Knowledge catalogue
arXiv:2605.26362v1 Announce Type: cross Abstract: In many reasoning tasks, large language models (LLMs) rely on structured external knowledge, such as graphs and tables, which is typically linearized
arXiv:2605.26655v1 Announce Type: new Abstract: Automated prompt optimization methods (e.g., DSpy, TextGrad) can substantially improve the performance of large language model (LLM), however, their gen
arXiv:2605.26660v1 Announce Type: new Abstract: Quantization is an effective approach to reduce the memory footprint and inference cost of large language models (LLMs), yet maintaining performance in
arXiv:2605.26200v1 Announce Type: cross Abstract: This paper argues that workflow closure is not scientific closure in auto-research systems. Current systems can increasingly complete research-like lo
arXiv:2605.26118v1 Announce Type: cross Abstract: Porting deep learning algorithms to new hardware accelerators requires developers to repeatedly apply the same low-level optimizations -- quantization
arXiv:2601.04426v3 Announce Type: replace Abstract: Modern LLM agents increasingly rely on dynamic structured generation, such as tool calling and response protocols. Unlike traditional structured gen
arXiv:2502.17666v4 Announce Type: replace-cross Abstract: Existing offline in-context reinforcement learning (ICRL) methods have predominantly relied on supervised training objectives, which are known
arXiv:2605.27129v1 Announce Type: new Abstract: In greenhouse tomato production, automated harvesting requires accurate detection of ripe tomatoes, ripeness classification, and precise picking-point l
arXiv:2605.26302v1 Announce Type: new Abstract: Long-lived AI agents are increasingly deployed as persistent operational systems, yet they are still evaluated like freshly initialized models. Day-one
arXiv:2601.16578v2 Announce Type: replace Abstract: We present a reproducible benchmark for evaluating sim-to-real transfer of Multi-Agent Reinforcement Learning (MARL) policies for Connected and Auto
arXiv:2605.26383v1 Announce Type: new Abstract: Object re-identification (ReID) in egocentric kitchen videos is challenging due to rapid viewpoint changes, frequent occlusions, cluttered scenes, and l
arXiv:2605.25640v1 Announce Type: cross Abstract: Accurate reconstruction of magnetic fields in inaccessible regions is vital for many high-precision experiments in physics. Traditional methods, such
arXiv:2605.25228v1 Announce Type: new Abstract: Concerns about algorithmic bias and fairness have increased as artificial intelligence has been incorporated into high-stakes decision-making. Tradition
arXiv:2505.22322v3 Announce Type: replace Abstract: Diffusion models have shown strong performance in generating high-quality tabular data, but they carry privacy risks by reproducing exact training s
arXiv:2605.24868v1 Announce Type: new Abstract: Temporal surrogate models are effective for predicting chaotic dynamical systems where computational cost can be prohibitive. Several deep neural networ
arXiv:2601.00553v2 Announce Type: replace-cross Abstract: Multimodal generative AI systems like Stable Diffusion, DALL-E, and MidJourney have fundamentally changed how synthetic images are created. Th
arXiv:2510.22874v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has led to increasingly human-like AI-generated text, raising concerns about content authentic
arXiv:2605.24752v1 Announce Type: new Abstract: We study learning-to-sample -- a basic algorithmic task underlying generative modeling -- for Ising models, a standard testbed for algorithmic ideas in
arXiv:2605.25391v1 Announce Type: new Abstract: We study the restless contextual multi-play multi-armed bandit (MP-MAB) problem for channel allocation in the opportunity spectrum access (OSA) scenario
arXiv:2605.24759v1 Announce Type: new Abstract: Discounted reinforcement learning is usually presented through Bellman equations on closed Markov decision processes. This paper develops a compositiona
arXiv:2605.25502v1 Announce Type: cross Abstract: Educational aspect-based sentiment analysis (ABSA) can support course improvement, but public aspect-labeled student feedback remains scarce because e
arXiv:2605.25051v1 Announce Type: new Abstract: Decentralized multi-robot LiDAR-SLAM is essential for collaborative missions but faces significant challenges in maintaining global consistency. Existin
arXiv:2605.25735v1 Announce Type: new Abstract: Problem formulation translating customer needs and constraints into a minimum set of independent first-level functional requirements, is arguably the mo
arXiv:2605.23942v1 Announce Type: new Abstract: This paper proposes a structural and dynamical framework for modeling cognitive processes within a cybernetic perspective. Cognitive states are represen
arXiv:2510.06351v2 Announce Type: replace Abstract: Planning safe trajectories under model uncertainty is a fundamental challenge. Robust planning ensures safety by considering worst-case realizations
arXiv:2605.25344v1 Announce Type: cross Abstract: Large language models (LLMs) are dominated by dense linear transformations, whose storage, memory and computational overheads hinder efficient adaptat
arXiv:2507.02628v2 Announce Type: replace Abstract: The reliability of clinical artificial intelligence (AI) depends on high-quality data, yet Electronic Health Records are often inconsistent with exi
arXiv:2605.24383v1 Announce Type: new Abstract: Ethical constraints on open-weight AI models are both a reflection of societal concerns and a foundation for AI governance policy. They are expected to
arXiv:2409.03777v3 Announce Type: replace-cross Abstract: Deep convolutional neural networks (CNNs) have achieved impressive performance in many computer vision tasks. However, their large model sizes
arXiv:2605.24045v1 Announce Type: cross Abstract: Protein-ligand modeling underpins computational drug discovery and molecular design. Existing protein-ligand benchmarks typically evaluate whether a p
arXiv:2512.21208v3 Announce Type: replace Abstract: We develop a finite-dimensional sensitivity framework for studying stability in learning systems whose states include representations, parameters, a
arXiv:2605.24274v1 Announce Type: new Abstract: Input-convex neural networks (ICNNs) are widely used for log-concave density estimation, convex-potential normalizing flows, optimal transport, and tran
arXiv:2605.25463v1 Announce Type: new Abstract: MedER refers to the identification of medical entities. It is crucial for extracting structured clinical information from unstructured medical text. Man
arXiv:2605.24324v1 Announce Type: cross Abstract: Quantum machine learning is often motivated by the idea that quantum systems can expose useful high-dimensional structure that is difficult to access
arXiv:2605.25440v1 Announce Type: cross Abstract: Verbal feedback delivered by attending surgeons in the operating room plays a critical formative role in resident trainee skill acquisition. Yet, asse
arXiv:2605.23977v1 Announce Type: new Abstract: This paper audits benchmark evaluation in clinical-interview depression detection through four complementary probes across DAIC/E-DAIC, CMDC, ANDROIDS,
arXiv:2605.26026v1 Announce Type: cross Abstract: Light sheet fluorescence microscopy (LSM) enables high-resolution, three-dimensional (3D) imaging of biological specimens, providing rich volumetric d
arXiv:2605.25540v1 Announce Type: cross Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia, affecting memory, reasoning, communication, an
arXiv:2411.06278v4 Announce Type: replace-cross Abstract: We propose a scalable preconditioned primal-dual hybrid gradient algorithm for solving partial differential equations (PDEs). We multiply the
arXiv:2502.16205v3 Announce Type: replace Abstract: Picking manipulators are task specific robots, with fewer degrees of freedom compared to general-purpose manipulators, and are heavily used in indus
arXiv:2605.25011v1 Announce Type: new Abstract: We consider the challenge of developing agents that efficiently interact with high-dimensional, evolving environments, towards a view of practical reinf
arXiv:2605.25299v1 Announce Type: cross Abstract: Recently, Deep Image Prior (DIP) has demonstrated strong capabilities for solving inverse imaging problems (IIPs) by optimizing a randomly initialized
arXiv:2605.24436v1 Announce Type: cross Abstract: Selecting the most suitable algorithm for a given problem instance remains a challenging task, particularly in online or dynamic environments where pr
arXiv:2605.25446v1 Announce Type: new Abstract: Electrocardiography (ECG) is central to cardiovascular care, but conventional AI models are often restricted to common arrhythmias and may generalize po
arXiv:2605.24197v1 Announce Type: new Abstract: We study a class of emergent misalignment in multi-agent systems (MAS), with a focus on automated workflows, which we refer to agentic misalignment. Alt
arXiv:2510.20954v3 Announce Type: replace-cross Abstract: Graphons, as limits of graph sequences, provide an operator-theoretic framework for analyzing the asymptotic behavior of graph neural operator
arXiv:2605.24006v1 Announce Type: cross Abstract: Pipeline parallelism is a key technique for distributed training of large language models because it reduces per-device parameter and activation memor
arXiv:2605.25536v1 Announce Type: cross Abstract: Context. Large language models (LLMs) are increasingly applied to code-generating tasks (CGTs) in software engineering. While reported results are pro
arXiv:2605.25422v1 Announce Type: cross Abstract: The convergence of large language models (LLMs) with 6G networks is fostering a paradigm of autonomous multi-agent cooperation, which in turn is expec
arXiv:2605.25652v1 Announce Type: new Abstract: Free-form legal essay evaluation in NLP treats expert inter-rater stability as a single ceiling number, and treats LLM-judge agreement with that ceiling
arXiv:2605.24406v1 Announce Type: new Abstract: Optimizing HVAC (Heating, Ventilation and Air Conditioning) can enhance a building's energy efficiency while providing comfort levels for its occupants.
arXiv:2601.05289v2 Announce Type: replace-cross Abstract: The high-dimensional complex nature of detectors makes fast calorimeter simulations a prime application for modern generative machine learning
arXiv:2605.23992v1 Announce Type: cross Abstract: Radiologist eye-tracking data provide a rich record of how experts search, compare, and accumulate evidence during image reading; yet, existing method
arXiv:2605.25156v1 Announce Type: cross Abstract: Prediction models trained under the source distribution do not generalize well to a different target distribution. A valid inference about an unseen d
arXiv:2605.25499v1 Announce Type: new Abstract: Importance weighting (IW) is a golden solver for joint distribution shift, where the joint distributions differ between the training and test data. To s
arXiv:2605.26059v1 Announce Type: cross Abstract: Bayesian inverse design provides a principled framework for inferring aerodynamic geometries from sparse flow observations while quantifying uncertain
arXiv:2605.23945v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a key post-training paradigm for improving model quality. However, the synchronous three-st
arXiv:2605.24301v1 Announce Type: new Abstract: Bidirectional thrust grants quadrotors a second equilibrium condition and increased control authority, expanding the envelope of possible aggressive man
arXiv:2602.02843v3 Announce Type: replace Abstract: When deciding how to act under uncertainty, agents may choose to act to reduce uncertainty or they may act despite that uncertainty. In communicativ
arXiv:2605.25584v1 Announce Type: cross Abstract: Multi-robot task allocation usually assumes some combination of communication, known task models, or a coordinator. We study the opposite extreme, a r