Knowledge catalogue
arXiv:2608.13283v1 Announce Type: new Abstract: Understanding motion in daily living requires context beyond kinematics, because similar inertial patterns during activities of daily living (ADLs) can
arXiv:2608.13112v1 Announce Type: new Abstract: Text-to-image generation has reached photorealistic quality, yet state-of-the-art systems remain unreliable at producing scientific diagrams, whose valu
arXiv:2608.12917v1 Announce Type: new Abstract: Developing effective robot navigation methods in crowded environments is essential for real-world applications. Although recent deep reinforcement learn
arXiv:2608.12714v1 Announce Type: new Abstract: Real-world object detection operates under ambiguous supervision, where unlabeled regions may correspond to missing annotations of known objects or genu
arXiv:2608.12713v1 Announce Type: cross Abstract: Watermarking LLM-generated text is an important task for tracing its provenance. Existing LLM watermarks preserve provenance under editing, but this s
arXiv:2608.13331v1 Announce Type: cross Abstract: The replicability of papers is a cornerstone of scientific knowledge, ensuring the reliability of existing results and providing a base for further ex
arXiv:2602.10840v2 Announce Type: replace Abstract: Large language models (LLMs) have been widely studied in areas such as mathematical reasoning, complex coding, and scientific problem solving. Howev
arXiv:2608.12655v1 Announce Type: new Abstract: A flat training curve does not reveal whether a neural network has reached a global optimum, is locally trapped, is representation-limited, or is mismat
arXiv:2509.24928v2 Announce Type: replace Abstract: This work introduces an adaptive Bayesian algorithm for real-time trajectory prediction via intention inference, where a target's intentions and mot
arXiv:2608.13167v1 Announce Type: cross Abstract: When visual evidence is occluded or chaotic, models should abstain. In this paper, we show that Vision-Language Models (VLMs) can internally distingui
arXiv:2608.13495v1 Announce Type: new Abstract: Efficiently retrieving relevant clips from large-scale driving logs is essential for data curation, model development, and safety analysis. Structured a
arXiv:2608.12574v1 Announce Type: new Abstract: Large language models increasingly need to generate structured outputs that conform to predefined schemas, with one common constraint being selection fr
arXiv:2608.13221v1 Announce Type: new Abstract: The evaluation of LLM reasoning is moving from final-answer accuracy to process-level assessment, yet existing methods still fail to capture how models
arXiv:2512.02965v2 Announce Type: replace Abstract: Addressing the urgent need for high-performance real-time low-light image enhancement on resource-constrained edge devices in low-illumination scena
arXiv:2608.12962v1 Announce Type: new Abstract: Vertical Federated Learning (VFL) enables organizations holding complementary features of shared entities to collaborate and train models. In this setti
arXiv:2608.13217v1 Announce Type: new Abstract: Convolutional Neural Networks (CNNs) capture local features efficiently but struggle with global context due to their limited receptive field. On the ot
arXiv:2608.12391v1 Announce Type: cross Abstract: Graph reasoning provides a promising testbed for evaluating the reasoning ability of large language models (LLMs), as graph instances can be programma
arXiv:2608.13061v1 Announce Type: new Abstract: As the feature representation changes, replay must preserve the earlier classes. However, only a bounded active exemplar set can be replayed. We propose
arXiv:2608.12953v1 Announce Type: new Abstract: Structured pruning is a promising approach for compressing large language models (LLMs), yet existing methods rely heavily on greedy heuristics that pro
arXiv:2608.12438v1 Announce Type: new Abstract: We formulate generative modeling as a path integral in which flow-based, diffusion-based, variational, and adversarial models arise as different evaluat
arXiv:2608.13453v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as generalist robotic policies capable of following diverse language instructions and performing a wi
arXiv:2608.13031v1 Announce Type: cross Abstract: Traffic video understanding has become an important problem in intelligent transportation, as road videos provide direct evidence for accidents, viola
arXiv:2508.12220v2 Announce Type: replace-cross Abstract: Can a prospectively instrumented training continuation reproduce a deletion counterfactual exactly after selected examples leave its replay da
arXiv:2501.14844v3 Announce Type: replace-cross Abstract: Detecting biases in the outputs produced by generative models is essential to reduce the potential risks associated with their application in
arXiv:2505.02779v2 Announce Type: replace Abstract: Retinal image registration, particularly for color fundus images, is a challenging yet essential task with diverse clinical applications. Existing r
arXiv:2505.02787v3 Announce Type: replace Abstract: Current color fundus image registration approaches are limited, among other things, by the lack of labeled data, which is even more significant in t
arXiv:2608.13556v1 Announce Type: new Abstract: Latent video generation relies on autoencoders to define a compact space in which generative models operate. Although video autoencoder architectures ha
arXiv:2608.13060v1 Announce Type: new Abstract: Machine learning theory studies learning procedures through mathematical setups in which the data model, training protocol, oracle access, loss, metric,
arXiv:2608.12827v1 Announce Type: new Abstract: 3D body scanning has become an important tool in healthcare applications because of its rapid and non-invasive nature. While smartphone-based photogramm
arXiv:2602.05379v2 Announce Type: replace-cross Abstract: Effective reinforcement learning (RL) for complex stochastic systems requires leveraging historical data to improve sample efficiency and acce
arXiv:2608.13522v1 Announce Type: cross Abstract: AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code. Verified code generation, in w
arXiv:2608.13260v1 Announce Type: new Abstract: Accurate modeling and forecasting of power transformer thermal behavior are critical for reliability, asset lifetime, and optimized power system operati
arXiv:2608.12333v1 Announce Type: cross Abstract: Vision-language models must associate visual entities with textual attributes. Whether these associations or concept bindings remain stable when the l
arXiv:2608.12776v1 Announce Type: new Abstract: This paper introduces ViTOED, a novel dataset for target-oriented emotion detection in Vietnamese social media texts. The ViTOED comprises 10,985 user c
arXiv:2608.12721v1 Announce Type: new Abstract: Complex video object segmentation requires robust target propagation under severe occlusion, disappearance and reappearance. Although SAM3 provides stro
arXiv:2608.13263v1 Announce Type: new Abstract: Large language model serving faces a critical memory bottleneck: the KV cache grows with sequence length and batch size. PagedAttention uses fixed-size
arXiv:2608.13418v1 Announce Type: cross Abstract: Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution. To this end, we
arXiv:2608.12322v1 Announce Type: cross Abstract: Self-reflection is widely assumed to improve LLM reasoning, yet which component drives the gain remains poorly understood. We present a controlled six
arXiv:2608.12594v1 Announce Type: cross Abstract: As more investors contemplate private markets and contend with limited transparency, sparse disclosures, and infrequent transactions, identifying econ
arXiv:2608.12324v1 Announce Type: cross Abstract: People increasingly ask large language models (LLMs) for counsel on questions of faith, doctrine, and pastoral care. These questions are not ordinary
arXiv:2608.12489v1 Announce Type: new Abstract: Organizations decide whom to treat under a budget and want to know what a targeting rule would have earned before deploying it. Off-policy evaluation pr
arXiv:2608.12623v1 Announce Type: new Abstract: Language model classifiers with explanations are used for moderation, routing, topic triage, and low-resource annotation. We study black-box auditing wh
arXiv:2608.13385v1 Announce Type: new Abstract: Implicit multimodal in-context learning compresses demonstrations into internal interventions, ranging from static task vectors to query-conditioned tra
arXiv:2505.09662v4 Announce Type: replace Abstract: Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare
arXiv:2608.13365v1 Announce Type: new Abstract: Rotation-based post-training quantisation commonly applies an orthogonal transform across an entire attention head to reduce outlier-induced error. RoPE
arXiv:2608.13237v1 Announce Type: cross Abstract: Multi-round retrieval-augmented generation (RAG) must decide when to stop searching as evidence accumulates. Because the deployed policy is determined
arXiv:2608.12888v1 Announce Type: new Abstract: Agent-memory systems increasingly buy retrieval quality with structure, transforming raw conversation histories into summaries, embeddings, trees, or kn
arXiv:2607.19058v2 Announce Type: replace-cross Abstract: Optimizer state is the largest single line item in the memory budget of mixture-of-experts (MoE) training. On a 6.78B-parameter MoE language m
arXiv:2608.13337v1 Announce Type: new Abstract: Sparse autoencoders are meant to name the things a language model computes, and the usual way to check that a latent matters is to switch it off and see
arXiv:2608.13168v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly applied for emotional companionship, evaluating their behavior and capabilities in intimate relationshi
arXiv:2608.12422v1 Announce Type: new Abstract: Two free satellite signals carry real information about glacial-lake outburst risk in the Nepal Himalaya: radar interferometry sees a moraine dam slowly
arXiv:2608.13410v1 Announce Type: new Abstract: Parliamentary proceedings are a primary record of democratic deliberation, yet their volume and fragmentation make multi-perspective access difficult fo
arXiv:2608.12352v1 Announce Type: cross Abstract: AI governance frameworks can be known, used, and implemented in form without becoming governance in practice. This paper examines that problem through
arXiv:2608.12323v1 Announce Type: cross Abstract: Specifying a penalty can paradoxically convert a legal obligation into a cost-benefit calculation that favors violation. We demonstrate that this enfo
arXiv:2505.05474v2 Announce Type: replace Abstract: 3D scene generation seeks to synthesize spatially structured, semantically meaningful, and photorealistic environments for applications such as imme
arXiv:2608.11733v1 Announce Type: cross Abstract: Effective implementation of high-level quantum gates is essential for practical quantum computing. To the best of our knowledge, we present the first
arXiv:2512.06547v4 Announce Type: replace-cross Abstract: Decoupled PPO has been a successful reinforcement learning (RL) algorithm to deal with the high data staleness under the asynchronous RL setti
arXiv:2608.12269v1 Announce Type: new Abstract: Public procurement involves the allocation of substantial financial resources; therefore, continuous oversight through audits, controls, and monitoring
arXiv:2608.11762v1 Announce Type: cross Abstract: Alzheimer's disease is a leading cause of death with no cure. Therefore, early detection is critical to slow progression and preserve quality of life.
arXiv:2608.11540v1 Announce Type: cross Abstract: The convergence of artificial intelligence (AI), Industrial Internet of Things, cyber-physical systems, and advanced robotics is reshaping manufacturi