Knowledge catalogue
arXiv:2608.09127v1 Announce Type: new Abstract: Despite the demand for robots in high-value clinical tasks like bathing, contemporary systems still lack the safety and reliability required for complex
arXiv:2507.01900v3 Announce Type: replace Abstract: Pruning is a highly effective approach for compressing large language models (LLMs), significantly reducing inference latency. However, conventional
arXiv:2608.08720v1 Announce Type: new Abstract: Although most existing exposure correction methods achieve high fidelity, they often place excessive focus on overall pixel-wise accuracy, making it cha
arXiv:2607.18867v2 Announce Type: replace-cross Abstract: Large language models leak parametric knowledge of what followed a historical date into decision tasks indexed by that date -- not necessarily
arXiv:2608.07609v1 Announce Type: cross Abstract: High-throughput screening (HTS) assays are central to early-stage drug discovery but are often limited by extreme data sparsity, as primary screens ty
arXiv:2608.07707v1 Announce Type: new Abstract: Computing distances between sets is essential in robotic motion planning and control, where differentiable gradients enable real-time optimization. The
arXiv:2608.08485v1 Announce Type: new Abstract: Current LLM safety guardrails face a fundamental tension: fine-tuning distorts pre-trained representations while generative judges incur prohibitive inf
arXiv:2505.18469v2 Announce Type: replace Abstract: Face restoration has achieved significant advancements through the years of development. However, maintaining high fidelity and authenticity while a
arXiv:2608.09031v1 Announce Type: new Abstract: Graph neural networks typically propagate information through repeated message-passing layers, coupling the distance over which information travels with
arXiv:2608.08975v1 Announce Type: cross Abstract: As large language models increasingly participate in scientific evaluation, we investigate a potential form of reward hacking: how rhetorical choices
arXiv:2608.09717v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to perform subjective evaluations traditionally made by humans, yet their validity as social judges
arXiv:2608.08989v1 Announce Type: cross Abstract: Public infant cry corpora are small, label-incompatible, and almost always evaluated one corpus at a time. We ask what this practice hides and what fi
arXiv:2505.24273v2 Announce Type: replace Abstract: Recent advancements in large language models (LLMs) suggest that reinforcement learning (RL) effectively internalizes search strategies, yielding si
arXiv:2608.07861v1 Announce Type: new Abstract: Vision-language models (VLMs) are becoming a practical backend for mobile visual question answering (VQA) systems, enabling smartphones and smart glasse
arXiv:2608.09019v1 Announce Type: cross Abstract: As generative AI increasingly becomes a common source of daily decision-making, including financial choices, it is critical to understand how people e
arXiv:2608.07511v1 Announce Type: cross Abstract: Background. Effective clinical practice relies heavily on the socio-communicative skills of medical professionals. Large language models (LLMs) have b
arXiv:2608.09433v1 Announce Type: cross Abstract: In regulated domains such as finance, a model that cannot be explained cannot be deployed, yet many interpretable classifiers defeat their own purpose
arXiv:2608.07494v1 Announce Type: cross Abstract: AI tools like ChatGPT and DeepSeek, powered by Large Language Models (LLMs), allow users to obtain instant and effective content responses simply by t
arXiv:2608.07616v1 Announce Type: new Abstract: Vision transformers face significant computational overheads in high-resolution dense prediction due to the quadratic complexity of self-attention. Line
arXiv:2608.08069v1 Announce Type: cross Abstract: Foundation models are increasingly reused as software components, making model selection a critical software-engineering decision. Current model hubs
arXiv:2608.08430v1 Announce Type: cross Abstract: Virtual cells employ machine learning models to simulate and predict cellular behaviors, serving as a critical computational framework for investigati
arXiv:2603.20004v3 Announce Type: replace-cross Abstract: Translating natural language questions to SQL queries (Text-to-SQL) is a long-standing problem in database research. Recent efforts have focus
arXiv:2601.17108v3 Announce Type: replace-cross Abstract: This paper proposes a hybrid Mamba-attention neural architecture to achieve improved channel estimation for orthogonal frequency-division mult
arXiv:2608.08825v1 Announce Type: cross Abstract: Foundation models for time series forecasting demonstrate impressive zero-shot generalization but often underperform on specialized domains such as hi
arXiv:2608.09572v1 Announce Type: new Abstract: Hyperbolic geometry has recently emerged as a powerful representation space for multimodal learning, as it naturally captures hierarchical semantic stru
arXiv:2505.18587v2 Announce Type: replace-cross Abstract: Deepfakes pose a significant threat to digital media security, with current detection methods struggling to generalize across different manipu
arXiv:2608.09586v1 Announce Type: new Abstract: The Independent Chip Model (ICM) converts tournament chips into reference prize equity, and policies are routinely constructed against those values. Bec
arXiv:2608.08734v1 Announce Type: new Abstract: Unrestricted adversarial transfer attacks are important for evaluating the black-box robustness of deep visual models. Diffusion-based attacks have show
arXiv:2608.08958v1 Announce Type: cross Abstract: Tree Search-based test-time scaling of LLMs is a powerful tool for automated scientific coding. However, pure Tree Search sometimes struggles with sys
arXiv:2608.08801v1 Announce Type: new Abstract: Translating technical requirements across languages can introduce semantic drift, altering numerical constraints, polarities, modalities, or other speci
arXiv:2608.08210v1 Announce Type: new Abstract: Collaborative dialogue can end with apparent agreement while participants still differ on goals, assumptions, or execution plans, creating an extbf{illu
arXiv:2608.09594v1 Announce Type: cross Abstract: Recently, AI-driven video generation has attracted considerable attention. This surge increases the demand for reliable video quality assessment (VQA)
arXiv:2511.15941v2 Announce Type: replace-cross Abstract: Tabular data underpins decisions across science, industry, and public services. Despite rapid progress, advances in deep learning have not ful
arXiv:2608.09385v1 Announce Type: cross Abstract: Generative AI models are primarily designed to imitate the data distribution, an objective that neither corrects diversity lost by a learned generator
arXiv:2608.07554v1 Announce Type: new Abstract: The development of vision-based wildfire detection systems for unmanned aerial vehicles is constrained by the limited availability of diverse real-world
arXiv:2607.15708v2 Announce Type: replace Abstract: Classical leader-follower formation control suffers from single points of failure and error propagation, and relies on absolute localization sensors
arXiv:2608.08127v1 Announce Type: new Abstract: The runtime of Constraint Programming (CP) solvers is highly sensitive to modeling choices, such as symmetry breaking, implied constraints, global const
arXiv:2608.08802v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) makes Multimodal Large Language Models more accurate, but the gains are brittle: simply paraphrasi
arXiv:2608.09348v1 Announce Type: new Abstract: Density estimation underlies many unsupervised tasks on tabular data such as anomaly detection, out-of-distribution detection, and data augmentation. Al
arXiv:2608.09244v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved remarkable success in generating high-quality images from a given text prompt. Subject-driven generation ai
arXiv:2601.03888v4 Announce Type: replace-cross Abstract: In prior work, we introduced IndexTTS 2, a zero-shot neural text-to-speech foundation model comprising two core components: a transformer-base
arXiv:2608.08876v1 Announce Type: cross Abstract: A graph layout is normally a table of N free coordinates. We optimise a function with a fixed number of parameters instead. This gives a drawing a sam
arXiv:2602.02704v2 Announce Type: replace Abstract: Reasoning over ultra-long documents requires synthesizing sparse evidence scattered across distant segments under strict memory constraints. While s
arXiv:2607.28503v3 Announce Type: replace Abstract: In this paper we present an active, constantly updated AI benchmark which measures the integrity of frontier language models against being co-opted
arXiv:2608.07922v1 Announce Type: new Abstract: Adaptive learning needs both a state that preserves what observations imply and opportunities to act on that state. We study this width--depth tradeoff
arXiv:2608.07504v1 Announce Type: cross Abstract: We propose a human bottleneck perspective for understanding how generative AI transforms the innovation process. The central premise is that many cons
arXiv:2608.09707v1 Announce Type: cross Abstract: Embedding trained neural networks as surrogates within optimisation problems is an established practice in operations research. The prevailing approac
arXiv:2608.08800v1 Announce Type: new Abstract: Pretraining LLMs on artificial languages ('pre-pretraining') is a technique that could reportedly increase token efficiency by 33%, i.e., save up to 33%
arXiv:2607.10194v2 Announce Type: replace-cross Abstract: We present IsalHG, a method for representing the structure of any finite, connected hypergraph of bounded hyperedge arity as a string over a c
arXiv:2608.08460v1 Announce Type: new Abstract: Given textual task instructions, generating step-by-step visual instructions as an image sequence requires the simultaneous satisfaction of multiple pro
arXiv:2608.08935v1 Announce Type: new Abstract: This work presents a unified multimodal AI system for damage assessment that integrates retrieval-augmented generation (RAG) models, thermal spectrum pe
arXiv:2511.19735v2 Announce Type: replace-cross Abstract: Randomized controlled trials (RCTs)have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility
arXiv:2608.07801v1 Announce Type: cross Abstract: Precision nitrogen (N) management (PNM) for cotton requires in-season monitoring of crop growth parameters and N status indicators to decide fertilize
arXiv:2608.07688v1 Announce Type: new Abstract: IT audits require auditors to judge whether heterogeneous organizational evidence satisfies semantic security and compliance controls. This judgment is
arXiv:2511.10119v4 Announce Type: replace Abstract: We propose a new perspective for approaching artificial general intelligence (AGI) through an intelligence foundation model (IFM). Unlike existing f
arXiv:2608.09420v1 Announce Type: new Abstract: User simulators are widely used as scalable environments for training and evaluating interactive assistants. Generating the next user turn is inherently
arXiv:2604.03919v2 Announce Type: replace-cross Abstract: We present the first systematic study of Sparse Autoencoders (SAEs) on video representations. Standard SAEs decompose video into interpretable
arXiv:2608.09177v1 Announce Type: cross Abstract: In recent years, systems that utilize immersive space have been developed in various fields. Immersive spaces often contain considerable amounts of vi
arXiv:2608.09167v1 Announce Type: cross Abstract: Hand positional guidance with intuitive perception is crucial for enhancing user interaction and task performance in immersive environments. However,
arXiv:2608.08915v1 Announce Type: new Abstract: Situated language use is multimodal and embodied. For example, gestures can carry information that is absent or underspecified in the speech signal, yet