Knowledge catalogue
arXiv:2608.30297v1 Announce Type: new Abstract: Attributes describing data content and context can induce diverse imbalance patterns that go beyond label imbalance alone. However, existing studies pri
arXiv:2608.30197v1 Announce Type: new Abstract: Safety alignment is essential for deploying large language models, requiring systems to prevent harmful compliance while preserving helpfulness on benig
arXiv:2602.06449v2 Announce Type: replace Abstract: Privacy and computational constraints limit the use of large language models in psychiatry, while adapting small language models (SLMs) often requir
arXiv:2608.29973v1 Announce Type: new Abstract: Cryptocurrency markets generate high-frequency, multi-source data that is expensive to work with unless a team already has commercial-grade streaming an
arXiv:2608.29239v1 Announce Type: new Abstract: Low-resource ASR remains difficult because scarce transcripts provide limited supervised evidence for target-side generation. To address this gap, we pr
arXiv:2606.17446v2 Announce Type: replace-cross Abstract: Simulation enables scalable robot data collection, but raw 3D assets provide only geometry, lacking the semantic, interactive, and physical kn
arXiv:2608.30929v1 Announce Type: new Abstract: We present a family of retrieval methods for Polish statutory law built on document surrogates: language-model annotations attached to statutory article
arXiv:2608.29355v1 Announce Type: new Abstract: Large non-uniform point sets make direct attention-based surrogate modeling costly for ship hydrodynamics. We introduce APPSolver, a point-wise flow-pre
arXiv:2608.29378v1 Announce Type: cross Abstract: Arabic large language models must refuse harmful prompts without over-refusing benign or sensitive prompts, yet a single refusal rate hides this trade
arXiv:2608.29510v1 Announce Type: new Abstract: Aerial object detection is increasingly deployed in real-world applications, but models remain vulnerable to physical, universal adversarial patches tha
As we prepare to release Astra, we’re focused on making increasingly capable AI safe and broadly accessible. Astra represents a significant advance in cybersecurity capability, reaching the Critical t
arXiv:2608.28790v1 Announce Type: cross Abstract: Technical operations teams resolve large volumes of incidents by synthesizing fragmented evidence from ticket text, historical cases, system logs, and
arXiv:2608.30107v1 Announce Type: cross Abstract: Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and in
arXiv:2608.29215v1 Announce Type: new Abstract: To effectively enable people to understand new topics, explanations should be tailored to their backgrounds and abilities. So far, prompting alone has b
arXiv:2608.31007v1 Announce Type: cross Abstract: Understanding how psychiatric patients subjectively experienced a clinical conversation is important for feedback and alliance-related process monitor
arXiv:2408.01362v4 Announce Type: replace-cross Abstract: Autoencoders have found widespread application in both their original deterministic form and in their variational formulation (VAEs). In scien
arXiv:2608.28676v1 Announce Type: new Abstract: Digitized herbarium collections, now comprising over 100 million freely accessible specimen images, have become a critical resource for addressing funda
arXiv:2608.30022v1 Announce Type: new Abstract: Introduction: NICE guidelines provide evidence-based recommendations for clinical care but remain largely in unstructured natural language. Existing app
arXiv:2608.28632v1 Announce Type: new Abstract: Large language model agents can discover alphas, yet current methods have three weaknesses. The search cannot adapt during the run, automation usually e
arXiv:2608.30181v1 Announce Type: new Abstract: We introduce A.X K2, a 688B-parameter Mixture-of-Experts (MoE) language model trained from scratch as a high-performance foundation for agentic applicat
arXiv:2608.29232v1 Announce Type: cross Abstract: Object detectors are typically trained under closed-set supervision, where unlabeled regions are implicitly treated as background. Under incomplete an
arXiv:2608.29196v1 Announce Type: new Abstract: Optic disc (OD) segmentation is essential for diagnosing ophthalmic pathologies from retinal fundus images. However, prevailing deep learning approaches
arXiv:2608.30283v1 Announce Type: cross Abstract: Expected-cost constraints can still permit rare, high-cost events. Monte Carlo conditional value at risk (CVaR) gradients can be noisy at high confide
arXiv:2608.29553v1 Announce Type: new Abstract: Geospatial foundation models such as the AlphaEarth Foundation produce compact and globally consistent representations of the Earth's surface that trans
arXiv:2608.29428v1 Announce Type: new Abstract: Single-trial EEG analyses are often organized around events and latencies, yet EEG-based reaction-time (RT) prediction is posed as scalar regression on
BenchMIRT is a new method for auditing LLM benchmarks question by question, revealing which capabilities they actually measure and helping researchers build smaller, more focused, and easier-to-interp
arXiv:2606.15932v3 Announce Type: replace Abstract: While Large Language Models (LLMs) have substantially advanced text-to-code generation, many real programming tasks specify intent through visual ar
arXiv:2608.30686v1 Announce Type: cross Abstract: Coding agents are increasingly used for software engineering tasks, including bootstrapping projects from third-party repositories whose integrity can
arXiv:2608.28699v1 Announce Type: new Abstract: Understanding long-form video remains a fundamental challenge for multimodal large language models (MLLMs). Sparse frame sampling fails to capture fine-
arXiv:2608.30646v1 Announce Type: cross Abstract: Reliable uncertainty estimation is a crucial requirement for deploying large language models (LLMs) and vision-language models (VLMs) in safety-critic
arXiv:2510.25901v2 Announce Type: replace Abstract: The vulnerability of cyclists, exacerbated by the rising popularity of faster e-bikes, motivates adapting automotive perception technologies for bic
arXiv:2608.29379v1 Announce Type: new Abstract: A language-guided robot operating in a real kitchen must do more than produce a plan that appears correct. It must also execute that plan safely in clut
arXiv:2608.30076v1 Announce Type: new Abstract: Single-GPU deployment of 70B-parameter language models on an NVIDIA GPU is constrained by device memory, long-context throughput, and engineering integr
arXiv:2608.29588v1 Announce Type: new Abstract: Reasoning over text-attributed graphs (TAGs) requires large language models (LLMs) to combine a node's text with evidence distributed across its neighbo
arXiv:2608.29460v1 Announce Type: new Abstract: When coding agents encounter defective test infrastructure they may reward-hack: hardcoding outputs or editing test files to pass tests they cannot legi
arXiv:2608.25937v2 Announce Type: replace Abstract: Multi-agent systems (MAS) sometimes already have the potential to answer correctly, but still report a wrong answer. Explaining this outcome is diff
arXiv:2608.30342v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) enables photorealistic real-time novel view synthesis, yet placing a virtual camera to capture a desired frame remains larg
arXiv:2608.30922v1 Announce Type: new Abstract: Masked diffusion language models predict tokens from a partially observed response canvas, enabling bidirectional conditioning and parallel token refine
arXiv:2608.30295v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in handling long-context tasks, but processing such long contexts remains challengi
arXiv:2608.28628v1 Announce Type: new Abstract: Compound drought-to-extreme-precipitation (CDEP) events are recognized in climate science as a growing driver of extreme impact, but whether this recogn
arXiv:2608.30466v1 Announce Type: new Abstract: Generative Engine Optimization (GEO) is increasingly used to improve content visibility in LLM-based retrieval systems, yet its population-level effects
arXiv:2608.30758v1 Announce Type: new Abstract: Recent radiology multi-modal language models have made substantial progress in chest X-ray report generation, visual question answering, and temporal re
arXiv:2608.29621v1 Announce Type: cross Abstract: Long-horizon story-driven video generation requires a production agent to coordinate narrative decomposition, state tracking, shot design, prompt cons
arXiv:2608.28784v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have recently made strong progress in visual--linguistic understanding. However, their performance on text-cent
arXiv:2502.18176v3 Announce Type: replace-cross Abstract: In this paper, we aim to build an adversarially robust zero-shot image classifier. We ground our work on CLIP, a vision-language pre-trained e
arXiv:2608.30352v1 Announce Type: new Abstract: Clinical AI often optimizes predictive performance without engaging how clinicians decide where to look and what to write. We present Co-Annotator, whic
arXiv:2608.29467v1 Announce Type: new Abstract: Zero-shot anomaly detection (ZSAD) has gained significant attention for its practical value in industrial inspection. Recently, CLIP-based approaches ha
arXiv:2608.28909v1 Announce Type: new Abstract: Large-scale, publicly available datasets have driven advances in deep learning, but privacy and legal restrictions often limit data sharing in medical i
arXiv:2608.29492v1 Announce Type: new Abstract: Large Language Models (LLMs) often favor Western-associated entities across cultural contexts. Conventional debiasing methods aim for uniform neutrality
arXiv:2608.29934v1 Announce Type: new Abstract: KV-cache compression reduces LLM inference memory by evicting context tokens, but when the evicted tokens contain answer-bearing evidence, the model may
arXiv:2608.29223v1 Announce Type: new Abstract: Accurate through-thickness measurement of subsurface delamination depth in Carbon Fiber Reinforced Polymer (CFRP) is important for structural assessment
arXiv:2603.26509v2 Announce Type: replace Abstract: Computed tomography (CT) provides rich 3D anatomical detail but is often constrained by high radiation exposure, substantial costs, and limited avai
arXiv:2608.29970v1 Announce Type: new Abstract: Artistic Text Recognition (ATR) remains challenging because word images often combine decorative fonts, curved layouts, object-like characters, clutter,
arXiv:2608.29004v1 Announce Type: cross Abstract: The advances in visual information modeling and representation during the last decades are remarkable, mainly supported by Convolutional Neural Networ
arXiv:2608.30382v1 Announce Type: new Abstract: Popular adaptive stochastic gradient descent (SGD) methods to train artificial intelligence (AI) systems include the RMSprop, the Adam, and the AdamW op
arXiv:2608.28958v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has dramatically improved large language models (LLMs) by allowing them to decompose problems into intermediate steps.
arXiv:2608.28631v1 Announce Type: new Abstract: An AI scientist should not grade its own homework. Yet in the systems we examined, the agent that reviews the work usually comes from the same model fam
arXiv:2503.12204v3 Announce Type: replace Abstract: This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incre
arXiv:2608.29243v1 Announce Type: new Abstract: Existing diffusion-based enhancement methods provide strong generative capability for low-light image enhancement (LLIE), yet they either rely on paired
arXiv:2608.29939v1 Announce Type: cross Abstract: Streaming-potential-mediated transport of viscoelastic fluids has attracted research attention owing to its applications in electrokinetic energy conv