Knowledge catalogue
arXiv:2603.02512v4 Announce Type: replace-cross Abstract: Human-Certified Module Repositories (HCMRs) are introduced in this work as a new architectural model for constructing trustworthy software in
arXiv:2605.17426v1 Announce Type: cross Abstract: We propose a framework for predicting the effects of mobility introduction measures using a human-flow digital twin. This digital twin incorporates a
arXiv:2602.02039v2 Announce Type: replace Abstract: The agency expected of Agentic Large Language Models goes beyond answering correctly, requiring autonomy to set goals and decide what to explore. We
arXiv:2605.17481v1 Announce Type: new Abstract: Nowadays, people in Bangladesh frequently rely on the internet and social media for daily news instead of traditional newspapers. However, the spread of
arXiv:2605.18345v1 Announce Type: cross Abstract: Hybrid quantum-classical neural networks (HQNNs) are emerging as a practical approach for quantum machine learning in the noisy intermediate-scale qua
arXiv:2605.16404v1 Announce Type: new Abstract: Extracting actionable knowledge from industrial visual data is fundamentally bottlenecked by extreme class imbalance and the prohibitive computational c
arXiv:2605.17106v1 Announce Type: new Abstract: Production LLM deployments increasingly maintain heterogeneous model pools spanning order-of-magnitude cost differences. Existing routers make binary st
arXiv:2605.17792v1 Announce Type: new Abstract: Calibrating distributed hydrologic models is a critical bottleneck across operational water resources management - streamflow prediction, reservoir oper
arXiv:2605.16527v1 Announce Type: cross Abstract: Hypergraphs model higher-order relations that drive real-world decisions, from drug prescriptions to recommendations. A central structural signal in s
arXiv:2605.17355v1 Announce Type: new Abstract: As a modern commodity, language has become a vast repository of socially and psychologically significant traits and concepts, reflecting the ways people
arXiv:2508.10678v2 Announce Type: replace Abstract: In practical application scenarios, moving infrared small target detection (MIRSTD) remains highly challenging due to the target's small size, weak
arXiv:2605.17286v1 Announce Type: new Abstract: While hyperspectral imaging provides rich spatial-spectral information across hundreds of narrow wavelength bands for precise material identification, g
arXiv:2605.16836v1 Announce Type: cross Abstract: Hypergraphs provide a principled framework for modeling polyadic interactions, with applications in recommendation systems, social networks, and molec
arXiv:2605.16457v1 Announce Type: cross Abstract: Transformer-based world models have shown strong performance in visual reinforcement learning, but often suffer from temporal inconsistency in long-ho
arXiv:2605.16682v1 Announce Type: new Abstract: Identifying latent state representations and dynamics is essential when direct modeling in observation space is infeasible, particularly under partial a
arXiv:2603.17577v2 Announce Type: replace-cross Abstract: Can latent actions and environment dynamics be recovered from offline trajectories when actions are never observed? We study this question in
arXiv:2603.00607v2 Announce Type: replace-cross Abstract: Multi-subject image generation requires seamlessly harmonizing multiple reference identities within a coherent scene. However, existing method
arXiv:2605.16816v1 Announce Type: new Abstract: Human-robot collaboration (HRC) can benefit from robots' abilities to interpret human emotional states. However, current emotion recognition (ER) models
arXiv:2605.17248v1 Announce Type: new Abstract: Diffusion-based extit{image-to-video} (I2V) generation has become a central direction in generative models by turning a reference image, with optional c
arXiv:2605.17865v1 Announce Type: new Abstract: LiDARs are being increasingly deployed for consumer imaging in handheld, wearable, and robotic applications. These sensors can capture the time-of-fligh
arXiv:2605.17179v1 Announce Type: new Abstract: Emotion understanding is a fundamental challenge in affective computing and artificial intelligence. While existing approaches predominantly focus on fa
arXiv:2605.18500v1 Announce Type: new Abstract: Large language models (LLMs) have increasingly leveraged tool invocation to enhance their reasoning capabilities. However, existing approaches typically
arXiv:2605.18324v1 Announce Type: cross Abstract: Representation Autoencoders (RAE) replace traditional VAE with pretrained vision encoders. In this paper, we systematically investigate several design
arXiv:2605.18561v1 Announce Type: cross Abstract: In retrieval-augmented coding, failures often begin when the relevant file is absent from the retrieved context. Under frozen generic tokenization, wh
arXiv:2509.18150v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated outstanding performance across a variety of domains. However, training MLLMs is oft
arXiv:2505.06852v2 Announce Type: replace Abstract: Random forest regression is a powerful non-parametric method that adapts to local data characteristics through data-driven partitioning, making it e
arXiv:2605.18068v1 Announce Type: cross Abstract: Residual error propagation remains a fundamental problem in recurrent models, where small prediction inaccuracies compound over time and degrade long-
arXiv:2605.16665v1 Announce Type: new Abstract: Continental-scale knowledge of subsurface temperature is limited by the cost and sparsity of borehole measurements, but such information is essential fo
arXiv:2605.18601v1 Announce Type: new Abstract: Modern interactive video world models have achieved impressive visual fidelity, yet lack fine-grained multi-entity control and cross-entity, cross-world
arXiv:2603.20873v2 Announce Type: replace Abstract: Federated learning (FL) is a communication-efficient collaborative learning framework that enables model training across multiple agents with privat
arXiv:2509.07793v4 Announce Type: replace-cross Abstract: How should well-being be prioritised in society, and what trade-offs are people willing to make between fairness and personal well-being? We i
arXiv:2605.16390v1 Announce Type: new Abstract: We investigate whether the training protocol can induce spatial locality in the early layers of a Vision Transformer (ViT) trained from scratch, without
arXiv:2510.24680v2 Announce Type: replace Abstract: While imitation learning (IL) has enabled successful visual navigation in many common environments, IL policies are prone to unpredictable failures
arXiv:2601.16172v2 Announce Type: replace Abstract: RL-trained Lean theorem provers mode-collapse at inference time: on miniF2F-test with DeepSeek-Prover-V1.5-RL, doubling the i.i.d. sampling budget f
arXiv:2605.18337v1 Announce Type: new Abstract: Large-scale news corpora support a wide range of research in Computational Social Science and NLP, yet access remains constrained: commercial archives i
arXiv:2605.17930v1 Announce Type: new Abstract: While the approximation properties of single-layer Transformer architectures have been studied in recent works, a rigorous theoretical understanding of
arXiv:2602.18217v2 Announce Type: replace Abstract: Real-time sentence comprehension imposes a significant load on working memory, as comprehenders must maintain contextual information to anticipate f
arXiv:2605.16809v1 Announce Type: new Abstract: The quality of graph-structured data is fundamental to the success of modern graph analysis techniques such as Graph Neural Networks (GNNs). However, re
arXiv:2605.18467v1 Announce Type: new Abstract: Recent diffusion-based methods have achieved impressive progress in video content manipulation. However, they typically ignore the accompanying audio, l
arXiv:2605.17219v1 Announce Type: cross Abstract: Artificial Intelligence (AI) is widely adopted today for its ability to detect patterns, automate tasks, and reduce time and cost across various appli
arXiv:2605.18006v1 Announce Type: cross Abstract: Because LiDAR sensors acquire point clouds with a fixed angular resolution, the resulting data can be systematically parameterized and efficiently com
arXiv:2605.18024v1 Announce Type: cross Abstract: Cooperation is central to multi-agent reinforcement learning (MARL), yet learned coordination can be fragile when external perturbations disrupt inter
arXiv:2605.17829v1 Announce Type: new Abstract: AI evaluation is undergoing a structural change. Large language models (LLMs) are increasingly deployed as systems that act over time through tools, env
arXiv:2605.17774v1 Announce Type: new Abstract: Large language models are increasingly used as planning components in agentic systems, but current tool-use pipelines often require full tool schemas to
arXiv:2510.21523v2 Announce Type: replace Abstract: Sequential generative models conditioned on uncertain rewards are central to AI-driven scientific discovery, yet the epistemic uncertainty they inhe
arXiv:2305.07152v4 Announce Type: replace Abstract: Robotic assisted (RA) surgery promises to transform surgical intervention. Intuitive Surgical is committed to fostering these changes and the machin
arXiv:2505.09203v2 Announce Type: replace-cross Abstract: Developing inverse design methods for functional materials with specific properties is critical to advancing fields like renewable energy, cat
arXiv:2605.16528v1 Announce Type: cross Abstract: The latest improvements in artificial intelligence (AI) raise new challenges for intellectual property laws, particularly concerning the inventorship
arXiv:2605.16318v1 Announce Type: new Abstract: Building and maintaining state to learn policies and value functions is critical for deploying reinforcement learning (RL) agents in the real world. Rec
arXiv:2605.18113v1 Announce Type: new Abstract: Prompt optimization has often been framed as a discrete search problem to find high-performing and robust instructions for an LLM. However, the search r
arXiv:2605.17799v1 Announce Type: new Abstract: Long-tailed out-of-distribution (LT-OOD) detection is often addressed with specialized training, including auxiliary out-of-distribution (OOD) data, abs
arXiv:2605.17268v1 Announce Type: new Abstract: We present the first systematic study of faithfulness in Vision-Language-Action (VLA) driving models, analyzing 300 Alpamayo-R1-10B inferences across 10
arXiv:2605.18320v1 Announce Type: cross Abstract: Offline reinforcement learning methods typically enforce strict constraints to ensure safety; yet this rigidity often prevents the discovery of optima
arXiv:2605.16708v1 Announce Type: new Abstract: Learning meaningful latent representations from nonlinear fMRI data remains a fundamental challenge in neuroimaging analysis. Traditional independent co
arXiv:2605.16571v1 Announce Type: cross Abstract: Time-to-event data is widespread across the life sciences and engineering, but it is typically encountered together with censoring, which complicates
arXiv:2605.17251v1 Announce Type: cross Abstract: Recent work due to Goel et al. gave the first efficient algorithms for learning with distribution shift in the challenging PQ framework. In this setti
arXiv:2605.17415v1 Announce Type: cross Abstract: We propose IVF-TQ, an IVF index with a codebook-free residual layer: a fixed random rotation followed by precomputed Lloyd-Max scalar quantization dep
arXiv:2605.16812v1 Announce Type: new Abstract: While Local Differential Privacy (LDP) serves as a foundational primitive for distributed data collection, its stringent noise injection requirement oft
arXiv:2603.29167v2 Announce Type: replace Abstract: We study a systems-level visual inference problem: using an expensive privileged modality during training while preserving a fixed-cost, single-moda
arXiv:2602.15405v2 Announce Type: replace Abstract: Robust classification in noisy environments remains a fundamental challenge in machine learning. Standard approaches typically treat signal enhancem