Knowledge catalogue
arXiv:2604.18951v1 Announce Type: cross Abstract: Adaptive multi-agent systems (MAS) are increasingly adopted to tackle complex problems.However, the narrow task coverage of their optimization raises
arXiv:2508.08080v2 Announce Type: replace Abstract: Symbolic Regression (SR) is a well-established framework for generating interpretable or white-box predictive models. Although SR has been successfu
arXiv:2604.18758v1 Announce Type: new Abstract: Low-resource machine translation requires methods that differ from those used for high-resource languages. This paper proposes a novel in-context learni
arXiv:2604.18939v1 Announce Type: new Abstract: Table annotation is crucial for making web and enterprise tables usable in downstream NLP applications. Unlike textual data where learning semantically
arXiv:2512.15907v2 Announce Type: replace Abstract: Evaluating the quality of tables generated by large language models (LLMs) remains an open challenge: existing metrics either flatten tables into te
arXiv:2505.22176v3 Announce Type: replace Abstract: Evaluating tables qualitatively and quantitatively poses a significant challenge, as standard metrics often overlook subtle structural and content-l
arXiv:2604.19372v1 Announce Type: cross Abstract: Graph representation learning has achieved notable success in encoding graph-structured data into latent vector spaces, enabling a wide range of downs
arXiv:2604.18932v1 Announce Type: cross Abstract: Despite growing interest in Quranic data research, existing Quran datasets remain limited in both scale and diversity. To address this gap, we present
arXiv:2601.09953v2 Announce Type: replace Abstract: Standardized math assessments require expensive human pilot studies to establish the difficulty of test items. We investigate the predictive value o
arXiv:2604.19245v1 Announce Type: cross Abstract: Repair, an important resource for resolving trouble in human-human conversation, remains underexplored in human-LLM interaction. In this study, we inv
arXiv:2604.19548v1 Announce Type: cross Abstract: Large Language Model agents have rapidly evolved from static text generators into dynamic systems capable of executing complex autonomous workflows. T
arXiv:2604.18905v1 Announce Type: new Abstract: The collaboration between humans and robots is critical in many robotic applications, especially in those requiring physical human-robot interaction (pH
arXiv:2604.18857v1 Announce Type: cross Abstract: In continual learning, the primary challenge is to learn new information without forgetting old knowledge. A common solution addresses this trade-off
arXiv:2601.18296v2 Announce Type: replace-cross Abstract: Temporal Knowledge Graph Question Answering (TKGQA) is inherently challenging, as it requires sophisticated reasoning over dynamic facts with
arXiv:2604.19295v1 Announce Type: new Abstract: Test-time training (TTT) adapts model parameters on unlabeled test instances during inference time, which continuously extends capabilities beyond the r
arXiv:2602.00758v2 Announce Type: replace Abstract: Search-engine date filters are widely used to enforce pre-cutoff retrieval in retrospective evaluations of search-augmented forecasters. We show thi
arXiv:2604.18860v1 Announce Type: cross Abstract: GUI agents that control desktop computers via screenshot-and-click loops introduce a new class of vulnerability: the observation-to-action gap (mean 6
arXiv:2604.19420v1 Announce Type: new Abstract: Maintaining long-term accuracy of stereo camera calibration parameters is important for autonomous systems' perception. This work proposes Online Tracki
arXiv:2509.18831v2 Announce Type: replace-cross Abstract: Recent advances in diffusion models have significantly improved image and video synthesis. In addition, several concept control methods have b
arXiv:2602.06400v2 Announce Type: replace-cross Abstract: The prediction of 3D semantic occupancy enables autonomous vehicles (AVs) to perceive the fine-grained geometric and semantic scene structure
arXiv:2510.08240v2 Announce Type: replace Abstract: Harnessing the power of LLMs requires a delicate dance between being helpful and harmless. This creates a fundamental tension between two competing
arXiv:2604.18728v1 Announce Type: cross Abstract: Many neural network (NN) verification systems represent the network's input-output relation as a constraint program. Sound and complete, representatio
arXiv:2412.01763v3 Announce Type: replace-cross Abstract: We study a censored variant of the data-driven newsvendor problem, where the decision-maker must select an ordering quantity that minimizes ex
arXiv:2603.20897v3 Announce Type: replace-cross Abstract: The strong and continuous increase of AI-based services leads to the steady proliferation of AI data centres worldwide with the unavoidable es
arXiv:2604.19064v1 Announce Type: new Abstract: In vision-and-language navigation (VLN), self-improvement from policy-induced experience, using only standard VLN action supervision, critically depends
arXiv:2604.18828v1 Announce Type: new Abstract: Artificial Intelligence (AI) surrogate models provide a computationally efficient alternative to full-physics simulations, but no public datasets curren
arXiv:2604.19212v1 Announce Type: new Abstract: Graph neural networks (GNNs) are the standard for learning on graphs, yet they have limited expressive power, often expressed in terms of the Weisfeiler
arXiv:2604.19447v1 Announce Type: new Abstract: We present a dual-track pipeline for detecting biblical allusions in literary fiction and apply it to the novels of Cormac McCarthy. A bottom-up embeddi
arXiv:2601.09926v3 Announce Type: replace Abstract: Current approaches to proactive assistance move beyond the ask-and-respond paradigm by anticipating user needs. In practice, they either burden user
arXiv:2604.19139v1 Announce Type: cross Abstract: As Large Language Models (LLMs) continue to evolve through alignment techniques such as Reinforcement Learning from Human Feedback (RLHF) and Constitu
arXiv:2604.19645v1 Announce Type: new Abstract: An earlier paper (Hong, Potteiger, and Zapata 2026) established that an unoptimized GPT 4.1 prompt predicts fan-reported experience ratings within one p
arXiv:2604.19620v1 Announce Type: new Abstract: Free-association norms provide essential empirical data for investigating linguistic, semantic, and cultural phenomena in the cognitive sciences. Althou
arXiv:2604.18850v1 Announce Type: cross Abstract: AI systems are increasingly embedded in multi-user social environments, yet most alignment frameworks conceptualize interaction as a dyadic relationsh
arXiv:2604.18606v1 Announce Type: cross Abstract: Damage caused by bushfires and volcanic eruptions escalates rapidly when detection is delayed, making fast and reliable early warning capabilities ess
arXiv:2604.19113v1 Announce Type: cross Abstract: Generative answer engines expose content through selective citation rather than ranked retrieval, fundamentally altering how visibility is determined.
arXiv:2604.19218v1 Announce Type: new Abstract: Learning identity-discriminative representations with multi-scene generality has become a critical objective in person re-identification (ReID). However
arXiv:2604.18900v1 Announce Type: new Abstract: Aerobat is a bat-inspired flapping-wing robot with a wing gait generate by the computational structure, a planar linkage of carbon fiber links driven by
arXiv:2508.12121v5 Announce Type: replace Abstract: We show that gating mechanisms in recurrent neural networks (RNNs) induce lag-dependent and direction-dependent effective learning rates, even when
arXiv:2604.19633v1 Announce Type: new Abstract: Evaluating the reasoning capabilities of Large Language Models (LLMs) for complex, quantitative financial tasks is a critical and unsolved challenge. St
arXiv:2604.18967v1 Announce Type: new Abstract: Chest X-ray (CXR) radiology report generation (RRG) models have shown rapid progress, yet their clinical utility remains uncertain due to limited evalua
arXiv:2604.19261v1 Announce Type: new Abstract: The evaluation of narrative quality remains a complex challenge, as it involves subjective factors such as plot, character development, and emotional im
arXiv:2407.05102v2 Announce Type: replace-cross Abstract: Executing flow estimation using Deep Learning (DL)-based soft sensors on resource-limited IoT devices has demonstrated promise in terms of rel
arXiv:2604.19377v1 Announce Type: new Abstract: The emergence of sixth-generation (6G) technologies has introduced new challenges and opportunities for machine learning (ML) applications in Internet o
arXiv:2510.06860v2 Announce Type: replace-cross Abstract: AC Optimal Power Flow (ACOPF) is computationally intensive for large-scale grids, often requiring prohibitive solution times with conventional
arXiv:2604.18718v1 Announce Type: cross Abstract: Agentic security systems increasingly audit live targets with tool-using LLMs, but prior systems fix a single coordination topology, leaving unclear w
arXiv:2511.01233v3 Announce Type: replace Abstract: We review human evaluation practices in automatic, speech-driven 3D gesture generation and find a lack of standardisation and frequent use of flawed
arXiv:2604.19089v1 Announce Type: new Abstract: Large language models (LLMs) require frequent knowledge updates to reflect changing facts and mitigate hallucinations. To meet this demand, lifelong kno
arXiv:2604.19635v1 Announce Type: cross Abstract: While generative models have set new benchmarks for Target Speaker Extraction (TSE), their inherent reliance on global context precludes deployment in
arXiv:2604.18756v1 Announce Type: cross Abstract: Large Language Models (LLMs) remain vulnerable to optimization-based jailbreak attacks that exploit internal gradient structure. While Sparse Autoenco
arXiv:2604.18846v1 Announce Type: cross Abstract: Barren-plateau results have established exponential gradient suppression as a widely cited obstacle to the scalability of variational quantum algorith
arXiv:2604.19571v1 Announce Type: new Abstract: Language-driven 3D Gaussian Splatting (3DGS) editing provides a more convenient approach for modifying complex scenes in VR/AR. Standard pipelines typic
arXiv:2604.18716v1 Announce Type: cross Abstract: Today, machine learning is widely applied in sensitive, security-related, and financially lucrative applications. Model extraction attacks undermine c
arXiv:2604.19070v1 Announce Type: new Abstract: Zero-shot reasoning on text-rich networks (TRNs) remains a challenging frontier, as models must integrate textual semantics with relational structure wi
arXiv:2512.07761v3 Announce Type: replace Abstract: Large language models have seen widespread adoption, yet they remain vulnerable to multi-turn jailbreak attacks, threatening their safe deployment.
arXiv:2604.19473v1 Announce Type: new Abstract: Generating high-quality videos from complex temporal descriptions that contain multiple sequential actions is a key unsolved problem. Existing methods a
arXiv:2604.19748v1 Announce Type: new Abstract: Recent advances in image generation and editing have opened new opportunities for virtual try-on. However, existing methods still struggle to meet compl
arXiv:2604.18607v1 Announce Type: cross Abstract: LLM-driven program evolution can discover high-quality programs, but its cost and run-to-run variance hinder reliable progress. We propose TurboEvolve
arXiv:2604.18592v1 Announce Type: cross Abstract: We introduce a two-dimensional (2D) early exit strategy that coordinates layer-wise and sentence-wise exiting for classification tasks in large langua
arXiv:2604.19221v1 Announce Type: new Abstract: Full-duplex speech interaction, as the most natural and intuitive mode of human communication, is driving artificial intelligence toward more human-like
arXiv:2604.19712v1 Announce Type: new Abstract: In [97,99,100], an fl-RDT framework is introduced to characterize statistical computational gaps (SCGs). Studying symmetric binary perceptrons (SBPs), [