Knowledge catalogue
arXiv:2605.20642v1 Announce Type: new Abstract: When annotators disagree, that disagreement can reflect epistemic uncertainty rather than simple label noise. We study hard-label delivery as an alterna
arXiv:2605.20545v1 Announce Type: cross Abstract: Transfer learning is an essential technique for many machine learning/AI models of complex structures such as large language models and generative AI.
arXiv:2605.20713v1 Announce Type: new Abstract: Multimodal IE in social media is difficult because a post may attach multiple images that are weakly related, redundant, or even misleading with respect
arXiv:2605.20395v1 Announce Type: new Abstract: A fundamental challenge in multi-robot motion planning is achieving sufficient coordination to avoid inter-robot conflicts without incurring the large c
arXiv:2605.20681v1 Announce Type: cross Abstract: Distributed principal component analysis (PCA) produces node-level estimates of both a mean vector and a principal subspace. Robustly aggregating thes
arXiv:2605.20396v1 Announce Type: new Abstract: Identifying latent variables and the causal structure involving them is essential across various scientific fields. While many existing works fall under
arXiv:2605.20712v1 Announce Type: new Abstract: Automatic speech recognition replaces typing only when correction costs less than manual entry, a threshold determined by error types, not counts: fixin
arXiv:2605.20308v1 Announce Type: new Abstract: Gradient-based attacks are important methods for evaluating model robustness. However, since the proposal of APGD, it has been difficult for such method
arXiv:2601.00418v2 Announce Type: replace-cross Abstract: We propose the Consensus-Based Privacy-Preserving Data Distribution (CPPDD) framework, a lightweight and post-setup autonomous protocol for se
arXiv:2605.20645v1 Announce Type: new Abstract: Foggy conditions are commonly encountered in real-world applications; however, existing action recognition approaches typically assume favorable weather
arXiv:2502.03752v5 Announce Type: replace Abstract: Meta-reinforcement learning (Meta-RL) facilitates rapid adaptation to unseen tasks but faces challenges in long-horizon environments. Skill-based ap
arXiv:2601.18577v2 Announce Type: replace Abstract: Modern video generators still struggle with complex physical dynamics, often falling short of physical realism. Existing approaches address this usi
arXiv:2605.20602v1 Announce Type: new Abstract: Successive self-training on a language model's own outputs is widely characterized as a process of flattening: diversity drops, distributions narrow, an
arXiv:2605.21190v1 Announce Type: new Abstract: Despite the generative capabilities of diffusion and flow models, real-image editing remains constrained by a persistent trade-off between semantic edit
arXiv:2605.21341v1 Announce Type: cross Abstract: Functional bilevel methods estimate a lower-level function and plug it into a hypergradient, but this plug-in gradient can retain first-order bias whe
arXiv:2509.13648v3 Announce Type: replace Abstract: Generative recommendation plays a crucial role in personalized systems, predicting users' future interactions from their historical behavior sequenc
arXiv:2605.20510v1 Announce Type: new Abstract: Urban heat exposure is becoming an increasingly critical challenge due to the intensifying urban heat island effect. Fine-grained shade patterns, especi
arXiv:2605.20763v1 Announce Type: new Abstract: Rapid progress in aerodynamic shape optimization (ASO) has outpaced currently-available standardized evaluation frameworks. Fair comparison requires a u
arXiv:2602.06358v2 Announce Type: replace Abstract: We propose SHINE (Scalable Hyper In-context NEtwork), a scalable hypernetwork that can map diverse meaningful contexts into high-quality LoRA adapte
arXiv:2605.20191v1 Announce Type: new Abstract: Modern Large Language Models (LLMs) have recently attracted much attention for their ability to simulate human behavior and generate text that reflects
arXiv:2504.19584v3 Announce Type: replace Abstract: Reconstructing dynamic radiance fields from video clips is challenging, especially when entertainment videos like TV shows are given. Many challenge
arXiv:2605.20998v1 Announce Type: new Abstract: Aspect-Term Sentiment Analysis (ATSA) in multi-aspect sentences faces a fundamental tradeoff between efficiency and expressiveness. Existing models eith
arXiv:2605.20733v1 Announce Type: new Abstract: Converting hand-drawn sketches into structured 3D geometries remains challenging due to the difficulty of representing non-Euclidean surfaces and mainta
arXiv:2605.20450v1 Announce Type: new Abstract: Differentially private stochastic gradient descent (DP-SGD) enables private deep learning through per-example clipping and calibrated Gaussian noise, bu
arXiv:2605.20272v1 Announce Type: new Abstract: While humans readily generalize abstract concepts to more complex or larger tasks, building Reinforcement Learning (RL) systems with this ability remain
arXiv:2605.21135v1 Announce Type: new Abstract: As MT quality increases, interest in enhanced post-editing features such as QE-derived error highlights is growing, yet evidence for their usefulness re
arXiv:2605.21147v1 Announce Type: cross Abstract: As the number of model parameters increases, parameter-efficient fine-tuning (PEFT) has become the go-to choice for tailoring pre-trained large langua
arXiv:2605.20850v1 Announce Type: new Abstract: Objective: Stage-wise workflows that separate model scaling and inverse kinematics can induce morphology-posture compensation, resulting in anatomically
arXiv:2605.20189v1 Announce Type: cross Abstract: Despite the remarkable success of large language models (LLMs), they still face bottlenecks while deploying in dynamic, real-world settings with prima
arXiv:2605.05863v2 Announce Type: replace Abstract: Incorporating prior data into online reinforcement learning accelerates training but typically forces a difficult trade-off between high computation
arXiv:2605.20433v1 Announce Type: new Abstract: Contact-rich manipulation tasks such as tight-clearance insertion, connector mating, polishing, and surface-conforming wiping remain difficult for data-
arXiv:2603.02845v3 Announce Type: replace Abstract: Efficient communication is critical for decentralized Multi-Robot Path Planning (MRPP), yet existing learned communication methods treat all neighbo
arXiv:2605.20808v1 Announce Type: new Abstract: Modern ultra-high-resolution image synthesis relies heavily on the robust generative capacity of large-scale pre-trained Latent Diffusion Models (LDMs).
arXiv:2605.21384v1 Announce Type: cross Abstract: As long-horizon coding agents produce more code than any developer can review, oversight collapses onto a single surface: the automated test suite. Re
arXiv:2605.20552v1 Announce Type: cross Abstract: Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs
arXiv:2605.20408v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) effectively aligns Large Language Models (LLMs) with aggregate human preferences but often fails to ad
arXiv:2605.20296v1 Announce Type: new Abstract: Fine-tuning a language model for a target task routinely degrades capabilities the training data never explicitly threatened. We study this phenomenon,
arXiv:2605.21075v1 Announce Type: new Abstract: Earth observation (EO) foundation models (FMs) are increasingly trained on multisensor data, spanning multispectral imagery (MSI), synthetic aperture ra
arXiv:2501.15151v5 Announce Type: replace Abstract: Spiking Neural Networks (SNNs) are the third generation of neural networks. They have gained widespread attention in object detection due to their l
arXiv:2605.20760v1 Announce Type: new Abstract: Automated segmentation of the vertebral column in Computed Tomography (CT) scans is a prerequisite for pathological assessment and surgical planning. Ho
arXiv:2605.21244v1 Announce Type: new Abstract: Super-Resolution (SR) has advanced rapidly in recent years, with diffusion-based models achieving unprecedented fidelity at the cost of introducing new
arXiv:2605.20478v1 Announce Type: new Abstract: LLM-curated tables can appear source-grounded while containing unsupported rows: the curator may recall entries from parametric memory and retroactively
arXiv:2605.20738v1 Announce Type: new Abstract: Remote sensing imagery typically arrives in the form of continuous data streams. Traditional detectors often forget previously learned categories when l
arXiv:2512.23943v2 Announce Type: replace-cross Abstract: U.S. discrimination law can impose liability on firms that fail to adopt a less discriminatory alternative (LDA): a decision policy that achie
arXiv:2605.20929v1 Announce Type: new Abstract: We propose STEAM (Spatial, Temporal, and Emergent congestion Awareness for MAPF), a training-free test-time enhancement framework for learning-based dec
arXiv:2605.20390v1 Announce Type: new Abstract: Model scaling has demonstrated remarkable success through large-scale training on diverse datasets. It remains an open question whether the same paradig
arXiv:2605.21324v1 Announce Type: cross Abstract: What can representational similarity matrices (RSMs) tell us about a neural code? As the popularity of these summary statistics grows, so too does the
arXiv:2605.21261v1 Announce Type: new Abstract: Training-free zero-shot composed image retrieval models are recently gaining increasing research interest due to their generalizability and flexibility
arXiv:2508.12247v2 Announce Type: replace Abstract: Recently, spatio-temporal time-series prediction has developed rapidly, yet existing deep learning methods struggle with learning complex long-term
arXiv:2605.20924v1 Announce Type: new Abstract: Designing effective task-level prompts is crucial for improving the performance of Large Language Models (LLMs). While prior work on instruction inducti
arXiv:2605.21472v1 Announce Type: new Abstract: View-conditioned 3D generators such as SAM 3D, TRELLIS and Hunyuan3D produce high-quality object reconstructions from a single view, but real-world visu
arXiv:2605.21466v1 Announce Type: new Abstract: Although existing video editing methods are generally feasible, they often require many costly iterations and still struggle to deliver high-quality yet
arXiv:2506.21039v3 Announce Type: replace Abstract: Long-horizon goal-conditioned tasks pose fundamental challenges for reinforcement learning (RL), particularly when goals are distant and rewards are
arXiv:2605.20917v1 Announce Type: new Abstract: Subterranean (SubT) environments have been a frontier for autonomous robotics, driven by the push for automation of mining operations and the interest i
arXiv:2605.20373v1 Announce Type: cross Abstract: Building humanoid robots capable of generalizable whole-body loco-manipulation in the real world remains a fundamental challenge. Existing methods eit
arXiv:2605.20413v1 Announce Type: new Abstract: High-dimensional biological data often exhibit a severe mismatch between feature dimensionality and sample size, making reliable classification difficul
arXiv:2605.20619v1 Announce Type: new Abstract: Scalarization is widely used in multi-objective optimization owing to its simplicity and scalability. In many applications, the goal is to generate solu
arXiv:2605.21132v1 Announce Type: new Abstract: Understanding surgical workflow in real time is fundamental for intelligent surgical embodiment, where AI systems continuously perceive and respond as s
arXiv:2603.26603v2 Announce Type: replace-cross Abstract: The migration of Large Language Models (LLMs) from cloud clusters to edge devices promises enhanced privacy and offline accessibility, but thi
arXiv:2605.20919v1 Announce Type: new Abstract: Sutra is a typed, purely functional programming language whose compiled forward pass is a PyTorch neural network. The compiler beta-reduces the whole pr