Knowledge catalogue
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
arXiv:2604.26181v1 Announce Type: new Abstract: Multimodal deep neural networks deployed in realistic environments must contend with runtime variations: changes in modality quality, overall input comp
arXiv:2604.26726v1 Announce Type: new Abstract: Languages of the world vary concerning the order of subject, object and verb. The most frequent dominant orders are SOV and SVO, and researchers have ta
arXiv:2604.26102v1 Announce Type: cross Abstract: Large language model agents have achieved remarkable progress on software engineering tasks, yet current approaches suffer from a fundamental context
arXiv:2604.26633v1 Announce Type: cross Abstract: The bottleneck in learning-based industrial defect detection is often limited not by model capacity, but by the scarcity of labeled defect data: defec
arXiv:2505.22910v2 Announce Type: replace Abstract: When a student fails an exam, do we tend to blame their effort or the test's difficulty? Attribution, defined as how reasons are assigned to event o
arXiv:2604.26772v1 Announce Type: new Abstract: Recent methods demonstrate that large-scale pretrained models, such as CLIP vision transformers, effectively detect AI-generated images (AIGIs) from uns
arXiv:2604.26511v1 Announce Type: cross Abstract: Alignment faking (AF) occurs when an LLM strategically complies with training objectives to avoid value modification, reverting to prior preferences o
arXiv:2604.26615v1 Announce Type: cross Abstract: Large language models (LLMs) accelerate software development but often exhibit instability, non-determinism, and weak adherence to development discipl
arXiv:2510.04214v3 Announce Type: replace Abstract: We deploy large language models (LLMs) as business development (BD) agents for persuasive price negotiation in online travel agencies (OTAs). The ag
arXiv:2511.20032v3 Announce Type: replace Abstract: Visual attention serves as the primary mechanism through which MLLMs interpret visual information; however, its limited localization capability ofte
arXiv:2604.26167v1 Announce Type: cross Abstract: Recent work has shown that a model's input word embeddings can serve as effective control variables for steering its behavior toward outputs that sati
arXiv:2604.26361v1 Announce Type: cross Abstract: Globalization of graphic designs such as those used in marketing materials and magazines is increasingly important for communication to broad audience
arXiv:2604.26514v1 Announce Type: cross Abstract: This paper investigates efficient methods for utilizing text-only data to improve speech recognition, focusing on encoder-dominated models that facili
arXiv:2603.02259v2 Announce Type: replace-cross Abstract: Multi-agent systems provide mature methodologies for role decomposition, coordination, and normative governance, capabilities that remain esse
arXiv:2604.26651v1 Announce Type: cross Abstract: With the increasing availability of online information, recommender systems have become an important tool for many web-based systems. Due to the conti
arXiv:2512.20340v3 Announce Type: replace Abstract: Although diffusion transformer (DiT)-based video virtual try-on (VVT) has made significant progress in synthesizing realistic videos, existing metho
arXiv:2602.03467v2 Announce Type: replace Abstract: Explanations are central to human cognition, yet AI systems often produce outputs that are difficult to understand. While symbolic AI offers a trans
arXiv:2604.26347v1 Announce Type: cross Abstract: Objective metrics for emotional expressiveness are vital for speech generation, particularly in expressive synthesis and voice conversion requiring em
arXiv:2508.16131v2 Announce Type: replace-cross Abstract: Code completion entails the task of providing missing tokens given a surrounding context. It can boost developer productivity while providing
arXiv:2602.06603v3 Announce Type: replace Abstract: Reinforcement learning (RL) is a type of artificial intelligence for making optimal choices. In healthcare, researchers generally use offline RL (OR
arXiv:2509.11295v2 Announce Type: replace Abstract: Developing effective prompts demands significant cognitive investment to generate reliable, high-quality responses from Large Language Models (LLMs)