Knowledge catalogue
arXiv:2604.08926v1 Announce Type: new Abstract: Post-training paradigms for Large Language Models (LLMs), primarily Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), face a fundamental dil
arXiv:2604.09359v1 Announce Type: new Abstract: Label noise in multi-label learning (MLL) poses significant challenges for model training, particularly in partial multi-label learning (PML) where cand
arXiv:2604.08805v1 Announce Type: cross Abstract: In November 2025, the authors ran a workshop on the topic of what makes a good reinforcement learning (RL) environment for autonomous cyber defence (A
arXiv:2604.09023v1 Announce Type: new Abstract: Multi-task visual anomaly detection is critical for car-related manufacturing quality assessment. However, existing methods remain task-specific, hinder
arXiv:2604.09195v1 Announce Type: new Abstract: We propose Camera Artist, a multi-agent framework that models a real-world filmmaking workflow to generate narrative videos with explicit cinematic lang
arXiv:2604.08568v1 Announce Type: cross Abstract: The evolution of writing assistance tools from machine translation to large language models (LLMs) has changed how researchers write. This study inves
arXiv:2604.08757v1 Announce Type: cross Abstract: Humor is one of the most culturally embedded and socially significant dimensions of human communication, yet it remains largely unexplored as a dimens
arXiv:2511.01383v2 Announce Type: replace Abstract: Accurate point-wise velocity estimation in 3D is crucial for robot interaction with non-rigid dynamic agents, enabling robust performance in path pl
arXiv:2604.09537v1 Announce Type: cross Abstract: Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the prov
arXiv:2604.08836v1 Announce Type: new Abstract: Generative object compositing methods have shown remarkable ability to seamlessly insert objects into scenes. However, when applied to real-world catalo
arXiv:2603.18561v2 Announce Type: replace Abstract: Planning-oriented end-to-end driving models show great promise, yet they fundamentally learn statistical correlations instead of true causal relatio
arXiv:2604.08772v1 Announce Type: cross Abstract: Atmospheric clouds exhibit complex three-dimensional structure and microphysical details that are poorly constrained by the predominantly two-dimensio
arXiv:2509.25835v4 Announce Type: replace Abstract: Test-time scaling improves large language models (LLMs) on long-horizon reasoning tasks by allocating more compute at inference. LLM inference via t
arXiv:2505.18600v3 Announce Type: replace-cross Abstract: Modern single-image super-resolution (SISR) models deliver photo-realistic results at the scale factors on which they are trained, but collaps
arXiv:2604.09282v1 Announce Type: cross Abstract: Reliable position and attitude sensing is critical for highly automated vehicles that operate on conventional roadways. Lidar sensors are increasingly
arXiv:2507.04736v2 Announce Type: replace Abstract: Large Language Models have emerged as powerful tools for automating Register-Transfer Level (RTL) code generation, yet they face critical limitation
arXiv:2603.21935v2 Announce Type: replace-cross Abstract: Quantitative disease severity scoring in medical imaging is costly, time-consuming, and subject to inter-reader variability. At the same time,
arXiv:2604.09101v1 Announce Type: cross Abstract: Organisations with limited data and computational resources increasingly outsource model training to Machine Learning as a Service (MLaaS) providers,
arXiv:2604.09370v1 Announce Type: cross Abstract: Labelling tissue components in histology whole slide images (WSIs) is prohibitively labour-intensive: a single slide may contain tens of thousands of
arXiv:2508.06656v2 Announce Type: replace Abstract: In-generation watermarking for latent diffusion models has recently shown high robustness in marking generated images for easier detection and attri
arXiv:2603.05744v2 Announce Type: replace Abstract: Current AI-powered code assistance tools often struggle with poorly-defined problem statements that lack sufficient task context and requirements sp
arXiv:2511.22963v2 Announce Type: replace-cross Abstract: Enabling humanoid robots to follow free-form language commands is critical for seamless human-robot interaction, collaborative task execution,
arXiv:2601.10632v2 Announce Type: replace Abstract: In this paper, we find that the generation of 3D human motions and 2D human videos is intrinsically coupled. 3D motions provide the structural prior
arXiv:2604.09313v1 Announce Type: cross Abstract: UAV images are critical for applications such as large-area mapping, infrastructure inspection, and emergency response. However, in real-world flight
arXiv:2604.09029v1 Announce Type: cross Abstract: Large language models have been widely explored as decision-support tools in high-stakes domains due to their contextual understanding and reasoning c
arXiv:2604.08974v1 Announce Type: new Abstract: Uncertainty quantification is a set of techniques that measure confidence in language models. They can be used, for example, to detect hallucinations or
arXiv:2501.19038v3 Announce Type: replace-cross Abstract: Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In
arXiv:2503.00035v2 Announce Type: replace-cross Abstract: Large language models (LLMs) struggle with hallucinations due to false or outdated knowledge. Given the high resource demands of retraining th
arXiv:2604.09308v1 Announce Type: new Abstract: Large language models are making autonomous drug discovery agents increasingly feasible, but reliable success in this setting is not determined by any s
arXiv:2604.09446v1 Announce Type: cross Abstract: The Tactile Internet demands sub-millisecond latency and ultra-high reliability, as high latency or packet loss could lead to haptic control instabili
arXiv:2510.21588v2 Announce Type: replace-cross Abstract: Biological and artificial learners are inherently exposed to a stream of data and experience throughout their lifetimes and must constantly ad
arXiv:2604.09155v1 Announce Type: cross Abstract: Graphical user interface (GUI) agents powered by vision language models (VLMs) are rapidly moving from passive assistance to autonomous operation. How
arXiv:2604.08643v1 Announce Type: new Abstract: User interactions in online recommendation platforms create interdependencies among content creators: feedback on one creator's content influences the s
arXiv:2604.08851v1 Announce Type: new Abstract: While significant work has been done on personality recognition, the lack of multilingual datasets remains an unresolved challenge. To address this, we
arXiv:2604.09076v1 Announce Type: new Abstract: Spatial transcriptomics provides a molecularly rich description of tissue organization, enabling unsupervised discovery of tissue niches -- spatially co
arXiv:2604.08584v1 Announce Type: cross Abstract: Long-context LLMs increasingly rely on extended, reusable prefill prompts for agents and domain Q&A, pushing attention and KV-cache to become the domi
arXiv:2604.09201v1 Announce Type: new Abstract: Camera-controllable video generation aims to synthesize videos with flexible and physically plausible camera movements. However, existing methods either
arXiv:2604.08924v1 Announce Type: new Abstract: Infrared-visible image fusion aims to integrate complementary information for robust visual understanding, but existing fusion methods struggle with sim
arXiv:2604.09246v1 Announce Type: cross Abstract: Differentiable Digital Signal Processing (DDSP) pipelines for voice conversion rely on subtractive synthesis, where a periodic excitation signal is sh
arXiv:2604.09351v1 Announce Type: cross Abstract: In this letter, we consider the problem of decentralized decision making among connected autonomous vehicles at unsignalized intersections, where exis
arXiv:2604.08723v1 Announce Type: cross Abstract: Preference optimization methods such as DPO and KTO are widely used for aligning language models, yet little is understood about what properties of pr
arXiv:2604.08711v1 Announce Type: cross Abstract: The disintegration of liquid sheets into ligaments and droplets involves highly transient, multi-scale dynamics that are difficult to quantify from hi
arXiv:2604.09145v1 Announce Type: new Abstract: Nighttime photography is severely degraded by light pollution induced by pervasive artificial lighting in urban environments. After long-range scatterin
arXiv:2604.09089v1 Announce Type: cross Abstract: Large Language Models (LLMs) for code generation can replicate insecure patterns from their training data. To mitigate this, a common strategy for sec
arXiv:2604.08847v1 Announce Type: new Abstract: Deepfake detection has become a fundamental component of modern media forensics. Despite significant progress in detection accuracy, most existing metho
arXiv:2604.08922v1 Announce Type: new Abstract: Complex degradations like noise, blur, and low resolution are typical challenges in real world image fusion tasks, limiting the performance and practica
arXiv:2510.10181v3 Announce Type: replace-cross Abstract: Embodied agents face a fundamental limitation: once deployed in real-world environments, they cannot easily acquire new knowledge to improve t
arXiv:2604.08939v1 Announce Type: new Abstract: Multi-Task Learning (MTL) is a foundational machine learning problem that has seen extensive development over the past decade. Recently, various optimiz
arXiv:2601.22285v4 Announce Type: replace Abstract: Model merging combines knowledge from separately fine-tuned models, yet success factors remain poorly understood. While recent work treats mergeabil
arXiv:2604.08720v1 Announce Type: cross Abstract: Performance optimization of AI infrastructure is key to the fast adoption of large language models (LLMs). The PyTorch compiler (torch.compile), a cor
arXiv:2602.20028v2 Announce Type: replace-cross Abstract: Accurate taxonomic identification is the cornerstone of biodiversity monitoring and agricultural management, particularly for the hyper-divers
arXiv:2604.09106v1 Announce Type: new Abstract: Diffusion models are known for generating high-quality images, causing serious security concerns. To combat this, most efforts rely on deep neural netwo
arXiv:2408.01257v2 Announce Type: replace-cross Abstract: Coordination is a fundamental aspect of life. The advent of social media has made it integral also to online human interactions, such as those
arXiv:2604.08609v1 Announce Type: cross Abstract: Digital forensic investigations increasingly rely on heterogeneous evidence such as images, scanned documents, and contextual reports. These artifacts
arXiv:2604.08846v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have been shown to be vulnerable to malicious queries that can elicit unsafe responses. Recent work uses prom
arXiv:2512.01708v2 Announce Type: replace-cross Abstract: Learning the structure of a Bayesian network from decentralized data poses two major challenges: (i) ensuring rigorous privacy guarantees for
arXiv:2604.06161v2 Announce Type: replace-cross Abstract: Most digital videos are stored in 8-bit low dynamic range (LDR) formats, where much of the original high dynamic range (HDR) scene radiance is
arXiv:2604.09240v1 Announce Type: new Abstract: High-Level Synthesis (HLS) compiles C/C++ into RTL, but exploring pragma-driven optimization choices remains expensive because each design point require
arXiv:2604.08837v1 Announce Type: new Abstract: Flow-based image generative models exhibit stable training and produce high quality samples when using multi-step sampling procedures. One-step generati
arXiv:2604.08574v1 Announce Type: cross Abstract: Large Genomic Foundation Models have recently achieved remarkable results and in-vivo translation capabilities. However these models quickly grow to o