Knowledge catalogue
arXiv:2609.07050v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) critically depends on retrieving the evidence necessary for effective reasoning. However, this remains particularly
arXiv:2609.05437v1 Announce Type: new Abstract: Previous AI alignment efforts have focused primarily on first-order social norms -- teaching models what is socially acceptable or unacceptable (e.g., `
arXiv:2609.08188v1 Announce Type: new Abstract: Vision-language models (VLMs) augmented with retrieval-augmented generation (RAG) benefit from access to external evidence. However, standard retrievers
arXiv:2609.06391v1 Announce Type: new Abstract: Graph-agentic retrieval-augmented generation combines structured evidence with adaptive controllers that can plan retrieval, traverse relations, verify
arXiv:2609.10377v1 Announce Type: cross Abstract: Safety is a fundamental requirement for autonomous driving, yet existing end-to-end driving models still lack explicit risk-aware learning capacities.
arXiv:2609.10018v1 Announce Type: new Abstract: EdgeAI systems are increasingly employing computer vision applications to enable intelligent, on-device decision-making in real-time. However, these dep
arXiv:2609.07194v1 Announce Type: new Abstract: We present EmoMed - a multimodal medical consultation agent that adapts its responses based on users' emotional states while maintaining clinical accura
arXiv:2609.08435v2 Announce Type: new Abstract: In persistent interactions, long contexts may encode an evolving process rather than a fixed record: later events can revise or revoke earlier informati
arXiv:2609.06155v1 Announce Type: cross Abstract: Neural models, including dense retrievers, have been widely adopted in Information Retrieval (IR), often delivering state-of-the-art performance. Desp
arXiv:2609.08418v1 Announce Type: new Abstract: Training capable cyber agents is often treated primarily as a problem of model scale, yet open-weight post-training is constrained more directly by the
arXiv:2609.07673v1 Announce Type: cross Abstract: Citation-based measures of scientific influence typically treat citations as uniform signals, ignoring the different roles that cited works play in a
arXiv:2609.06250v1 Announce Type: cross Abstract: Security Operations Centers (SOCs) process large amounts of tickets, most of which are low-interest events not worthy of further investigation. The re
arXiv:2609.09491v1 Announce Type: new Abstract: Camera pose estimation is a key step in 3D reconstruction and view-synthesis pipelines. We present a deep, global Structure-from-Motion framework based
arXiv:2609.09424v1 Announce Type: new Abstract: Longitudinal characterization of multiple sclerosis (MS) lesions remains constrained by the lack of frameworks capable of establishing consistent instan
arXiv:2609.08273v1 Announce Type: new Abstract: Agent memory systems have demonstrated significant potential in long-term dialogue, personalized assistants, and video understanding. However, continuou
arXiv:2609.10433v1 Announce Type: cross Abstract: This study develops a deep reinforcement learning framework for training Unmanned Aerial Vehicle (UAV) agents to navigate and monitor simulated wildfi
arXiv:2609.09745v1 Announce Type: new Abstract: We present the PccDiffuser, a conditional diffusion framework for continuum robots that learns a multimodal distribution over complete configuration-spa
arXiv:2609.05488v1 Announce Type: new Abstract: Clinical deterioration unfolds through coupled, partially observed trajectories, not a single diagnostic label. We introduce PGP-Clinical-TimeKAN, a tra
arXiv:2609.09162v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide a mesh-free framework for solving partial differential equations, yet their performance in nonlinear pr
arXiv:2603.22714v2 Announce Type: replace-cross Abstract: We present PopResume, a population-representative resume dataset for causal fairness auditing of LLM- and VLM-based resume screening systems.
arXiv:2609.07910v1 Announce Type: new Abstract: Multi-agent federations need governance that answers three questions under adversarial conditions: who participated (identity), did they conform (enforc
arXiv:2609.10363v1 Announce Type: new Abstract: SceneHI is a framework that lifts high-resolution, illumination-aware priors from 2D diffusion models to perform 3D texture synthesis. It is the first t
arXiv:2609.08228v1 Announce Type: new Abstract: Modern LLM agents increasingly rely on reusable skills, yet as skill libraries scale to thousands of entries, effective retrieval becomes a bottleneck.
arXiv:2609.06961v1 Announce Type: new Abstract: Existing car-following prediction methods mainly optimize trajectory accuracy, while rarely considering whether predicted disturbances propagate realist
arXiv:2609.09552v1 Announce Type: new Abstract: In this paper, we solve two fundamental obstacles in applying LLMs to critical domains: token efficiency and faithfulness. To address both constraints j
arXiv:2609.09840v1 Announce Type: new Abstract: Accurate long-term trajectory prediction for maritime vessels is essential for safety and logistical efficiency. While deep learning models, particularl
arXiv:2603.17523v2 Announce Type: replace Abstract: Neural operators (NOs) are powerful deep learning frameworks designed to learn solution operators of partial differential equations. This study eval
arXiv:2609.06715v1 Announce Type: new Abstract: The contemporary debate over machine consciousness begins from a concealed assumption: that the object called 'AI' already constitutes the kind of entit
arXiv:2609.09372v1 Announce Type: cross Abstract: Although MMLU is widely adopted as a benchmark for calibrating general AI capabilities, we psychometrically demonstrate that its aggregate score prima
arXiv:2609.06941v1 Announce Type: new Abstract: Causal effect estimation asks how an outcome would change under an intervention, and medicine, economics, and public policy all treat it as a foundation
arXiv:2609.10261v1 Announce Type: new Abstract: Multi-modal medical image segmentation leverages complementary diagnostic information, yet fusion can underperform single-modality baselines when spatia
arXiv:2609.08230v1 Announce Type: new Abstract: Chunk-autoregressive video world models typically condition each generated chunk on one action. An action received during sampling must therefore wait f
arXiv:2606.19993v2 Announce Type: replace Abstract: We present Activation- and Influence-Aware Ranks (AIR), an SVD-based LLM compression framework that guides each weight matrix's low-rank approximati
arXiv:2609.08777v1 Announce Type: new Abstract: Hyperspectral images (HSIs) are often degraded by mixed noise, including band-dependent Gaussian perturbations and structured artifacts such as stripes,
arXiv:2609.08220v1 Announce Type: new Abstract: Large language models (LLMs) have recently emerged as a promising tool for automating robot design from high-level specifications, yet they remain ineff
arXiv:2609.07070v1 Announce Type: new Abstract: Spatio-temporal context has become increasingly crucial for visual tracking. However, most existing approaches extract spatio-temporal cues via discrete
arXiv:2609.05520v1 Announce Type: new Abstract: In this paper, we tackle automatic anomaly detection in multi-illumination and multi-focus display images. The minute defects on the display surface are
arXiv:2609.07336v1 Announce Type: new Abstract: Morphology of the sub-basal nerve plexus (SNP) reflects peripheral nerve health, and corneal confocal microscopy (CCM) provides an important means for i
arXiv:2609.09099v1 Announce Type: new Abstract: Curriculum learning is governed by several coupled design choices---how difficulty is defined, how examples are ordered, how much exposure each level re
arXiv:2510.23258v2 Announce Type: replace-cross Abstract: Autonomous robotic navigation in real-world environments requires exploration to acquire environmental information as well as goal-directed na
arXiv:2609.07110v1 Announce Type: new Abstract: Natural adversarial examples (NAEs) reveal that vision models can fail under realistic semantic changes beyond norm-bounded perturbations. However, gene
arXiv:2609.05525v1 Announce Type: new Abstract: Large vision-language models (VLMs) are increasingly deployed in safety-critical settings, yet existing visual jailbreak research has focused almost exc
arXiv:2609.09005v1 Announce Type: new Abstract: We present DXPR, a depth-based cross-modal place recognition (CMPR) framework that uses vision foundation models (VFMs) to match monocular camera querie
arXiv:2609.08705v1 Announce Type: new Abstract: Table Structure Recognition (TSR) aims to extract the bounding boxes of cells and table structure (e.g., HTML) from table images. Although current appro
arXiv:2609.07147v1 Announce Type: new Abstract: Federated learning, as a privacy-preserving distributed machine learning paradigm, faces significant threats from backdoor attacks. Compared to centrali
arXiv:2609.08300v1 Announce Type: new Abstract: While multimodal large language models (MLLMs) achieve remarkable performance on generic image captioning, they frequently suffer from structural halluc
arXiv:2609.06522v1 Announce Type: new Abstract: A hybrid hierarchical learning framework is proposed to achieve high-precision assembly of 170mm cylindrical components with tolerance of 0.1mm. The low
arXiv:2509.04772v2 Announce Type: replace Abstract: Timely floodwater depth estimates support road accessibility assessment and emergency response during urban flooding. Supervised vision methods ofte
arXiv:2609.06341v1 Announce Type: new Abstract: Linear algebra provides the framework of concepts (matrix rank, singular value decomposition (SVD), and eigendecomposition) that modern artificial intel
arXiv:2609.07029v1 Announce Type: new Abstract: Localizing a font into new languages is a highly intricate task requiring precise design adaptation of glyphs, color/texture, and spacing/kerning, from
arXiv:2609.08283v1 Announce Type: new Abstract: Contrastive Language-Image Pre-training (CLIP) has demonstrated impressive capabilities in zero-shot transfer but often struggles with dense prediction
arXiv:2509.04757v2 Announce Type: replace Abstract: Hurricanes cause widespread damage to buildings, roads, and other infrastructure, making timely post-disaster damage assessment critical for emergen
arXiv:2609.05436v1 Announce Type: cross Abstract: Protein scaffold gap filling is an important computational task in protein sequence reconstruction, where missing amino acid regions must be inferred
arXiv:2609.06490v1 Announce Type: new Abstract: Recursive Super-Resolution (SR) extends fixed-scale SR to extreme magnification by repeatedly feeding predictions back into the same model, analogous to
arXiv:2609.08992v1 Announce Type: new Abstract: False ventricular tachycardia (VT) alarms are a leading contributor to alarm fatigue in intensive care units. We propose a deep learning framework combi
arXiv:2609.05542v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) provide a mesh-free framework for solving governing equations, but their application to granular avalanche dy
arXiv:2609.05953v1 Announce Type: new Abstract: Few-shot fine-grained object detection (FGOD) in remote sensing imagery is challenging because limited annotations must support both object localization
arXiv:2609.05952v1 Announce Type: new Abstract: Nearest-neighbor classification is widely used in machine learning, yet existing methods often suffer from low computational efficiency and limited robu
arXiv:2512.23243v3 Announce Type: replace Abstract: Multimodal vision-language modeling has emerged as a promising paradigm for remote sensing (RS) image understanding. However, existing methods are l
arXiv:2512.12749v3 Announce Type: replace-cross Abstract: Learning surrogate models for physical systems with latent uncertainty remains challenging in data-scarce regimes: deterministic neural operat