Knowledge catalogue
arXiv:2605.18045v1 Announce Type: cross Abstract: Robotic systems often use predictive uncertainty to decide whether to act autonomously or defer to a fallback policy. In threshold-gated autonomy, unc
arXiv:2605.17301v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in
arXiv:2605.16829v1 Announce Type: new Abstract: Discrete diffusion models are a powerful, emerging paradigm for code generation. They construct programs through iterative refinement of partially corru
arXiv:2605.18226v1 Announce Type: cross Abstract: Modern large language model (LLM) applications increasingly rely on long conditioning prefixes to control model behavior at inference time. While pref
arXiv:2508.04227v2 Announce Type: replace Abstract: Vision-language models (VLMs) and the recent surge of Multimodal Large Language Models (MLLMs) have revolutionized artificial intelligence with unpr
arXiv:2605.18530v1 Announce Type: cross Abstract: While diffusion has drawn considerable recent attention from the language modeling community, continuous diffusion has appeared less scalable than dis
arXiv:2605.17450v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly used for automated vulnerability repair (AVR), where repository-level reasoning enables them to ins
arXiv:2605.16603v1 Announce Type: new Abstract: Controllable multimodal generation is commonly formulated as an inference-time conditioning problem using prompts, guidance, or auxiliary modules. While
arXiv:2605.18675v1 Announce Type: cross Abstract: Offline reinforcement learning struggles with distributional shift and constrained performance due to static dataset limitations, while online RL dema
arXiv:2506.23549v3 Announce Type: replace Abstract: Effective coordination among unfamiliar partners remains a major challenge in multi-agent systems. Existing approaches, such as population-based met
arXiv:2602.03012v2 Announce Type: replace-cross Abstract: Evaluating and improving the security capabilities of code agents requires high-quality, executable vulnerability tasks. However, existing wor
arXiv:2605.16342v1 Announce Type: cross Abstract: Diffusion large language models are a compelling alternative to autoregressive models, yet existing RL methods for diffusion treat all denoising steps
arXiv:2605.18608v1 Announce Type: new Abstract: Continual Test-Time Adaptation (CTTA) aims to empower perception systems to handle dynamic distribution shifts encountered after deployment. Existing me
arXiv:2605.18102v1 Announce Type: new Abstract: Monocular video human mesh recovery is essential for digital humans, avatar animation, and embodied simulation, where both temporal stability and expres
arXiv:2603.08145v2 Announce Type: replace-cross Abstract: Preference-based alignment methods (e.g., RLHF, DPO) typically optimize a single scalar objective, implicitly averaging over heterogeneous hum
arXiv:2605.18298v1 Announce Type: new Abstract: Foundation models pre-trained through masked reconstruction on large-scale EEG data have emerged as a promising paradigm for learning generalizable neur
arXiv:2605.18635v1 Announce Type: cross Abstract: Credit default prediction is a tabular learning problem with severe class imbalance, heterogeneous features, and tight latency budgets. Tabular Founda
arXiv:2605.17899v1 Announce Type: cross Abstract: AlphaFold3 introduces a diffusion-based architecture that elevates protein structure prediction to all-atom resolution with improved accuracy. This st
arXiv:2406.13187v2 Announce Type: replace Abstract: While long-tailed semi-supervised learning (LTSSL) has attracted growing attention in many real-world classification tasks, existing LTSSL algorithm
arXiv:2605.18354v1 Announce Type: new Abstract: Bayesian conformal optimisation methods often use the same held-out data both to search for efficient prediction sets and to certify coverage or risk. T
arXiv:2605.17506v1 Announce Type: new Abstract: A fundamental difficulty in all-in-one blind image restoration is that degradation is usually treated as an implicit factor hidden in degraded-to-clean
arXiv:2510.01479v2 Announce Type: replace Abstract: Offline reinforcement learning (RL) enables policy optimization from fixed datasets, making it suitable for safety-critical applications where onlin
arXiv:2605.16519v1 Announce Type: new Abstract: Accurate polyp segmentation in colonoscopy is essential for early colorectal cancer detection, yet real-world clinical environments pose persistent chal
arXiv:2605.17527v1 Announce Type: new Abstract: Street-view imagery (SVI) is widely used to quantify key indicators of urban environment, such as green- ery, sky, or road view indices. However, existi
arXiv:2605.18722v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently become a central direction in embodied AI, but current systems are restricted to either dual-gripper c
arXiv:2603.04870v2 Announce Type: replace Abstract: Denoising in the sRGB image space is challenging due to large noise variability. Although end-to-end methods perform well, their effectiveness in re
arXiv:2605.17107v1 Announce Type: cross Abstract: We introduce a novel framework for uncertainty quantification of solution operators associated with stochastic partial differential equations (SPDEs).
arXiv:2605.17295v1 Announce Type: cross Abstract: Modern reasoning agents are increasingly evaluated on their ability to generate multiple valid solution paths, plans, or tool-use traces for a given i
arXiv:2603.12676v2 Announce Type: replace Abstract: Generalizing neural surrogate models across different PDE parameters remains difficult because changes in PDE coefficients often make learning harde
arXiv:2602.12015v2 Announce Type: replace Abstract: Deploying large language models for clinical Text-to-SQL requires distinguishing two qualitatively different causes of output diversity: (i) input a
arXiv:2605.17733v1 Announce Type: new Abstract: The promise of Rectified Flow rests on producing self-generated couplings whose trajectories are straight, or nearly so. In practice, trajectories gener
arXiv:2605.18173v1 Announce Type: new Abstract: End-to-end scene text spotting, which unifies text detection and recognition within a single framework, has witnessed remarkable progress driven by deep
arXiv:2605.18048v1 Announce Type: new Abstract: While Graphical User Interface (GUI) agents have shown promising performance in automated device interaction, they primarily depend on static parametric
arXiv:2510.11391v3 Announce Type: replace-cross Abstract: Recent agentic workflows automate professional document generation but focus narrowly on textual quality, overlooking structural and stylistic
arXiv:2602.08354v5 Announce Type: replace Abstract: Recent advancements in large reasoning models (LRMs) have greatly improved their capabilities on complex reasoning tasks through Long Chains of Thou
arXiv:2601.11956v2 Announce Type: replace-cross Abstract: Reliable reasoning in Large Language Models (LLMs) is challenged by their propensity for hallucination. While augmenting LLMs with Knowledge G
arXiv:2605.17432v1 Announce Type: new Abstract: Large language models (LLMs) are commonly adapted to downstream tasks through fine-tuning, but fine-tuning data often contains sensitive information tha
arXiv:2605.16892v1 Announce Type: cross Abstract: Comprehensive situational awareness is essential for autonomous vehicles operating in safety-critical environments, as it enables the identification a
arXiv:2605.18023v1 Announce Type: new Abstract: Open-Vocabulary Object Detection (OVD) models break the limitations of closed-set detection, enabling the iden- tification of unseen categories through
arXiv:2604.07393v2 Announce Type: replace-cross Abstract: Accurate forecasting of industrial time series requires balancing predictive accuracy with physical plausibility under non-stationary operatin
arXiv:2605.18190v1 Announce Type: cross Abstract: Diffusion models achieve state-of-the-art generative performance but suffer from high computational costs during inference due to the repeated evaluat
arXiv:2605.17486v1 Announce Type: cross Abstract: Recent progress in Reinforcement Learning (RL) provides a principled approach to optimizing Vision-Language-Action (VLA) models, facilitating a shift
arXiv:2605.18316v1 Announce Type: new Abstract: Inferring time-varying graph structures from high-dimensional nodal observations is a fundamental problem arising in neuroscience, finance, climatology,
arXiv:2510.07799v2 Announce Type: replace-cross Abstract: The efficiency of multi-agent systems driven by large language models (LLMs) largely hinges on their communication topology. However, designin
arXiv:2605.17470v1 Announce Type: new Abstract: Image super-resolution (SR) aims to reconstruct high-quality, high-resolution (HR) images from low-resolution (LR) inputs and plays a critical role in v
arXiv:2605.16692v1 Announce Type: cross Abstract: We introduce EfficientTDMPC, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC family of algorit
arXiv:2605.18214v1 Announce Type: new Abstract: Collecting large-scale egocentric video datasets with dense spatial and temporal annotations is costly, slow, and often constrained by environmental bia
arXiv:2601.21841v3 Announce Type: replace Abstract: While Large Language Models (LLMs) have demonstrated strong zero-shot reasoning capabilities, their deployment as embodied agents still faces fundam
arXiv:2605.16739v1 Announce Type: cross Abstract: Decoding visual experience from brain activity has advanced substantially, but cur- rent brain-to-text systems largely recover semantic content while
arXiv:2605.17045v1 Announce Type: cross Abstract: Recent advances in Time Series Foundation Models (TSFMs) promise zero-shot forecasting capabilities with minimal task-specific training. While these m
arXiv:2605.17489v1 Announce Type: new Abstract: Face Image Quality Assessment (FIQA) is a crucial control step in biometric pipelines. It ensures only reliable samples are processed to maintain system
arXiv:2508.07292v3 Announce Type: replace Abstract: Endoscopic diagnosis is an iterative process in which clinicians progressively acquire, compare, and verify local visual evidence before reaching a
arXiv:2601.22530v2 Announce Type: replace Abstract: Large Language Models (LLMs) struggle with multi-step reasoning over structured tables. The primary reason is the lack of explicit supervision for i
arXiv:2605.17958v1 Announce Type: new Abstract: Code reasoning refers to the task of predicting the output of a program given its source code and specific inputs. It can measure the reasoning capabili
arXiv:2605.18696v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) now match or beat tuned gradient-boosted trees on a growing fraction of tabular tasks, but no single TFM wins on ever
arXiv:2605.17770v1 Announce Type: new Abstract: The advancement of Large Reasoning Models (LRMs) has catalyzed a paradigm shift from reactive ``fast thinking'' text generation to systematic, step-by-s
arXiv:2605.18703v1 Announce Type: new Abstract: Equipping LLMs with tool-use capabilities via Agentic Reinforcement Learning (Agentic RL) is bottlenecked by two challenges: the lack of scalable, robus
arXiv:2605.17591v1 Announce Type: new Abstract: Aggregate object detection metrics inherently mask catastrophic and repeatable failures in operationally critical, long-tail minority classes. This pape
arXiv:2605.18746v1 Announce Type: cross Abstract: Spatial intelligence unfolds through a perception-action loop: agents act to acquire observations, and reason about how observations vary as a functio
arXiv:2605.17894v1 Announce Type: new Abstract: While agentic AI and its core multimodal large language models (MLLMs) have demonstrated remarkable promise in language and visual reasoning across doma