Knowledge catalogue
arXiv:2609.13667v1 Announce Type: new Abstract: Geospatial agents are increasingly expected to support recurring and evolving analytical tasks rather than execute isolated workflows. In such settings,
arXiv:2609.13204v1 Announce Type: cross Abstract: Neural ordinary differential equations use numerical solvers inside the training loop. The solver determines the forward trajectory and also affects t
arXiv:2609.14066v1 Announce Type: cross Abstract: Although existing multi-agent Retrieval-Augmented Generation (RAG) systems have demonstrated promise on complex multimodal reasoning tasks, they remai
arXiv:2609.13836v1 Announce Type: new Abstract: The Minimum Dominating Set (MDS) problem is a classic NP-hard combinatorial optimization problem with critical applications in social network analysis,
arXiv:2609.15169v1 Announce Type: new Abstract: Driving vision-language-action (VLA) models increasingly reason before acting, but their intermediate reasoning is often weakly grounded in physical sce
arXiv:2609.13308v1 Announce Type: new Abstract: A companion evaluation found that naming the target part in a manipulation prompt increased action accuracy by 0.32-0.63 across eight vision-language mo
arXiv:2609.15483v1 Announce Type: new Abstract: Unsupervised multivariate time series anomaly detection methods typically identify anomalies through forecasting, reconstruction, or representation disc
arXiv:2609.13947v1 Announce Type: cross Abstract: In-sensor computing reduces the cost of transmitting high-resolution image data by performing early-stage processing near the sensor. However, the log
arXiv:2609.13296v1 Announce Type: new Abstract: Test-time reinforcement learning can adapt vision-language models (VLMs) to unlabeled target data, but its effectiveness is fundamentally limited by the
arXiv:2609.15195v1 Announce Type: new Abstract: Embodied navigation requires agents to interpret visual observations, accumulate spatial knowledge, and execute actions to follow instructions or locate
arXiv:2609.14968v1 Announce Type: new Abstract: Online scheduling of dependency-aware tasks in heterogeneous cloud clusters is a fundamental yet challenging problem due to the complex interplay betwee
arXiv:2603.23803v2 Announce Type: replace-cross Abstract: In this paper, we present DROP, high-Density Relocation-free sequential OPerations in automated valet parking. DROP addresses the challenges i
arXiv:2609.14959v1 Announce Type: new Abstract: We study decentralized learning of Nash equilibria (NE) in infinite-horizon discounted Markov games under bandit feedback, focusing on Markov alpha-pote
arXiv:2603.11321v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards improves reasoning in large language models, yet on-policy learning often suffers from cold-sta
arXiv:2609.15035v1 Announce Type: new Abstract: Short-term photovoltaic power forecasting requires models to represent regular solar cycles and weather-driven fluctuations whose importance changes wit
arXiv:2609.14903v1 Announce Type: new Abstract: Autonomous surface vehicles operating in inland waterways require a persistent representation of both surrounding structures and the water surface. LiDA
arXiv:2609.15938v1 Announce Type: new Abstract: Scientific agents contribute to hypothesis discovery by synthesizing evidence, assessing proposals, and developing new explanations. Recent systems comb
arXiv:2601.19847v3 Announce Type: replace Abstract: Despite the strong reasoning capabilities of recent large language models (LLMs), achieving reliable performance on challenging tasks often requires
arXiv:2609.15284v1 Announce Type: new Abstract: Missing values are ubiquitous in heterogeneous data mining, where numerical, categorical, and binary variables often coexist. Many imputation methods, e
arXiv:2609.15833v1 Announce Type: new Abstract: Cultural heritage documentation increasingly relies on image-based 3D surface reconstruction, with photogrammetry software making such workflows accessi
arXiv:2609.13936v1 Announce Type: new Abstract: Datasets that ship automatically generated feature annotations invite a question rarely asked of them: would a human agree with those labels? This repor
arXiv:2603.29396v2 Announce Type: replace Abstract: Standard evaluations of Large language models (LLMs) focus on task performance, offering limited insight into whether correct behavior reflects appr
arXiv:2609.14246v1 Announce Type: cross Abstract: In wireless federated learning (FL), data heterogeneity and multiple local updates induce client drift, degrading model convergence. It is further aff
arXiv:2505.08123v2 Announce Type: replace-cross Abstract: Multi-material decomposition (MMD) enables quantitative reconstruction of tissue compositions in the human body, supporting a wide range of cl
arXiv:2609.13437v1 Announce Type: new Abstract: Scientific research is a continuous process that emphasizes inheritance. Methods developed by predecessors are often expanded upon by new researchers to
arXiv:2609.14783v1 Announce Type: cross Abstract: Robots need touch to manipulate objects safely and reliably, as many properties, such as softness, texture, and contact stability, are hard to infer f
arXiv:2209.11097v2 Announce Type: replace Abstract: Agile flights of autonomous quadrotors in cluttered environments require constrained motion planning and control subject to translational and rotati
arXiv:2609.14426v1 Announce Type: new Abstract: We present a learning-based approach to kinodynamic online motion planning for an Unmanned Aerial Vehicle (UAV) operating at a fixed altitude in unknown
arXiv:2609.14316v1 Announce Type: new Abstract: Advances in image generation have made synthetic images increasingly difficult to distinguish from real photographs, raising concerns about the trustwor
arXiv:2609.13761v1 Announce Type: new Abstract: In-hand manipulation allows multi-fingered dexterous hands to reconfigure grasped objects without releasing and regrasping them. This improves manipulat
arXiv:2609.14712v1 Announce Type: cross Abstract: Metastability---a phenomenon where systems remain trapped in quasi-stable states before abruptly transitioning under rare perturbations---is ubiquitou
arXiv:2609.15883v1 Announce Type: cross Abstract: Offline reinforcement learning aims to learn a policy solely from fixed datasets, which often contain multimodal action distributions. Flow policies c
arXiv:2609.15851v1 Announce Type: new Abstract: Language models can learn from experience, but raw solution trajectories are often too long and noisy to provide effective guidance. In this work, we pr
arXiv:2609.14972v1 Announce Type: new Abstract: We study continuous-time and possibly high-dimensional stochastic control problems where drift coefficients and running reward functions are unknown. Du
arXiv:2609.13893v1 Announce Type: new Abstract: Earnings conference calls are a primary channel through which managers disclose information under analyst scrutiny. Prior work has linked vocal and lexi
arXiv:2609.14708v1 Announce Type: new Abstract: A core goal of efficient reasoning is to improve the accuracy-efficiency frontier. However, jointly improving reasoning accuracy and inference efficienc
arXiv:2609.15745v1 Announce Type: cross Abstract: Hallucination remains a critical challenge in large language models (LLMs), where early factual errors compound through autoregressive generation in a
arXiv:2602.04192v3 Announce Type: replace Abstract: Learning the intrinsic dimensionality of subjective perceptual spaces such as taste, smell, or aesthetics from ordinal data is a challenging problem
arXiv:2609.15863v1 Announce Type: new Abstract: Video diffusion models are stochastic and hard to control: precise content often requires repeated sampling without guaranteed success, and long-horizon
arXiv:2609.14660v1 Announce Type: cross Abstract: Split computing constitutes a widely used distributed inference approach, where a lightweight head model is onloaded onto the device and a heavier tai
arXiv:2609.13665v1 Announce Type: new Abstract: Accurate cell segmentation remains a major bottleneck in subcellular spatial transcriptomics (SST), in which morphological images and spatially resolved
arXiv:2603.22179v2 Announce Type: replace Abstract: Cardiovascular disease remains the leading cause of global mortality, with progress hindered by human interpretation of complex cardiac tests. Curre
arXiv:2609.15408v1 Announce Type: cross Abstract: Long-video understanding remains challenging for multimodal large language models (MLLMs) because densely encoding long frame sequences is computation
arXiv:2606.13989v2 Announce Type: replace-cross Abstract: Recent alignment-free non-autoregressive (NAR) text-to-speech (TTS) models formulate synthesis as a conditional infilling task, bypassing expl
arXiv:2609.15221v1 Announce Type: cross Abstract: Passive acoustic monitoring can measure biodiversity at larger scales, but time--frequency annotation of animal vocalizations is expensive, site-speci
arXiv:2609.15647v1 Announce Type: new Abstract: Medical counterfactual generation modifies images to change model predictions for interpretability. However, existing diffusion-based approaches are oft
arXiv:2511.19046v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consumin
arXiv:2511.12041v4 Announce Type: replace-cross Abstract: Super-resolution flow reconstruction using state-of-the-art data-driven techniques is valuable for a variety of applications, such as subgrid/
arXiv:2609.14795v1 Announce Type: cross Abstract: There is a tradeoff in machine translation meta-evaluation between prioritizing alignment with adequacy versus fluency. The balance depends on the com
arXiv:2609.13991v1 Announce Type: new Abstract: Reconstructing 3D objects from brain signals offers a promising avenue for understanding human visual cognition. While prior work has shown initial succ
arXiv:2609.15972v1 Announce Type: new Abstract: As language models become more capable, long-term collaboration in learning, reasoning, and decision-making calls for a deeper understanding of the peop
arXiv:2609.15045v1 Announce Type: cross Abstract: Prompt echoing is a recognized failure mode of instruct language models, in which a model instead of generating a response, mirrors the provided promp
arXiv:2608.00929v2 Announce Type: replace Abstract: Social dynamics encode the process in which individual network and discourse interactions aggregate into collective influence, narrative dominance a
arXiv:2609.14857v1 Announce Type: new Abstract: Recent work extends recursive self-improvement (RSI) to agent harnesses for long-horizon coding and terminal tasks, enabling agents to improve execution
arXiv:2609.14716v1 Announce Type: new Abstract: Autonomous agents are typically obliged to follow user-assigned tasks. However, strict obedience may conflict with moral obligations that arise during e
arXiv:2609.14189v1 Announce Type: new Abstract: Human motion may be viewed as a combination of action content, style, and body morphology. Existing motion style transfer methods transfer a reference s
arXiv:2609.14965v1 Announce Type: new Abstract: Text-driven 3D human motion generation models face significant challenges in responding to diverse and unconstrained textual prompts, primarily due to t
arXiv:2506.04166v3 Announce Type: replace Abstract: Nearest neighbor (NN) methods have re-emerged as competitive tools for matrix completion, offering strong empirical performance and recent theoretic
arXiv:2609.14652v1 Announce Type: cross Abstract: Large Language Model (LLM) applications often transfer domain concepts into the model's context informally, through prompt prose, schema dumps, and ex
arXiv:2603.02504v3 Announce Type: replace Abstract: Large Language Models (LLMs) achieve strong performance on natural language tasks but remain unreliable in mathematical reasoning, frequently genera