Knowledge catalogue
arXiv:2605.26855v1 Announce Type: new Abstract: Public datasets such as DLC-2021, SynID, and KID34K have significantly contributed to research on presentation attack detection for identity documents,
arXiv:2605.26969v1 Announce Type: cross Abstract: User modeling aims to use language models (LMs) to mimic an individual's behavior from a corpus of past context-action pairs (e.g., conversation turns
arXiv:2512.01572v3 Announce Type: replace-cross Abstract: Extreme sensor sparsity makes full-field reconstruction a fundamentally ill-posed problem in scientific sensing,where the goal is to infer phy
arXiv:2605.26535v1 Announce Type: cross Abstract: Generative models have emerged as a powerful paradigm for solving physics systems and modeling complex spatiotemporal dynamics. However, achieving hig
arXiv:1909.08210v4 Announce Type: replace Abstract: A restricted Boltzmann machine (RBM) is a two-layer neural network with shared weights and has been extensively studied for dimensionality reduction
arXiv:2605.26560v1 Announce Type: cross Abstract: Objective. Outpatient notes carry follow-up instructions pairing actions with future times ('MRI brain in two weeks'). Extracting (action, date) pairs
arXiv:2605.27081v1 Announce Type: cross Abstract: Fine-grained Mixture-of-Experts (MoE) models sparsely activate only a subset of experts per token, reducing activated computation while maintaining hi
arXiv:2605.26327v1 Announce Type: new Abstract: Shampoo-based methods, such as KL-Shampoo and SOAP, have demonstrated strong performance in training neural networks and rely on QR decomposition. Becau
arXiv:2605.26177v1 Announce Type: cross Abstract: Code agents are currently having skillful performance on repository-level software engineering benchmarks, but it remains unclear whether success on e
arXiv:2601.10566v5 Announce Type: replace Abstract: Entity-level unlearning is usually evaluated by what a model says: whether it stops naming the target, refuses a query, or shifts a Truth Ratio dist
arXiv:2605.24634v2 Announce Type: replace Abstract: Composed image retrieval (CIR) searches a corpus with a reference image and a text describing how to modify it. Despite rapid progress from triplet-
arXiv:2605.26661v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection has emerged as a popular technique to enhance the reliability of machine learning models by identifying unexpected
arXiv:2605.26735v1 Announce Type: new Abstract: Recent reasoning Large Language Models produce a chain-of-thought (CoT) predominantly in English, even when prompted in non-English languages. Prior wor
arXiv:2602.04879v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) ser
arXiv:2605.26441v1 Announce Type: cross Abstract: This paper addresses the challenging task of weakly-supervised video temporal grounding. Existing approaches are generally based on the moment proposa
arXiv:2605.26921v1 Announce Type: new Abstract: The study of representations is widespread across fields, including neuroscience, psychology, and artificial intelligence. While representations are oft
arXiv:2604.04940v2 Announce Type: replace Abstract: Designing effective heuristics for NP-hard combinatorial optimization problems remains challenging and often requires substantial domain expertise.
arXiv:2605.26861v1 Announce Type: new Abstract: Image geo-localization aims to determine where a photograph was taken, a task that often requires more than recognizing visible landmarks. Human experts
arXiv:2605.26352v1 Announce Type: new Abstract: Retrieval is increasingly moving from one-shot matching toward interactive reasoning, where language agents iteratively inspect evidence, reformulate qu
arXiv:2605.27314v1 Announce Type: new Abstract: Reactive control is often considered insufficient for multi-objective tasks because conflicting objectives give rise to local minima. We argue this limi
arXiv:2605.27299v1 Announce Type: cross Abstract: Modern intrusion detection systems generate thousands of alerts daily, but alert fatigue severely limits security operations effectiveness due to too
arXiv:2605.26971v1 Announce Type: new Abstract: The proliferation of Reinforcement Learning from Verifiable Rewards (RLVR) datasets has exacerbated provenance collapse due to unclear lineage among exi
arXiv:2605.26862v1 Announce Type: new Abstract: Accurate road segmentation from aerial imagery is fundamental to many geospatial applications. However, existing datasets often suffer from limited scen
arXiv:2605.26452v1 Announce Type: cross Abstract: Safe reinforcement learning (RL) for robotic systems requires policies that improve task performance while satisfying state and input constraints duri
arXiv:2506.03627v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across various tasks by effectively utilizing a prompting strategy. Howe
arXiv:2605.26241v1 Announce Type: new Abstract: Success in generative modeling across language, image, and video demonstrates that large, well-curated datasets are the key driver for building capable
arXiv:2605.26702v1 Announce Type: cross Abstract: Reliable watermarking of panoramic imagery is fundamentally challenged by arbitrary 3D rotations. As panoramas are defined on the sphere, they natural
arXiv:2504.02993v2 Announce Type: replace-cross Abstract: In this paper, we aim to mitigate congestion in traffic management systems by guiding travelers along system-optimal (SO) routes. However, we
arXiv:2605.26632v1 Announce Type: new Abstract: Diffusion Transformers (DiT) achieve strong performance in image generation but incur substantial inference costs. While prior work has reduced this cos
arXiv:2601.22476v2 Announce Type: replace-cross Abstract: Floorplanning determines the coordinate and shape of each module in Integrated Circuits. With the scaling of technology nodes, in floorplannin
arXiv:2605.26640v1 Announce Type: cross Abstract: We study the sample complexity of policy gradient for log-growth control -- the problem of learning, from observed state transitions, a feedback gain
arXiv:2605.27006v1 Announce Type: new Abstract: Sampling from learned high-dimensional distributions is a foundational computational problem. We introduce U-turn chains: Markov chains obtained by iter
arXiv:2509.24935v2 Announce Type: replace-cross Abstract: Scalability has driven recent advances in generative modeling, yet its principles remain underexplored for adversarial learning. We investigat
arXiv:2605.27134v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown rapid progress in mobile GUI navigation. This paper presents a systematic study of data scaling, benchmarking,
arXiv:2602.09038v2 Announce Type: replace-cross Abstract: LLMs have recently shown strong potential in enhancing node-level tasks on text-attributed graphs (TAGs) by providing explanation features. Ho
arXiv:2605.26282v1 Announce Type: new Abstract: Model-based reinforcement learning (RL) can be effectively supported at scale through the use of world models. However, in practice, scaling such approa
arXiv:2605.27009v1 Announce Type: new Abstract: Predicting human olfactory perception from molecular structure has seen remarkable progress, yet these approaches require explicit chemical structure at
arXiv:2605.26538v1 Announce Type: new Abstract: Style transfer with pre-trained diffusion models has advanced rapidly, but a core question remains underexplored: where in the model should style inject
arXiv:2605.26340v1 Announce Type: new Abstract: Autonomous research agents produce competitive solutions and professional-looking manuscripts, yet their outputs contain verifiability failures undetect
arXiv:2605.27032v1 Announce Type: new Abstract: Accurate pancreas segmentation is critical for early cancer diagnosis, where annotation scarcity necessitates Semi-Supervised Learning (SSL). However, d
arXiv:2512.04868v2 Announce Type: replace-cross Abstract: Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual depend
arXiv:2509.26619v3 Announce Type: replace-cross Abstract: Many static benchmarks are beginning to saturate: as models rapidly improve, they achieve near-perfect scores on fixed test sets, leaving litt
arXiv:2605.26548v1 Announce Type: cross Abstract: Large language models (LLMs) now support automated software security tasks, including vulnerability discovery and proof-of-concept (PoC) generation. E
arXiv:2510.19420v2 Announce Type: replace-cross Abstract: Multi-Agent Systems (MAS) have become a prevalent paradigm for Large Language Model (LLM) applications. However, the complex multi-agent desig
arXiv:2605.26788v1 Announce Type: cross Abstract: Large language models (LLMs) achieve impressive performance when a task is fully specified in a single turn, yet the same models lose up to 39% of tha
arXiv:2601.07737v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in mainstream visual understanding tasks, but their ability
arXiv:2506.07813v2 Announce Type: replace-cross Abstract: Arbitrary-scale image super-resolution aims to upsample images to any desired resolution, offering greater flexibility than traditional fixed-
arXiv:2605.27298v1 Announce Type: new Abstract: Charts effectively convey quantitative information, but the underlying data are often locked in image form, hindering reuse and analysis. Manually digit
arXiv:2605.26690v1 Announce Type: cross Abstract: Protein sequence optimization under tight oracle budgets requires methods that explore vast combinatorial spaces while making each evaluation informat
arXiv:2605.26744v1 Announce Type: new Abstract: Human motion generation has made tremendous progress in recent years, with state-of-the-art approaches surpassing ground truth data in leading evaluatio
arXiv:2510.06843v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have exhibited impressive capabilities across diverse application domains. Recent work has explored Multi-LLM Age
arXiv:2605.26132v1 Announce Type: new Abstract: Can post-trained large language models (LLMs) further improve themselves using only unlabeled prompts, without external teachers or feedback from tools?
arXiv:2605.27322v1 Announce Type: new Abstract: We introduce interaction SSD, an extension of Supervised Semantic Differential that models how semantic meaning varies across moderators such as groups,
arXiv:2605.27155v1 Announce Type: cross Abstract: Testing object detectors in safety-critical domains requires semantically meaningful probes beyond pixel-level corruptions. We present SemProbe, a too
arXiv:2605.27080v1 Announce Type: new Abstract: Appearance-based gaze estimation always suffers from poor generalization due to limited annotated samples and insufficient dataset diversity. Leading ap
arXiv:2605.26324v1 Announce Type: cross Abstract: Learned physics simulators are often evaluated by one-step or short-horizon prediction error, but these metrics can miss failures in temporal composit
arXiv:2605.26239v1 Announce Type: new Abstract: In this work, we study Cooperative Spatial Intelligence, the ability of decentralized embodied agents to coordinate effectively under dynamic environmen
arXiv:2605.26571v1 Announce Type: new Abstract: Federated learning enables collaborative model training without sharing raw data, but its performance can degrade substantially under heterogeneous clie
arXiv:2605.27294v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems can respond incorrectly even when the correct passage was retrieved. The model must still read the retrieve
arXiv:2605.26186v1 Announce Type: cross Abstract: Functionality-correct repository setup aims to configure execution environments (e.g., dependencies, build scripts) to successfully execute a reposito