Knowledge catalogue
arXiv:2605.30031v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) expand jailbreak risks from token-level prompting to the full speech perception-to-reasoning pipeline, where unsaf
arXiv:2605.29202v1 Announce Type: new Abstract: Recent advances in text-to-music generation enable high-fidelity synthesis of structured musical audio, raising growing concerns about data provenance,
arXiv:2601.12699v2 Announce Type: replace Abstract: Deep Brain Stimulation (DBS) is an effective treatment for Parkinson's disease, but conventional fixed-parameter stimulation can reduce battery life
arXiv:2510.27663v3 Announce Type: replace-cross Abstract: Modern imaging techniques heavily rely on Bayesian statistical models to address difficult image reconstruction and restoration tasks. This pa
arXiv:2605.28855v1 Announce Type: new Abstract: Temporal-difference learning with function approximation can be unstable under off-policy sampling. TDC stabilizes off-policy TD through an auxiliary co
arXiv:2605.28849v1 Announce Type: new Abstract: Gradient temporal-difference methods provide stable off-policy prediction with linear function approximation, but their practical performance is strongl
arXiv:2509.23571v3 Announce Type: replace-cross Abstract: As cyber threats continue to grow in scale and sophistication, blue team defenders increasingly require advanced tools to proactively detect a
arXiv:2605.30339v1 Announce Type: new Abstract: Generative video-to-audio (V2A) models produce highly plausible soundtracks, but it remains unclear whether they capture the underlying physical process
arXiv:2605.30231v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) often struggle with robust 3D spatial reasoning. Prevailing methods that rely on fine-tuning with 3D visual question-ans
arXiv:2510.16060v2 Announce Type: replace-cross Abstract: The recent development of foundation models for time series data has generated considerable interest in using such models across a variety of
arXiv:2603.26668v2 Announce Type: replace-cross Abstract: As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces the
arXiv:2601.01162v3 Announce Type: replace-cross Abstract: Qualitative data are widespread in domains such as healthcare, marketing, and bioinformatics, where clustering offers a fundamental tool for p
arXiv:2605.29849v1 Announce Type: cross Abstract: Machine learning (ML) is increasingly used for data-driven modeling of buildings to enable downstream tasks such as fault detection and diagnosis, and
arXiv:2605.29155v1 Announce Type: cross Abstract: In the literature, actor-critic model predictive control (AC-MPC) integrates MPC with reinforcement learning to enable high-performance control of com
arXiv:2605.29868v1 Announce Type: cross Abstract: Centralised biometric identity systems expose users to single points of failure, opaque verification processes, and irreversible biometric compromise.
arXiv:2605.29935v1 Announce Type: cross Abstract: Autonomous driving systems are commonly trained and evaluated within limited geographic regions, which hinders their scalability when deployed in new
arXiv:2605.28861v1 Announce Type: cross Abstract: A recent article [Phys. Rev. X 15, 011047 (2025)] utilizes group-equivariant convolutional neural networks to study the ground state of the kagome Hei
arXiv:2605.29612v1 Announce Type: cross Abstract: Although large language model (LLM) based multi-agent systems (MAS) show their capability to solve complex tasks and achieve higher performance over s
arXiv:2605.28866v1 Announce Type: cross Abstract: Token-based time series large language models (TS-LLMs) have emerged as a promising direction for time series analysis and reasoning. However, prior s
arXiv:2605.29939v1 Announce Type: cross Abstract: Integrated sensing, communication, and computation (ISCC) provides a promising framework for indoor human-centric applications. In these applications,
arXiv:2502.03805v2 Announce Type: replace Abstract: Large language models have revolutionized natural language processing but face significant challenges of high storage and runtime costs, due to the
arXiv:2605.29378v1 Announce Type: new Abstract: Natural language interfaces can simplify interaction with multi-robot systems, especially when non-expert users need to issue high-level commands. Acous
arXiv:2605.29409v1 Announce Type: cross Abstract: Chandrayaan-3 mission achieved a historic milestone with its successful soft landing near the lunar south pole, highlighting the critical role of the
arXiv:2605.29464v1 Announce Type: cross Abstract: In randomized trials involving multiple treatments, bivariate survival outcomes present significant analytical challenges for making decisions. This p
arXiv:2605.29260v1 Announce Type: new Abstract: Psychovisual models suggest human vision decouples low-level feature extraction from higher cognition by first forming intermediate abstractions. In con
arXiv:2605.30334v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized various fields, yet their training efficiency is heavily reliant on effective data curation. While data
arXiv:2605.29021v1 Announce Type: new Abstract: Active tether-net systems are a promising solution for capturing large non-cooperative targets, such as space debris, by deploying a flexible net manipu
arXiv:2602.06036v2 Announce Type: replace Abstract: Autoregressive large language models (LLMs) deliver strong performance but require inherently sequential decoding, leading to high inference latency
arXiv:2605.29191v1 Announce Type: cross Abstract: Non-uniform scaling control of formation enables multi-agent systems to adjust their shape by scaling with different ratios along different coordinate
arXiv:2605.29359v1 Announce Type: cross Abstract: Compute governance proposals often rely on the assumption that frontier AI training requires large, detectable computing clusters. However, recent adv
arXiv:2605.29707v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by drafting multiple tokens and verifying them in parallel with the target model. However, its practical
arXiv:2605.30200v1 Announce Type: new Abstract: The double-edged sword of integrating Large Language Models (LLMs) requires an effective triadic collaboration mechanism among LLMs, teachers and studen
arXiv:2602.00324v2 Announce Type: replace-cross Abstract: Synchronization over the special Euclidean group SE(3) aims to recover absolute poses from noisy pairwise relative transformations and is a co
arXiv:2602.08783v3 Announce Type: replace Abstract: Latent or continuous chain-of-thought methods replace explicit textual rationales with a number of internal latent steps, but these intermediate com
arXiv:2605.30010v1 Announce Type: new Abstract: Video large language models (Video-LLMs) have demonstrated strong capabilities in video understanding tasks. However, their practical deployment is stil
arXiv:2605.29505v1 Announce Type: new Abstract: Online 3D scene perception in real time is essential for robotics, AR/VR, and autonomous systems, particularly in edge computing scenarios where computa
arXiv:2605.30099v1 Announce Type: new Abstract: Valuable decisions and highly prioritized analysis now depend on applications such as facial biometrics, social media photo tagging, and human robots in
arXiv:2605.29977v1 Announce Type: new Abstract: High-fidelity ECG interpretation is increasingly reliant on massive foundation models, yet their deployment in clinical edge-care remains hindered by ex
arXiv:2605.29847v1 Announce Type: new Abstract: Reinforcement Learning (RL) has significantly advanced Large Language Models (LLMs) in verifiable domains, but aligning models for open-ended generation
arXiv:2505.20634v2 Announce Type: replace Abstract: Concept shift occurs when the distribution of labels conditioned on the features changes between domains, which can make even a well-tuned ML model
arXiv:2605.25975v2 Announce Type: replace-cross Abstract: Capturing relightable 3D assets from real-world objects is a widely researched problem. Several per-scene optimization-based methods, based on
arXiv:2605.29793v1 Announce Type: new Abstract: Given an untrimmed video and a sentence query, video moment retrieval using language (VMR) aims to locate a target query-relevant moment. Since the untr
arXiv:2605.29695v1 Announce Type: new Abstract: Approximately 10% of newborns require assistance to initiate breathing at birth, and around 5% need ventilation support. Fetal heart rate (FHR) monitori
arXiv:2605.29979v1 Announce Type: cross Abstract: The behavior of LLMs does not depend solely on the model itself. Components of the inference system, such as the inference engine, attention backend,
arXiv:2605.28874v1 Announce Type: new Abstract: Charts play a critical role in conveying numerical data insights through structured visual representations. However, semantic visual understanding and n
arXiv:2605.29675v1 Announce Type: cross Abstract: Collaborations with Generative AI often begin with a short prompt and end with an opaque output, leaving implicit who was involved, what task was bein
arXiv:2605.29997v1 Announce Type: new Abstract: We present FRUC, a feed-forward 3D Gaussian splatting framework for dynamic scene reconstruction from uncalibrated collaborative driving views. Existing
arXiv:2605.29803v1 Announce Type: new Abstract: Graph attention networks learn neighbor importance through data-dependent coefficients, but standard layers lack explicit control over unreliable featur
arXiv:2605.30248v1 Announce Type: new Abstract: Image generation models have evolved from text-conditioned pixel synthesis toward multimodal agents endowed with visual comprehension and tool invocatio
arXiv:2605.29661v1 Announce Type: new Abstract: Monocular 3D shape recovery is fundamental to geometric understanding, yet achieving robust generalization across arbitrary viewpoints and unseen object
arXiv:2605.29539v1 Announce Type: cross Abstract: Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD). However, they fa
arXiv:2605.29494v1 Announce Type: new Abstract: Deep neural network training involves both forward propagation (from features through logits to loss) and backward propagation (from loss through gradie
arXiv:2605.30237v1 Announce Type: cross Abstract: Semi-structured knowledge bases (SKBs) embed textual documents in a typed graph of entities and relations, and underpin applications such as product s
arXiv:2605.30214v1 Announce Type: new Abstract: Third-person singular pronouns have long been used to study stereotypical biases in language models and to test their abilities to reason about referenc
arXiv:2605.29055v1 Announce Type: new Abstract: Hallucination remains a major reliability barrier for production LLM systems, particularly in multi-agent pipelines where unsupported claims can propaga
arXiv:2605.29354v1 Announce Type: cross Abstract: LLM-powered coding agents increasingly participate in software development workflows by generating code, selecting dependencies, and producing package
arXiv:2512.15133v2 Announce Type: replace-cross Abstract: Proteins inherently possess a consistent sequence-structure duality. The abundance of protein sequence data, which can be readily represented
arXiv:2605.30058v1 Announce Type: new Abstract: While LLM agents have demonstrated remarkable task-oriented abilities such as planning, reasoning, and action, few works have treated them as complete h
arXiv:2605.29960v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly leverage long term memory to support persistent and autonomous task execution. However, this capability
arXiv:2605.29734v1 Announce Type: new Abstract: High-performance GPU kernels are essential for efficient LLM deployment, yet optimizing them remains expertise-intensive. Recent LLM-based code generati