Knowledge catalogue
arXiv:2606.31101v1 Announce Type: new Abstract: Bridging the sim-to-real gap is a core challenge in deploying learned manipulation policies. Sim-to-real learning is attractive because it can replace e
arXiv:2511.17581v3 Announce Type: replace-cross Abstract: Modeling the cognitive and experiential factors of human navigation is central to deepening our understanding of human-environment interaction
arXiv:2511.18242v3 Announce Type: replace Abstract: Egocentric video understanding requires procedural reasoning under partial observability and continuously shifting viewpoints. Current multimodal la
arXiv:2606.31132v1 Announce Type: new Abstract: Generative control policies (GCPs), such as diffusion policies and flow-based vision-language-action models, enable test-time scaling in robot control.
arXiv:2606.30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models ha
arXiv:2606.30662v1 Announce Type: cross Abstract: The advent of Generative Artificial Intelligence (GenAI), and in particular Large Language Models (LLMs), is reshaping educational practice, while int
arXiv:2606.31252v1 Announce Type: new Abstract: Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature,
arXiv:2606.30668v1 Announce Type: cross Abstract: What happens when LLM agents operate with no context outside a turn, minimal prompting, and simple tools? Inspired by swarm engineering, we give colle
arXiv:2603.23461v2 Announce Type: replace Abstract: We study reinforcement learning (RL) with linear function approximation in Markov Decision Processes (MDPs) satisfying linear Bellman completeness -
arXiv:2606.31487v1 Announce Type: new Abstract: Due to the limited endurance of embedded energy sources such as lithium-polymer (LiPo) batteries, the flight duration and operational range of unmanned
arXiv:2511.11046v3 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) have become an indispensable tool for analyzing relational data. Classical GNNs are broadly classified into three
arXiv:2603.21463v2 Announce Type: replace Abstract: The deep-learning based image matching networks can now handle significantly larger variations in viewpoints and illuminations while providing match
arXiv:2606.31982v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) incur prohibitive inference costs due to long visual token sequences. Training-free visual token reduction prov
arXiv:2606.30999v1 Announce Type: new Abstract: In two-sided marketplaces with heterogeneous products, it is important to understand the causal relationship between additional supply and marketplace o
arXiv:2606.30664v1 Announce Type: cross Abstract: The coupon incentive is one of the most common tools marketers use to court users to engage with a business at various stages of the customer life cyc
arXiv:2606.31760v1 Announce Type: new Abstract: Rolling-shutter cameras introduce characteristic distortions when imaging fast moving objects, and these effects are typically treated as artifacts to b
arXiv:2606.31990v1 Announce Type: cross Abstract: We analyze the effect of optimizing the initial population of genetic programming (GP) for symbolic regression (SR) on the accuracy and complexity of
arXiv:2603.13402v3 Announce Type: replace Abstract: Current text-to-video models can make individual frames look convincing while still getting simple interactions wrong: objects move before contact,
arXiv:2606.31367v1 Announce Type: cross Abstract: The proliferation of multimedia content on social platforms has fueled multimodal misinformation, where images are used to reinforce false claims. Con
arXiv:2606.31591v1 Announce Type: cross Abstract: Emergent misalignment (EM) is a recently discovered phenomenon in LLMs where fine-tuning on a narrow misaligned task, such as writing insecure code, l
arXiv:2606.31800v1 Announce Type: new Abstract: Despite recent progress, the reasoning capabilities of large multimodal language models (MLLMs) remain fundamentally constrained by static supervision,
arXiv:2606.31331v1 Announce Type: new Abstract: Collaborative inference can improve predictive performance by integrating complementary information across agents, but applying collaborative fusion to
arXiv:2606.30665v1 Announce Type: cross Abstract: Stage B heart failure is characterized by asymptomatic structural or functional cardiac abnormalities. Identifying individuals at this stage is clinic
arXiv:2606.31110v1 Announce Type: new Abstract: Artificial neural networks (NNs) and machine learning (ML) algorithms are poorly understood from a theoretical perspective, which makes it difficult to
arXiv:2606.31845v1 Announce Type: new Abstract: A transformer's feed-forward (FFN) sublayer materializes the distinctions attention gathers, yet gives no account of what it computes. In a parameter-ne
arXiv:2606.31201v1 Announce Type: new Abstract: Multi-objective masked image modeling (MIM) combines complementary learning signals (token distillation, CLS alignment, and pixel reconstruction) but ex
arXiv:2606.31058v1 Announce Type: new Abstract: The composition of author teams is an important factor influencing the novelty of academic papers. However, existing studies have paid limited attention
arXiv:2606.32040v1 Announce Type: new Abstract: Low-resolution face recognition (LR-FR) remains a challenging task due to poor feature extraction and aggregation, as probe images often contain limited
arXiv:2602.06625v2 Announce Type: replace Abstract: Existing LLM-as-a-Judge systems suffer from three fundamental limitations: limited adaptivity to task- and domain-specific evaluation criteria, syst
arXiv:2606.31511v1 Announce Type: cross Abstract: In deployment settings where retraining is infeasible, small frozen code models are routinely asked to repair a failed program after seeing their own
arXiv:2606.31651v1 Announce Type: new Abstract: Recent automated research systems show that language-model agents can generate hypotheses, run experiments, and write complete manuscripts, but most evi
arXiv:2606.31691v1 Announce Type: new Abstract: Scalable reinforcement learning has popularized high-throughput sampling architectures, which significantly compresses the training time for off-policy
arXiv:2606.32016v1 Announce Type: new Abstract: Multimodal graph foundation models aim to learn reusable knowledge from graphs enriched with text, images, attributes, and relational topology, thereby
arXiv:2606.31742v1 Announce Type: cross Abstract: Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep lear
arXiv:2511.17979v2 Announce Type: replace Abstract: Diffusion models have achieved remarkable success in generative modeling, yet how to effectively adapt large pretrained models to new tasks remains
arXiv:2509.25682v2 Announce Type: replace Abstract: AI-generated image (AIGI) attribution presents a pressing challenge that goes beyond mere AIGI detection, aiming to identify the source model or tec
arXiv:2509.12878v2 Announce Type: replace Abstract: Few-shot 3D point cloud semantic segmentation aims to segment novel categories using a minimal number of annotated support samples. However, prototy
arXiv:2412.02798v3 Announce Type: replace Abstract: We consider the problem of reconstructing a HxWx31 hyperspectral image from a Himes W grayscale snapshot measurement that is captured using only a s
arXiv:2603.09930v2 Announce Type: replace Abstract: Text-motion retrieval aims to learn a semantically aligned latent space between natural language descriptions and 3D human motion skeleton sequences
arXiv:2603.12893v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a standard technique for post-training diffusion-based image synthesis models, as it enables learning f
arXiv:2606.31522v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous financial agents initialized with explicit behavioral mandates such as 'preserve
arXiv:2606.31567v1 Announce Type: cross Abstract: Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety. Yet the AI reporting ecosystem is fragme
arXiv:2606.31082v1 Announce Type: new Abstract: AI-generated image (AIGI) detection is undergoing a critical transition from laboratory benchmarks to open-world adversarial defense. The prevalent para
arXiv:2606.31938v1 Announce Type: cross Abstract: Deploying Vision Transformer (ViT) models on edge platforms remains challenging due to their high computational demands and the architectural heteroge
arXiv:2606.32023v1 Announce Type: cross Abstract: Forest attributes are essential for national-scale resource monitoring. Airborne LiDAR metrics are among the auxiliary variables most strongly correla
arXiv:2512.04390v2 Announce Type: replace-cross Abstract: Joint video super-resolution and deblurring (VSRDB) requires both efficient long-range temporal modeling and robustness to frame-wise exposure
arXiv:2606.31092v1 Announce Type: new Abstract: Full fine-tuning adapts large language models to new tasks but can erode capabilities they already possess. Existing remedies protect through proxies su
arXiv:2606.31226v1 Announce Type: new Abstract: World-model-based autonomous driving endows the model with the ability to understand scene evolution. Yet this promise is undermined by the prevailing i
arXiv:2606.31484v1 Announce Type: cross Abstract: Parallel thinking has enjoyed great success for boosting LLM performance on reasoning tasks without the need for any re-training. However, existing me
arXiv:2606.32027v1 Announce Type: cross Abstract: Reward design remains a central bottleneck for autonomous robot policy improvement, especially in long-horizon manipulation tasks where sparse success
arXiv:2606.31589v1 Announce Type: cross Abstract: Organisations designing, developing, and deploying machine learning systems (MLS) need to be able to check that these systems are trustworthy, and com
arXiv:2606.30749v1 Announce Type: new Abstract: Large-scale dexterous grasp datasets encode rich priors over hand-object interaction, but their use has largely been confined to grasp generation and pi
arXiv:2606.31311v1 Announce Type: cross Abstract: Testing a new visual-analytics idea usually takes months: one needs to find a realistic data set, clean it, and implement an interactive prototype. We
arXiv:2606.31366v1 Announce Type: cross Abstract: Driven by high-throughput experimentation, computational modeling, and artificial intelligence (AI), materials data has expanded at an unprecedented r
arXiv:2506.17294v3 Announce Type: replace-cross Abstract: The advent of artificial intelligence has propelled AI-Generated Game Commentary (AI-GGC) into a rapidly expanding research area, offering adv
arXiv:2606.30973v1 Announce Type: new Abstract: Frictive Policy Optimization (FPO; Pustejovsky et al., 2025) treats friction in collaborative dialogue -- misalignment, misunderstanding, repair -- as a
arXiv:2606.30704v1 Announce Type: cross Abstract: Large language models (LLMs) excel across a wide range of tasks, yet their instance-specific solutions often lack the structural consistency needed fo
arXiv:2601.23088v2 Announce Type: replace-cross Abstract: Semantic caching has emerged as a pivotal technique for scaling LLM applications, widely adopted by major providers including AWS and Microsof
arXiv:2606.31136v1 Announce Type: new Abstract: Few-shot segmentation asks a model to delineate a target class in a query image from only a handful of annotated examples, a setting most acute in remot
arXiv:2606.31502v1 Announce Type: new Abstract: Geographic atrophy (GA) secondary to age-related macular degeneration (AMD) requires precise monitoring of relevant structural biomarkers to assess dise