Knowledge catalogue
arXiv:2605.07492v1 Announce Type: new Abstract: The past year has seen over 20 open-source document parsing models, yet thefield still benchmarks almost exclusively on OmniDocBench, a 1,355-pagemanual
arXiv:2603.15001v2 Announce Type: replace-cross Abstract: Recently, it has been shown that the Stochastic Gradient Bandit (SGB) algorithm converges to a globally optimal policy with a constant learnin
arXiv:2605.06850v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has emerged as a crucial paradigm for unlocking the advanced reasoning capabilities of Large Language Models (LLMs), encom
arXiv:2605.07933v1 Announce Type: new Abstract: Latent diffusion models offer an attractive alternative to discrete diffusion for non-autoregressive text generation by operating on continuous text rep
arXiv:2605.07560v1 Announce Type: new Abstract: Imitation learning for robotic tasks has relied primarily on policies trained only on successful demonstrations, although failures are unavoidable durin
arXiv:2605.07925v1 Announce Type: new Abstract: Conversational Large Language Models are post-trained on language that expresses specific behavioural traits, such as curiosity, open-mindedness, and em
arXiv:2605.06882v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved great improvements in recent years. Nevertheless, it still remains unclear how good LLMs are for reasoning ta
arXiv:2508.05803v2 Announce Type: replace Abstract: Human memory is fleeting. As words are processed, the exact wordforms that make up incoming sentences are rapidly lost. Cognitive scientists have lo
arXiv:2605.06747v1 Announce Type: new Abstract: Progress in embodied intelligence increasingly depends on scalable data infrastructure. While vision and language have scaled with internet corpora, lea
arXiv:2605.07793v1 Announce Type: new Abstract: This paper presents a compact three-class sentiment analysis study for Indonesian social media text. The task is formulated with positive, negative, and
arXiv:2506.12362v3 Announce Type: replace-cross Abstract: Inductive link prediction with knowledge hypergraphs is the task of predicting missing hyperedges involving completely novel entities (i.e., n
arXiv:2605.07177v1 Announce Type: cross Abstract: Existing multimodal search agents process target entities sequentially, issuing one tool call per entity and accumulating redundant interaction rounds
arXiv:2605.07816v1 Announce Type: new Abstract: This paper presents CircleID, a large-scale ICDAR 2026 competition on writer identification and pen classification from scanned hand-drawn circles. The
arXiv:2506.09816v3 Announce Type: replace Abstract: Dynamical systems modeling is a core pillar of scientific inquiry across natural and life sciences. Increasingly, dynamical system models are learne
arXiv:2605.07082v1 Announce Type: new Abstract: In the design of surgical guides for implant placement, determining the precise implant position is a critical step. However, the implant region itself
arXiv:2605.07316v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves LLM reasoning but often induces overthinking, where models generate unnecessarily long reasoning
arXiv:2605.07491v1 Announce Type: new Abstract: This paper proposes a novel framework for implicit multi-camera system calibration utilizing Gaussian Process (GP) regression. Conventional explicit cal
arXiv:2605.07545v1 Announce Type: cross Abstract: Human image animation has witnessed significant advancements, yet generating high-fidelity hand motions remains a persistent challenge due to their hi
arXiv:2605.07218v1 Announce Type: new Abstract: For continuous state-action space scenarios, classical reinforcement learning (RL) theory predominantly focuses on low-rank Markov decision processes (M
arXiv:2605.06920v1 Announce Type: cross Abstract: We propose incentive-aligned mechanisms for in-context credit assignment: the task of assigning credit for AI-generated content (e.g. code, news artic
arXiv:2605.07010v1 Announce Type: new Abstract: Identifying vulnerable transmission lines in power grids before a cascading failure occurs is challenging: existing methods can learn inter-line failure
arXiv:2605.07433v1 Announce Type: cross Abstract: Qualitative models provide crucial instruments for modelling complex biological systems. While advances in automated reasoning and symbolic encodings
arXiv:2605.07456v1 Announce Type: new Abstract: Inference-time controllable generation is essential for real-world applications of unconditional diffusion models. However, most existing techniques foc
arXiv:2605.07631v1 Announce Type: new Abstract: Causal probing methods aim to test and control how internal representations influence the behavior of generative models. In causal probing, an intervent
arXiv:2605.07099v1 Announce Type: new Abstract: Cross-view geo-localization (CVGL) is fundamental for precise localization and navigation in GPS-denied environments, aiming to match ground or UAV imag
arXiv:2605.06710v1 Announce Type: cross Abstract: Information theory plays a central role in establishing fundamental limits on what any learning or estimation algorithm can -- and cannot -- achieve,
arXiv:2605.07930v1 Announce Type: cross Abstract: Differential privacy (DP) is widely employed in machine learning to protect confidential or sensitive training data from being revealed. As data owner
arXiv:2605.07402v1 Announce Type: new Abstract: Human insertion aims to naturally place specific individuals into a target background. Although existing image editing models may have such ability, the
arXiv:2605.07284v1 Announce Type: new Abstract: Recent interpretability work has identified model-internal handles on post-trained behavior, including refusal directions, assistant/persona axes, and s
arXiv:2605.07057v1 Announce Type: new Abstract: Online reinforcement learning (RL) relies on the Markov property for guaranteed performance, but real-world applications often lack well-defined states
arXiv:2605.07733v1 Announce Type: cross Abstract: Accurate truck-to-shipment matching using GPS data is foundational for full truckload supply chain visibility, enabling real-time tracking and accurat
arXiv:2605.07613v1 Announce Type: new Abstract: Conversational news recommendation requires grounding each suggestion in a rapidly evolving article corpus while addressing implicit user intents that l
arXiv:2605.06832v1 Announce Type: cross Abstract: Accurately understanding the intent behind speech, conversation, and writing is crucial to the development of helpful Large Language Model (LLM) assis
arXiv:2605.07037v1 Announce Type: new Abstract: Robot systems for teleoperation commonly use a spring-like force pulling the follower robot towards the leader's position to track their movements. With
arXiv:2605.07768v1 Announce Type: cross Abstract: We investigate interactive trajectory planning subject to uncertainty in the decisions of surrounding agents. To control the ego-agent, we aim to firs
arXiv:2603.01586v3 Announce Type: replace Abstract: Emerging unified editing models have demonstrated strong capabilities in general object editing tasks. However, it remains a significant challenge t
arXiv:2605.07510v1 Announce Type: cross Abstract: Existing benchmarks for multimodal agentic search evaluate multimodal search and visual browsing, but visual evidence is either confined to the input
arXiv:2605.08007v1 Announce Type: new Abstract: Susceptibilities are a technique for neural network interpretability that studies the response of posterior expectation values of observables to perturb
arXiv:2603.03096v2 Announce Type: replace-cross Abstract: How do speech models trained through self-supervised learning structure their representations? Previous studies have looked at how information
arXiv:2510.08638v3 Announce Type: replace-cross Abstract: DINOv2 is routinely deployed to recognize objects, scenes, and actions; yet the nature of what it perceives remains unknown. As a working base
arXiv:2509.21172v2 Announce Type: replace Abstract: Inverse reinforcement learning (IRL) aims to infer rewards from observed behavior, but rewards are not identified from the policy alone: many reward
arXiv:2510.01569v3 Announce Type: replace Abstract: We present InvThink, a training and prompting framework that requires the model to enumerate, analyze, and constrain potential failures before gener
arXiv:2512.23032v2 Announce Type: replace-cross Abstract: Recent work, using the Biasing Features metric, labels a CoT as unfaithful if it omits a prompt-injected hint that affected the prediction. We
arXiv:2605.07622v1 Announce Type: new Abstract: Gender bias in large language models has primarily been investigated for English, while languages with grammatical or morphological gender remain compar
arXiv:2605.07514v1 Announce Type: cross Abstract: World Action Models (WAMs) enable decision-making through imagined rollouts by predicting future observations and actions. However, the reliability of
arXiv:2510.22944v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have become indispensable for automated code generation, yet the quality and security of their outputs remain a c
arXiv:2605.07972v1 Announce Type: cross Abstract: Neural posterior estimation has emerged as a powerful tool for amortized inference, with growing adoption across scientific and applied domains. In ma
arXiv:2605.06883v1 Announce Type: cross Abstract: The Maximum Mean Discrepancy (MMD) is a cornerstone statistic for nonparametric two-sample testing, but its test power is dictated entirely by the cho
arXiv:2605.07865v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) has emerged as a dominant post-training paradigm for large language models, especially for reasoning domains. However, OP
arXiv:2605.05957v2 Announce Type: replace Abstract: LLMs reliably correct false claims when presented in isolation, yet when the same claims are embedded in task-oriented requests, they often comply r
arXiv:2605.06859v1 Announce Type: cross Abstract: Vision foundation models are increasingly moving beyond 2D to volumetric domains such as 3D medical imaging, where unified pretraining across differen
arXiv:2602.02832v3 Announce Type: replace Abstract: Forecasting physical systems over long horizons from irregularly sampled observations demands models that are stable, computationally efficient, and
arXiv:2605.06955v1 Announce Type: cross Abstract: Denoising score matching (DSM) provides a way to learn data distributions by training a neural network to recover the score function, defined as the g
arXiv:2605.07740v1 Announce Type: new Abstract: Mining operations are of utmost importance to the economy of some nations. However, such operations result in land-use change, very high energy consumpt
arXiv:2605.07517v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances the factual grounding of Large Language Models by conditioning their outputs on external documents. Howe
arXiv:2512.15840v2 Announce Type: replace-cross Abstract: General-purpose robots require decision-making models that generalize across diverse tasks and environments. Recent works build robot foundati
arXiv:2605.07304v1 Announce Type: new Abstract: Bandit algorithms solve diverse sequential decision-making problems, but are often too sample-inefficient for from-scratch personalization. To substanti
arXiv:2602.01166v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models benefit from chain-of-thought (CoT) reasoning, but existing approaches incur high inference overhead and rely on
arXiv:2602.09034v2 Announce Type: replace-cross Abstract: Neuroimaging does not observe causal variables directly: hemodynamics and volume conduction distort signals so that statistical dependence nee
arXiv:2605.07315v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning improves large language models (LLMs) on difficult tasks, but it also makes inference expensive because every intermedi