Knowledge catalogue
arXiv:2609.13548v1 Announce Type: new Abstract: Web agents can utilize reusable tools to reduce the cost and latency of low-level browser interaction, but automatically discovered tool collections can
arXiv:2609.15702v1 Announce Type: new Abstract: Pretrained score-based diffusion models provide strong unconditional priors, yet enforcing measurement or physics consistency in inverse problems is oft
arXiv:2501.03008v2 Announce Type: replace-cross Abstract: Context-aware machine translation can expose the evidence needed for pronoun choice, but standard fine-tuning does not explicitly prioritize t
arXiv:2609.15248v1 Announce Type: new Abstract: A learner probes at most k of n arms each round, receives the maximum of their rewards in [0,1], and competes with the best fixed arm. When does the pro
arXiv:2609.13869v1 Announce Type: cross Abstract: Automatic sentence function identification is important for many downstream natural language processing (NLP) applications such as dialogue systems, t
arXiv:2512.24999v2 Announce Type: replace-cross Abstract: In this work, we introduce extit{basic inequalities} for first-order iterative optimization algorithms, forming a simple yet versatile framewo
arXiv:2609.14724v1 Announce Type: new Abstract: Inferring intelligence from observable behavior is a foundational challenge in artificial intelligence. We develop a theory of Bayesian intelligence for
arXiv:2609.15555v1 Announce Type: new Abstract: Bayesian optimisation (BO) typically relies on a single global Gaussian process (GP) model as its surrogate model. However, GP regression has cubic comp
arXiv:2609.14262v1 Announce Type: new Abstract: Joint source localization and geoacoustic inversion requires optimizing an objective built from an expensive normal mode propagation model. Bayesian opt
arXiv:2609.13264v1 Announce Type: cross Abstract: Generating human-centric videos that preserve both visual identity and person-specific expressive behavior remains a fundamental challenge. In additio
arXiv:2609.15849v1 Announce Type: cross Abstract: Can LLMs reason through new information like humans, or do they merely retrieve cached opinions? This is critical for silicon sampling, where LLM pers
arXiv:2609.14806v1 Announce Type: new Abstract: Reliable online autonomy is critical for quadrotor operation in urban airspaces, where global navigation satellite systems (GNSS) measurements suffer fr
arXiv:2609.15987v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models (LLMs). We introduce Bellman Policy
arXiv:2609.15726v1 Announce Type: cross Abstract: Tactile sensing provides contact information that can be difficult to infer from vision alone, but tactile hardware for dexterous hands has not conver
arXiv:2609.15669v1 Announce Type: cross Abstract: Multimodal image registration is a key component of many clinical workflows, yet it remains challenging because corresponding anatomical structures of
arXiv:2609.13819v1 Announce Type: new Abstract: Solving inverse problems with differentiable physics simulators holds the potential to revolutionize scientific discovery and engineering design, as it
arXiv:2609.15706v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used to extract structured fields from business documents, yet most evaluations report accuracy on clean
arXiv:2609.15624v1 Announce Type: cross Abstract: Researchers assessing competent generative-AI use at work must choose among self-reports, objective tests, and measures of oversight and reliance. We
arXiv:2609.14748v1 Announce Type: new Abstract: Road tunnels remove satellite visibility where connected and automated vehicles still require continuous, attributable, and integrity-bounded positionin
arXiv:2609.14995v1 Announce Type: new Abstract: The same task and compute budget can require different reasoning policies when evidence arrives in a different order. Early computation has more time to
arXiv:2609.12606v2 Announce Type: replace Abstract: While multimodal reasoning has advanced rapidly, solving complex geometry problems critically hinges on active visual assistance, such as constructi
arXiv:2609.14352v1 Announce Type: new Abstract: AI-generated image detection has attracted increasing attention, but existing evaluations mainly focus on natural images, leaving AI-generated document
arXiv:2609.15527v1 Announce Type: new Abstract: The energy efficiency of analog computing makes it one of the most promising candidates for deploying resource-intensive machine learning workloads on c
arXiv:2609.15087v1 Announce Type: cross Abstract: Most time series forecasting benchmarks remain numerical-centric and provide limited support for evaluating contextual information that shapes real-wo
arXiv:2609.13815v1 Announce Type: new Abstract: Text-centric Visual Question Answering (VQA) requires reading and reasoning over text embedded in images, a task made substantially harder when images s
arXiv:2609.13345v1 Announce Type: cross Abstract: Probabilistic forecasting is central to decision-making under uncertainty, yet its methodological landscape has become increasingly fragmented across
arXiv:2504.01157v1 Announce Type: cross Abstract: Knowledge-intensive analytical applications retrieve context from both structured tabular data and unstructured, text-free documents for effective dec
arXiv:2609.15517v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) pose a dual-surface safety challenge: both intermediate reasoning traces and final answers can contain harmful content. Ex
arXiv:2609.14512v1 Announce Type: new Abstract: Virtual worlds now host classrooms, meetings, conferences, shops, and social venues, and nearly every interaction they expose assumes a user who can sca
arXiv:2609.15940v1 Announce Type: new Abstract: Vision-language-action policies are typically evaluated one perturbation at a time, providing a useful diagnosis of their sensitivity to individual dist
arXiv:2503.00389v2 Announce Type: replace-cross Abstract: We propose BGM2Pose, a non-invasive 3D human pose estimation method using arbitrary music (e.g., background music) as active sensing signals.
arXiv:2609.14185v1 Announce Type: cross Abstract: Bird's Eye View (BEV)-based multi-view 3D object detection suffers from challenges of computational complexity, multi-scale feature extraction, and ef
arXiv:2609.15569v1 Announce Type: cross Abstract: Large brains are metabolically costly, and associations with changing environments do not imply they evolved there, as the Cognitive Buffer Hypothesis
arXiv:2609.14343v1 Announce Type: new Abstract: Control barrier functions (CBFs) provide a mathematically grounded framework for enforcing local collision-avoidance constraints in autonomous mobile ro
arXiv:2609.15255v1 Announce Type: new Abstract: Ecological monitoring increasingly relies on machine learning models, whose performance depends on the quality and quantity of labelled data. However, o
arXiv:2609.14988v1 Announce Type: new Abstract: Background. Large language models are increasingly used to help write biomedical text but may fabricate references to nonexistent work. How often large
arXiv:2609.14279v1 Announce Type: cross Abstract: Temporal knowledge graph embedding (TKGE) models infer missing facts in knowledge graphs that evolve over time. Many existing models use a single geom
arXiv:2609.13941v1 Announce Type: new Abstract: We present BLInD (Blind Learned Intent Distribution), a compact network that maps recent vehicle-state history (e.g. speed, curvature, indicator, and ve
arXiv:2609.12394v2 Announce Type: replace Abstract: Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent ga
arXiv:2609.13592v1 Announce Type: cross Abstract: GPU memory bandwidth and capacity limit throughput in large language model (LLM) inference. The GPU memory system consists of a primary tier of high-b
arXiv:2602.01257v2 Announce Type: replace Abstract: Recently, point-supervised temporal action localization has gained significant attention for its effective balance between labeling costs and locali
arXiv:2609.15072v1 Announce Type: cross Abstract: Methods of Branched Optimal Transport (BOT) mimic the economy and efficiency of natural tree-like structures, such as those found in rivers and biolog
arXiv:2609.13290v1 Announce Type: new Abstract: Achieving biological-level running speeds has largely been pursued through advances in control algorithms, which improve the utilization of existing har
arXiv:2609.15897v1 Announce Type: cross Abstract: The last decade has seen the development of powerful methods for learning complex structure from high-dimensional data. These advances have brought to
arXiv:2609.08797v2 Announce Type: replace-cross Abstract: The Potts model extends the Ising model to multinomial data. We introduce a Rater Ising-Potts model that uses agreement indicators between pai
arXiv:2609.15055v1 Announce Type: cross Abstract: Deep learning has led to numerous proposed architectures for Automated Emotion Recognition (AER) from electrocardiogram (ECG) data, but inconsistencie
arXiv:2609.14467v1 Announce Type: cross Abstract: Automating clinical documentation from long-form doctor-patient conversations remains challenging for modern audio-language models. While cascaded ASR
arXiv:2609.14097v1 Announce Type: new Abstract: Subtomogram classification in cryo-electron tomography (cryo-ET) is a challenging problem due to the scarcity of labeled examples. While cryo-ET simulat
arXiv:2609.13335v1 Announce Type: cross Abstract: Large Language Models (LLMs) have enabled more natural human-robot interaction, but open-source models often exhibit unstable long-horizon reasoning a
arXiv:2609.15400v1 Announce Type: new Abstract: Vessel segmentation in X-ray coronary angiography (XCA) is a fundamental step for quantitative coronary analysis and subsequent assessment of coronary a
arXiv:2609.13149v1 Announce Type: cross Abstract: For local large language model agents, active context is a scarce resource: memory capacity, prefill latency, cache growth, and service objectives all
arXiv:2609.13498v1 Announce Type: cross Abstract: We report a multi-month engineering program to build Sophea, a production bilingual Greek-English automatic speech recognition system. We evaluate the
arXiv:2609.14739v1 Announce Type: cross Abstract: Large language models are increasingly used in high-stakes domains such as law, where systems must ground their reasoning in retrieved evidence and ab
arXiv:2609.15054v1 Announce Type: new Abstract: Multi-Agent (MA) systems are effective at solving complex tasks that demand planning, tool use, and the synthesis of evidence from multiple sources. Exi
arXiv:2609.15478v1 Announce Type: new Abstract: Multimodal agents can create complex videos in software such as Blender by coding without relying on diffusion models. Yet video understanding benchmark
arXiv:2609.13807v1 Announce Type: new Abstract: Large language models reason in high-dimensional hidden-state spaces, while users observe only final outputs. We introduce Bypass Observation, a non-int
arXiv:2609.15142v1 Announce Type: new Abstract: Zero-shot vision-and-language navigation in continuous environments (VLN-CE) increasingly places foundation vision-language models (VLMs) inside the nav
arXiv:2609.14956v1 Announce Type: cross Abstract: Speech Quality Assessment (SQA) is essential for modern speech technologies, and recent non-intrusive SQA predictors increasingly rely on Speech Found
arXiv:2609.14754v1 Announce Type: cross Abstract: Causal claims about large language model (LLM) internals rest on measurements. Those might include a projection, a cosine, an ablation delta, or an in
arXiv:2609.13242v1 Announce Type: cross Abstract: Subgrid parametrizations of vertical mixing in ocean models depend on free coefficients that cannot be measured directly and must be calibrated agains