Knowledge catalogue
arXiv:2602.06105v2 Announce Type: replace-cross Abstract: We study robustness verification of neural networks via metric algebraic geometry. For polynomial neural networks, certifying a robustness rad
arXiv:2509.25897v2 Announce Type: replace-cross Abstract: People often encounter role conflicts -- social dilemmas where the expectations of multiple roles clash and cannot be simultaneously fulfilled
arXiv:2604.15524v1 Announce Type: cross Abstract: This paper presents a novel density control framework for multi-robot systems with spatial safety and energy sustainability guarantees. Stochastic rob
arXiv:2604.16033v1 Announce Type: cross Abstract: Buildings account for approximately 40% of global energy consumption, and with the growing share of intermittent renewable energy sources, enabling de
arXiv:2604.16111v1 Announce Type: new Abstract: We study the sample complexity of learning an epsilon-optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bo
arXiv:2503.03509v3 Announce Type: replace Abstract: In many robotics applications, multiple robots are working in a shared workspace to complete a set of tasks as fast as possible. Such settings can b
arXiv:2604.16200v1 Announce Type: new Abstract: This paper presents a novel saturation aware space variant blind image deblurring framework designed to address challenges posed by saturated pixels in
arXiv:2603.27312v2 Announce Type: replace Abstract: Maximum entropy (MaxEnt) modelling provides a principled framework for generating synthetic populations from aggregate census data, without access t
arXiv:2504.13541v5 Announce Type: replace-cross Abstract: Training resource-constrained autonomous agents on multiple tasks simultaneously is crucial for adapting to diverse real-world environments. R
arXiv:2603.03188v2 Announce Type: replace-cross Abstract: We introduce a novel framework for uncertainty quantification in clustering that combines martingale posterior distributions with density-base
arXiv:2602.07006v2 Announce Type: replace Abstract: Shoe print evidence recovered from crime scenes plays a key role in forensic investigations. By examining shoe prints, investigators can determine d
arXiv:2503.05578v4 Announce Type: replace Abstract: Pose estimation-guided unseen object 6-DoF robotic manipulation is a key task in robotics. However, the scalability of current pose estimation metho
arXiv:2509.25300v4 Announce Type: replace-cross Abstract: While scaling laws for large language models (LLMs) during pre-training have been extensively studied, their behavior under reinforcement lear
arXiv:2604.15998v1 Announce Type: new Abstract: Few-shot Hierarchical Text Classification (few-shot HTC) is a challenging task that involves mapping texts to a predefined tree-structured label hierarc
arXiv:2604.16117v1 Announce Type: cross Abstract: Practice and extensive exercises are essential in programming education. Intelligent tutoring systems (ITSs) are a viable option to provide individual
arXiv:2604.15499v1 Announce Type: cross Abstract: Cryptographically secure neural network inference typically relies on secure computing techniques such as Secure Multi-Party Computation (MPC), enabli
arXiv:2602.11327v2 Announce Type: replace-cross Abstract: The rapid development of the AI agent communication protocols, including the Model Context Protocol (MCP), Agent2Agent (A2A), Agora, and Agent
arXiv:2603.20633v3 Announce Type: replace Abstract: We present Seed1.8, a foundation model aimed at generalized real-world agency: going beyond single-turn prediction to multi-turn interaction, tool u
arXiv:2604.15374v1 Announce Type: cross Abstract: Recent progress in visual brain decoding from fMRI has been enabled by large-scale datasets such as the Natural Scenes Dataset (NSD) and powerful diff
arXiv:2308.10562v2 Announce Type: cross Abstract: The field of Computer Vision (CV) is increasingly shifting towards ``high-level'' visual sensemaking tasks, yet the exact nature of these tasks remain
arXiv:2604.15777v1 Announce Type: cross Abstract: Segmentation is a critical task in computational pathology, as it identifies areas affected by disease or abnormal growth and is essential for diagnos
arXiv:2509.05489v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards has significantly advanced reasoning in large language models (LLMs), but such signals remain coarse,
arXiv:2604.15794v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable success, underpinning diverse AI applications. However, they often suffer from performance degra
arXiv:2604.15681v1 Announce Type: new Abstract: Photoacoustic tomography (PAT) is an emerging imaging modality that combines the complementary strengths of optical contrast and ultrasonic resolution.
arXiv:2604.16263v1 Announce Type: new Abstract: Coordinating multi-robot systems (MRS) to search in unknown environments is particularly challenging for tasks that require semantic reasoning beyond ge
arXiv:2604.15946v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation enables models to segment objects or image regions beyond fixed class sets, offering flexibility in dynamic enviro
arXiv:2604.16138v1 Announce Type: new Abstract: We present a dataset and a model for sentiment analysis of German sign language (DGS) fairy tales. First, we perform sentiment analysis for three levels
arXiv:2604.15356v1 Announce Type: cross Abstract: Recent work on KV cache quantization, culminating in TurboQuant, has approached the Shannon entropy limit for per-vector compression of transformer ke
arXiv:2507.03759v2 Announce Type: replace-cross Abstract: This paper presents ``randomized SINDy', a sequential machine learning algorithm designed for dynamic data that has a time-dependent structure
arXiv:2604.15569v1 Announce Type: new Abstract: Manipulation policies deployed in uncontrolled real-world scenarios are faced with great in-category geometric diversity of everyday objects. In order t
arXiv:2504.16315v4 Announce Type: replace-cross Abstract: The complexity of Sign Language (SL) data processing brings many challenges. The current approach to recognition of SL signs aims to translate
arXiv:2604.15783v1 Announce Type: new Abstract: Urban bike-sharing systems require strategic station expansion to meet growing demand. Traditional allocation approaches rely on explicit demand modelli
arXiv:2604.15628v1 Announce Type: cross Abstract: Cross-modal retrieval between food images and recipe texts is an important task with applications in nutritional management, dietary logging, and cook
arXiv:2604.15735v1 Announce Type: cross Abstract: Fine-grained image retrieval via hand-drawn sketches or textual descriptions remains a critical challenge due to inherent modality gaps. While hand-dr
arXiv:2604.16197v1 Announce Type: new Abstract: Data attribution and valuation are critical for understanding data-model synergy for Large Language Models (LLMs), yet existing gradient-based methods s
arXiv:2604.15771v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a foundational paradigm for grounding large language models in external knowledge. While adaptive re
arXiv:2603.20410v2 Announce Type: replace Abstract: Scientific machine learning is increasingly used to build surrogate models, yet most models are trained under a restrictive assumption in which futu
arXiv:2603.02263v2 Announce Type: replace-cross Abstract: World models compress rich sensory streams into compact latent codes that anticipate future observations. We let separate agents acquire such
arXiv:2604.16022v1 Announce Type: new Abstract: As Large Language Models (LLMs) transition from text processors to autonomous agents, evaluating their social reasoning in embodied multi-agent settings
arXiv:2604.15347v1 Announce Type: cross Abstract: Autism Spectrum Disorder (ASD) affects more than 75 million people worldwide. However, scalable support for practicing everyday conversation is scarce
arXiv:2504.20966v4 Announce Type: replace Abstract: We introduce softpick, a rectified, not sum-to-one, drop-in replacement for softmax in transformer attention mechanisms that eliminates attention si
arXiv:2602.14757v2 Announce Type: replace-cross Abstract: We develop an interpolation-based modeling framework for parameter-dependent partial differential equations arising in control, inverse proble
arXiv:2411.13109v4 Announce Type: replace Abstract: This paper revisits the topic of rotation estimation through the lens of special unitary matrices. We begin by reformulating Wahba's problem using S
arXiv:2603.19339v2 Announce Type: replace-cross Abstract: Dimensionality reduction is critical for deploying dense retrieval systems at scale, yet mainstream post-hoc methods face a fundamental trade-
arXiv:2511.20834v2 Announce Type: replace-cross Abstract: Sparse Convolution (SpC) powers 3D point cloud networks widely used in autonomous driving and augmented/virtual reality. SpC builds a kernel m
arXiv:2604.15862v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has recently redefined the paradigm of 3D reconstruction, striking an unprecedented balance between visual fidelity and com
arXiv:2604.15651v1 Announce Type: new Abstract: Machine learning has achieved impressive performance in tomographic reconstruction, but supervised training requires paired measurements and ground-trut
arXiv:2604.15828v1 Announce Type: new Abstract: Hyperspectral imaging enables fine-grained recognition of materials by capturing rich spectral signatures, but learning robust classifiers is challengin
arXiv:2604.15711v1 Announce Type: cross Abstract: Pathological diagnosis is highly reliant on image analysis, where Regions of Interest (ROIs) serve as the primary basis for diagnostic evidence, while
arXiv:2604.15664v1 Announce Type: new Abstract: The rise of autonomous AI agents suggests that dynamic benchmark environments with built-in feedback on scientifically grounded tasks are needed to eval
arXiv:2604.15837v1 Announce Type: new Abstract: Combinatorial black-box optimization in high-dimensional settings demands a careful trade-off between exploiting promising regions of the search space a
arXiv:2604.16037v1 Announce Type: new Abstract: The widespread adoption of large language models (LLMs) has increased concerns about their robustness. Vulnerabilities in perturbations of tokenisation
arXiv:2604.15416v1 Announce Type: cross Abstract: Sign-based optimization algorithms, such as SignSGD, have garnered significant attention for their remarkable performance in distributed learning and
arXiv:2604.15727v1 Announce Type: new Abstract: Large language models exhibit systematic limitations in structured logical reasoning: they conflate hypothesis generation with verification, cannot dist
arXiv:2604.15324v1 Announce Type: cross Abstract: As AI enters creative practice, audiences face growing uncertainty in judging authenticity and value. This study examines the Struggle Premium, the ad
arXiv:2604.16086v1 Announce Type: cross Abstract: One of the dominant paradigms in self-supervised learning (SSL), illustrated by MoCo or DINO, aims to produce robust representations by capturing feat
arXiv:2409.17596v2 Announce Type: replace-cross Abstract: In recent years, live video streaming has gained widespread popularity across various social media platforms. Quality of experience (QoE), whi
arXiv:2604.15559v1 Announce Type: new Abstract: Recent work on subliminal learning demonstrates that language models can transmit semantic traits through data that is semantically unrelated to those t
arXiv:2602.05638v3 Announce Type: replace Abstract: While foundation models have advanced surgical video analysis, current approaches rely predominantly on pixel-level reconstruction objectives that w
arXiv:2604.16262v1 Announce Type: new Abstract: Recent advances in language models have substantially improved Natural Language Understanding (NLU). Although widely used benchmarks suggest that Large