Knowledge catalogue
arXiv:2605.01399v1 Announce Type: new Abstract: The conventional Retrieval-Augmented Generation (RAG) paradigm of injecting raw retrieved texts into the Large Language Model (LLM)'s context often resu
arXiv:2601.20055v2 Announce Type: replace Abstract: Despite the syntactic fluency of Large Language Models (LLMs), ensuring their logical correctness in high-stakes domains remains a fundamental chall
arXiv:2605.02746v1 Announce Type: new Abstract: Accurate histological differentiation between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) is critical for personalized treatment in non-small
arXiv:2605.01391v1 Announce Type: new Abstract: Existing benchmarks for Vision-Language Models (VLMs) primarily evaluate spatio-temporal understanding on simple single-action videos, closed attribute
arXiv:2605.01081v1 Announce Type: new Abstract: The performance of state-of-the-art object detectors degrades significantly under adverse weather, causing a safety-critical domain shift problem for au
arXiv:2605.01331v1 Announce Type: new Abstract: Image steganalysis, which aims at detecting secret information concealed within images, has become a critical countermeasure for assessing the security
arXiv:2605.00410v1 Announce Type: new Abstract: A multi-agent pipeline with N agents typically issues N LLM calls per run. Merging agents into fewer calls (compound execution) promises token savings,
arXiv:2605.00111v1 Announce Type: new Abstract: Person re-identification (Re-ID) aims to match images of the same individual across non-overlapping camera views and remains challenging due to domain s
arXiv:2605.00369v1 Announce Type: new Abstract: We study how large language models can be used to evolve inventory policies in online, non-stationary environments. Our work is motivated by recent adva
arXiv:2605.00667v1 Announce Type: new Abstract: Safety is a primary challenge in real-world reinforcement learning (RL). Formulating safety requirements as state-wise constraints has become a prominen
arXiv:2508.06361v4 Announce Type: replace Abstract: Large Language Models (LLMs) are widely deployed in reasoning, planning, and decision-making tasks, making their trustworthiness critical. A signifi
arXiv:2605.00348v1 Announce Type: cross Abstract: Recent multi-bit watermarking methods for large language models (LLMs) prioritize capacity over reliability, often conflating decoding with detection.
arXiv:2407.10853v5 Announce Type: replace Abstract: Bias and fairness risks in Large Language Models (LLMs) vary substantially across deployment contexts, yet existing approaches lack systematic guida
arXiv:2504.11901v5 Announce Type: replace Abstract: The growing integration of robots in shared environments-such as warehouses, shopping centres, and hospitals-demands a deep understanding of the und
arXiv:2605.00083v1 Announce Type: new Abstract: Accurate forecasting of bus ridership (passengers numbers) is crucial for efficient management and optimization of public transport systems. Traditional
arXiv:2605.00675v1 Announce Type: new Abstract: Medical imaging datasets are often characterized by extreme class imbalances, where rare pathologies are significantly underrepresented compared to comm
arXiv:2605.00059v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) finds extensive application in autonomous drone navigation within complex, high-risk environments. However, its practi
arXiv:2605.00177v1 Announce Type: cross Abstract: We consider the problem of synthesizing photorealistic, physically plausible combustion effects in in-the-wild 3D scenes. Traditional CFD and graphics
arXiv:2509.24621v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) are emerging as versatile foundations for mixed-modality retrieval. Yet, they often require heavy post-hoc
arXiv:2605.00498v1 Announce Type: new Abstract: Recent advances in Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have made it standard practice to reconstruct 3D scenes from multi-vie
arXiv:2511.08156v2 Announce Type: replace Abstract: Land Use and Land Cover (LULC) mapping is a fundamental task in Earth Observation (EO). However, current LULC models are typically developed for a s
arXiv:2510.19897v2 Announce Type: replace Abstract: We investigate how agents built on pretrained large language models (LLMs) can learn target classification functions from labeled examples without p
arXiv:2605.00051v1 Announce Type: new Abstract: Anticipating traffic accidents is a critical yet unresolved problem for autonomous driving, hindered by the inherent complexity of modeling interactions
arXiv:2605.00644v1 Announce Type: new Abstract: Energy-based models (EBMs) are a flexible class of deep generative models and are well-suited to capture complex dependencies in multimodal data. Howeve
arXiv:2605.00760v1 Announce Type: new Abstract: This paper deals with solving the 2D Helmholtz equation on non-parametric domains, leveraging a physics-informed neural operator network based on the De
arXiv:2605.00480v1 Announce Type: new Abstract: Active learning aims to reduce annotation cost by selectively querying informative samples for supervision under a limited labeling budget. In this work
arXiv:2510.17281v5 Announce Type: replace Abstract: Scaling up data, parameters, and test-time computation has been the mainstream methods to improve LLM systems (LLMsys), but their upper bounds are a
arXiv:2505.23723v2 Announce Type: replace Abstract: The emergence of large language model (LLM)-based agents has significantly advanced the development of autonomous machine learning (ML) engineering.
arXiv:2605.00025v1 Announce Type: cross Abstract: Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communicatio
arXiv:2605.00764v1 Announce Type: new Abstract: Urban perception describes how people subjectively evaluate urban environments, shaping how cities are experienced and understood. Existing computationa
arXiv:2605.00169v1 Announce Type: cross Abstract: Network digital twins (NDTs) are transforming network management by offering precise virtual replicas of physical network systems. However, their reli
arXiv:2605.00778v1 Announce Type: new Abstract: In biomechanical systems, observable performance is often used as a proxy for underlying system organization. However, this assumption implicitly presum
arXiv:2603.25860v2 Announce Type: replace-cross Abstract: Transformer architectures have achieved remarkable empirical success in modeling contextual relations, yet a clear understanding of their expr
arXiv:2407.16216v3 Announce Type: replace Abstract: Large language models (LLMs) trained via pretraining and supervised fine-tuning (SFT) can still produce harmful and misaligned outputs, or struggle
arXiv:2605.00257v1 Announce Type: new Abstract: The inception of Large Language Models (LLMs) has catalyzed AI adoption in the finance sector, yet their reliability in complex, jurisdiction-specific t
arXiv:2605.00510v1 Announce Type: cross Abstract: Complex physical systems, from supersonic turbulence to the macroscopic structure of the universe, are governed by continuous multiscale dynamics. Whi
Orchestration is no longer just about moving data; it is about governing enterprise intelligence. To reflect our deep commitment to and embrace of open-source software, we shared earlier that Cloud Co
arXiv:2603.15988v2 Announce Type: replace-cross Abstract: Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies. However, subjective evalua
arXiv:2512.01116v3 Announce Type: replace Abstract: The integration of histology images and gene profiles has shown great promise for improving survival prediction in cancer. However, current approach
arXiv:2509.23330v3 Announce Type: replace Abstract: Large language models (LLMs) have achieved significant advancements in reasoning capabilities through reinforcement learning (RL) via environmental
arXiv:2506.11989v3 Announce Type: replace Abstract: Test-time scaling offers a promising way to improve the reasoning performance of vision-language large models (VLLMs) without additional training. I
arXiv:2512.04694v3 Announce Type: replace Abstract: Effective earthquake risk reduction relies on accurate site-specific evaluations, which require models capable of representing the influence of loca
arXiv:2605.00143v1 Announce Type: new Abstract: Humor is a fundamental cognitive phenomenon in which humans derive pleasure from the expectation violations and their resolution, exemplifying the brain
arXiv:2605.00300v1 Announce Type: cross Abstract: Public inference benchmarks compare AI systems at the model and provider level, but the unit at which deployment decisions are actually made is the en
arXiv:2605.00182v1 Announce Type: new Abstract: Proteins are shaped by gradual evolution under biophysical and functional constraints. Protein language models learn rich evolutionary constraints from
arXiv:2605.00793v1 Announce Type: cross Abstract: With the development of deep learning, medical image processing has been widely used to assist clinical research. This paper focuses on the denoising
arXiv:2605.00116v1 Announce Type: new Abstract: In this article, we introduce ViLegalNLI, the first large-scale Vietnamese Natural Language Inference (NLI) dataset specifically constructed for the leg
arXiv:2604.28064v1 Announce Type: new Abstract: This comprehensive review examines the evolution and the current state of the art in three-dimensional (3D) reconstruction techniques in manufacturing a
arXiv:2604.27117v1 Announce Type: cross Abstract: Recommender systems increasingly incorporate textual reviews to enrich user and item representations. However, most review-aware models remain optimiz
arXiv:2604.27550v1 Announce Type: cross Abstract: Privacy policies are essential for users to understand how service providers handle their personal data. However, these documents are often long and c
arXiv:2604.27266v1 Announce Type: new Abstract: This paper introduces AutoREC, an open-source Python package for developing reinforcement learning (RL) agents to automatically generate equivalent circ
arXiv:2604.27361v1 Announce Type: new Abstract: Synthesizing realistic 3D indoor scenes remains challenging due to data scarcity and the difficulty of simultaneously enforcing global architectural con
arXiv:2604.28186v1 Announce Type: cross Abstract: Most familiar equilibrium concepts, such as Nash and correlated equilibrium, guarantee only that no single player can improve their utility by deviati
arXiv:2604.27728v1 Announce Type: new Abstract: The advancement of automated vehicles introduces complex safety challenges, particularly in dynamic and unpredictable environments where AI-enabled perc
arXiv:2504.19342v3 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm in artificial intelligence to align large models with human pr
arXiv:2102.05231v1 Announce Type: cross Abstract: Color is an essential component of graphic design, acting not only as a visual factor but also carrying cultural implications. However, existing resea
arXiv:2604.27547v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) for domain-specific tasks requires training datasets that comprehensively cover the target capabilities a pract
arXiv:2604.27952v1 Announce Type: cross Abstract: Joint image compression and wireless transmission remain relatively underexplored compared to generic image restoration, despite its importance in pra
arXiv:2604.27166v1 Announce Type: new Abstract: We focus on the problem of Answer-Level Fine-Tuning (ALFT), where the goal is to optimize a language model based on the correctness or properties of its
arXiv:2407.19001v3 Announce Type: replace Abstract: This paper studies effective prompt pool learning for Continual Category Discovery (CCD), a challenging open-world setting where a model must discov