Knowledge catalogue
arXiv:2608.26198v1 Announce Type: new Abstract: Operating a scientific instrument such as an atomic force microscope (AFM) requires continuous expert decision-making. A trained user defines the experi
arXiv:2608.26130v1 Announce Type: new Abstract: Coding agents built on large language models (LLMs), such as Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Aider, receive tool responses that r
arXiv:2608.26993v1 Announce Type: new Abstract: Explicit visual intermediates can help multimodal large language models (MLLMs) externalize spatial evidence and updated visual states, but their utilit
arXiv:2510.11297v2 Announce Type: replace Abstract: Knowledge graphs (KGs) are widely used in knowledge-intensive applications, yet it remains unclear how effectively current large language models (LL
arXiv:2608.26152v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit unprecedented natural language generation and many text-based problem-solving capabilities. Indeed, in many langu
arXiv:2608.26992v1 Announce Type: new Abstract: Representation learning has attracted great atten- tion and managed to reach good performances as a pretraining method for downstream tasks or as a firs
arXiv:2608.26753v1 Announce Type: cross Abstract: LLM agents used for scientific experimentation must do more than generate executable code: they must implement the reference method faithfully, design
arXiv:2608.26685v1 Announce Type: cross Abstract: Identifying white space --- the unexplored but potentially valuable regions of a patent landscape --- is essential for strategic R&D planning, yet exi
arXiv:2608.27149v1 Announce Type: new Abstract: Conversational AI systems, such as chatbots and virtual assistants, are becoming increasingly important to digital business processes. However, the esta
arXiv:2504.09409v2 Announce Type: replace-cross Abstract: In this paper, we study nonconvex constrained stochastic zeroth-order optimization problems, for which we have access to exact information of
arXiv:2608.27167v1 Announce Type: new Abstract: An LLM agent shown a professional-looking market panel commits to a directional call on a provably unpredictable question far more often than one asked
arXiv:2608.26414v1 Announce Type: new Abstract: Medical guidelines encode rich, evidence-based decision logic, yet the specific decision artifact a clinician needs is hard to locate within a guideline
arXiv:2608.26819v1 Announce Type: new Abstract: Operational requirements developed with the City of Braunschweig frame municipal micromobility planning under geofenced exclusions, mandatory retained s
arXiv:2608.26386v1 Announce Type: cross Abstract: Retrieval-augmented methods improve factual accuracy by grounding language models in external knowledge, but retrieving over unstructured text often i
arXiv:2608.27203v1 Announce Type: new Abstract: A known necessary condition for Fisher consistency of the structured support vector machine requires the task loss to be a metric for which every output
arXiv:2608.26932v1 Announce Type: new Abstract: Accurate state estimation for autonomous underwater vehicles performing close-range seafloor sampling remains challenging. In low-altitude operation, do
arXiv:2608.26383v1 Announce Type: new Abstract: Autonomous robots performing laboratory tasks depend on 3D reconstruction pipelines that can turn raw camera streams into actionable object representati
arXiv:2608.26788v1 Announce Type: new Abstract: Recent work shows that pre-trained, instruction-tuned vision-language models (VLMs) perform well at mapping from instructions and observations to high-l
arXiv:2608.26757v1 Announce Type: new Abstract: Faithful chart generation in real-world data-science workflows requires grounding visualizations in scattered evidence, computing chart-ready quantities
arXiv:2608.26574v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) offer a promising alternative to autoregressive generation by decoding multiple tokens in parallel through itera
arXiv:2608.27034v1 Announce Type: new Abstract: This paper proposes a fully differentiable jitter correction method for X-ray phase-contrast micro computed tomography using a deep learning-based image
arXiv:2608.27158v1 Announce Type: new Abstract: Robot crowd navigation requires safe and efficient decision-making under dense, dynamic, and multimodal human--robot interactions. Existing reinforcemen
arXiv:2608.26314v1 Announce Type: new Abstract: Steering-based planners require solutions to state-to-state boundary value problems, which can be inaccessible for nonlinear platforms. Forward propagat
arXiv:2608.26299v1 Announce Type: cross Abstract: Urban food environments may reflect broader socioeconomic inequalities, but district-level evidence remains limited in Brazilian cities. This study ex
arXiv:2607.15439v2 Announce Type: replace Abstract: Our previous ARC-AGI-3 agent bundled executable world modeling, prompted simplification, and exact replay verification, leaving their individual con
arXiv:2608.26686v1 Announce Type: new Abstract: Despite the growing number of public datasets, annotated medical images remain scarce. Supervised learning methods achieve strong performance on many be
arXiv:2608.26589v1 Announce Type: new Abstract: Image-to-Point Cloud Registration aims to estimate the camera pose of a given image within a 3D scene point cloud, which is a fundamental task in autono
arXiv:2510.04802v2 Announce Type: replace-cross Abstract: Observing surgical practice has historically relied on fixed vantage points or recollections, leaving the egocentric perspectives that shape c
arXiv:2608.27371v1 Announce Type: new Abstract: This paper introduces Embodied Scene Rearrangement Planning (ESRP), a novel task requiring embodied agents to rearrange furniture in 3D scenes to match
arXiv:2608.27072v1 Announce Type: cross Abstract: A core question in decision-making for agents is whether the relative priorities of competing lower-level objectives can be determined by emotional pr
arXiv:2608.26762v1 Announce Type: new Abstract: Rerankers, reward models and multi-document QA scorers score candidate documents or responses in one LLM prompt, so each score depends on their order. S
arXiv:2608.26209v1 Announce Type: cross Abstract: Data-driven software systems are increasingly deployed in high-stakes socio-economic domains, from criminal justice to financial lending. However, the
arXiv:2608.26746v1 Announce Type: cross Abstract: Generated operational programs are often validated with either a few hand-written examples or exhaustive regression suites. The former can miss sparse
arXiv:2608.26430v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) strategies such as Low-Rank Adaptation (LoRA) are effective solutions for fine-tuning large-scale pre-trained mod
arXiv:2608.26129v1 Announce Type: cross Abstract: Scientific peer review datasets have trained AI systems exclusively on Computer Science and Machine Learning venues, producing models that critique ab
arXiv:2608.27029v1 Announce Type: cross Abstract: Training molecular machine-learning models on ephemeral or memory-constrained accelerator instances can require repeatedly retrieving preprocessed mol
arXiv:2606.08357v2 Announce Type: replace Abstract: Diffusion language models generate text through iterative denoising, offering a powerful alternative to autoregressive generation. However, discrete
arXiv:2505.22203v3 Announce Type: replace-cross Abstract: Trustworthy verifiers are essential for the success of reinforcement learning with verifiable reward (RLVR), which is the core methodology beh
arXiv:2608.26856v1 Announce Type: cross Abstract: Although Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in Medical Visual Question Answering (Med-VQA), their relia
arXiv:2608.26163v1 Announce Type: cross Abstract: Cough events during live spoken conversations carry clinically valuable respiratory signals, yet existing dialogue systems treat them as acoustic nois
arXiv:2608.26607v1 Announce Type: new Abstract: Oralscan image segmentation is essential for computer-aided diagnosis and treatment planning in digital dentistry. However, existing visual state space
arXiv:2608.27142v1 Announce Type: new Abstract: Despite their potential in standardized graph tasks, Large Language Models (LLMs) remain brittle to real-world shifts in node identifiers and task formu
arXiv:2608.26157v1 Announce Type: new Abstract: Natural-language analytics over enterprise data warehouses is increasingly important, but production use is limited by hallucinated metrics, invalid joi
arXiv:2604.02324v2 Announce Type: replace-cross Abstract: Language models (LMs) are increasingly extended with new learnable vocabulary tokens for domain-specific tasks, such as Semantic-ID tokens in
arXiv:2608.27233v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using inertial measurement units (IMUs) enables a wide range of applications, yet the field still lacks a unified model
arXiv:2608.26517v1 Announce Type: new Abstract: Visual intelligence seeks to perceive, interpret, and synthesize the visual world and is central to modern computer vision. Human-centered visual intell
arXiv:2608.26665v1 Announce Type: new Abstract: Maritime vessel detectors often face scenes where hulls are small, low-contrast, or blurred, while wakes are longer and easier to detect. This creates a
arXiv:2505.15740v2 Announce Type: replace-cross Abstract: Formal methods play a crucial role in ensuring the reliability of critical systems through rigorous mathematical verification. However, their
Impressive new work from Google DeepMind showing real-world applications of agents for scientific research. Impressive new paper from Google DeepMind. (bookmark it) It takes Co-Scientist out of simula
arXiv:2608.27031v1 Announce Type: new Abstract: Measuring alignment between documents and structured reference frameworks requires identifying conceptual evidence distributed throughout the text and r
arXiv:2608.27365v1 Announce Type: cross Abstract: Physics-integrated 3D Gaussian representations now allow reconstructed deformable objects to be simulated and rendered under explicit material models.
arXiv:2608.27102v1 Announce Type: new Abstract: Large Language Models (LLMs) operate in hospitals, courtrooms, banks, and public service desks, where fluent, confident outputs are treated as authorita
arXiv:2608.27421v1 Announce Type: new Abstract: Currently used sepsis severity indices rely on fixed variables and weights established decades ago, which are coarsely discretized and calibrated to a c
arXiv:2608.27077v1 Announce Type: cross Abstract: Extracting transverse momentum dependent parton distribution functions (TMD PDFs) from semi-inclusive deep inelastic scattering (SIDIS) data is a cent
arXiv:2608.27395v1 Announce Type: cross Abstract: Video carries the temporal structure of the physical world, yet learning representations from it has remained computationally expensive: prevailing se
arXiv:2506.14496v3 Announce Type: replace Abstract: Swarm intelligence describes how simple, decentralized agents can collectively produce complex behaviors. Recently, the concept of swarming has been
arXiv:2608.26175v1 Announce Type: cross Abstract: Extractive prompt compression promises to cut LLM inference costs by removing low-information tokens, and learned compressors such as LLMLingua-2 repo
arXiv:2608.26925v1 Announce Type: new Abstract: In many low-resource settings, even just eliciting speech for data collection is difficult. One promising approach has been to ask speakers to describe
arXiv:2608.27271v1 Announce Type: cross Abstract: The marine science community increasingly relies on Marine Autonomous Vehicles (MAVs) to collect the critical environmental data required to understan
arXiv:2608.27429v1 Announce Type: new Abstract: Chemical reactions are fundamentally transformations in electron space, yet most machine learning approaches model them either through extit{de novo} ge