where stochastic sampling produce
arXiv:2604.26793v1 Announce Type: new Abstract: Motivated by sensing modalities in modern autonomous systems that involve hardware-constrained spatial sampling over large arrays with limited coherence