Knowledge catalogue
arXiv:2604.28118v1 Announce Type: cross Abstract: Transformer models are widely deployed in critical AI applications, yet faults in their attention mechanisms, projections, and other internal componen
arXiv:2604.28176v1 Announce Type: cross Abstract: Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as b
arXiv:2604.26958v1 Announce Type: cross Abstract: Agentic AI systems capable of autonomous goal setting and proactive intervention introduce new challenges for regulating moral-emotional processes in
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:2603.22078v3 Announce Type: replace Abstract: Robot action planning in the real world is challenging as it requires not only understanding the current state of the environment but also predictin
arXiv:2604.27994v1 Announce Type: new Abstract: Learned driving agents often degrade when deployed in unseen environments. This paper studies a deliberately bounded instance of that problem in the CAR
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
arXiv:2604.27085v1 Announce Type: cross Abstract: Fine-tuning Large Language Models (LLMs) on consumer-grade GPUs is highly cost-effective, yet constrained by limited GPU memory and slow PCIe intercon
arXiv:2604.27143v1 Announce Type: cross Abstract: Recent research has demonstrated the potential of Large Language Models (LLMs) for autonomous penetration testing, particularly when using cloud-based
arXiv:2604.27695v1 Announce Type: cross Abstract: Long-term conversational memory requires retrieving evidence scattered across multiple sessions, yet single-pass retrieval fails on temporal and multi
arXiv:2604.28149v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in en
arXiv:2604.28182v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become essential to the post-training of large language models (LLMs) for reasoning, agentic capabilities and alignmen
arXiv:2604.27454v1 Announce Type: new Abstract: Large language models (LLMs) sometimes exhibit qualitative shifts in response style under sustained self-referential dialogue conditions (Berg et al., 2
arXiv:2604.27996v1 Announce Type: new Abstract: This paper examines how different types of large language model (LLM) agents perform on scientific visualization (SciVis) tasks, where users generate vi
arXiv:2502.07189v2 Announce Type: replace Abstract: The remarkable performance of modern deep neural networks (DNNs) is largely driven by their massive scale, often comprising tens to hundreds of mill
arXiv:2604.28024v1 Announce Type: new Abstract: Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learnin
arXiv:2604.27935v1 Announce Type: new Abstract: This paper presents an expert-guided active-inference-inspired framework for adaptive UAV swarm trajectory planning. The proposed method converts multi-
arXiv:2503.04956v2 Announce Type: replace-cross Abstract: Time series forecasts are widely used to inform decisions. Human decision-makers interpret these forecasts, incorporate prior experience and u
arXiv:2604.27660v1 Announce Type: new Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learn
arXiv:2604.27972v1 Announce Type: new Abstract: Since the dawn of Trading Card Games, the genre has grown into a multi-billion-dollar industry engaging millions of analog and digital players worldwide
arXiv:2604.27969v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly used to translate visual artifacts into code, from UI mockups into HTML to scientific plots
arXiv:2604.27906v1 Announce Type: new Abstract: Persistent AI memory is often reduced to a retrieval problem: store prior interactions as text, embed them, and ask the model to recover relevant contex
arXiv:2604.27653v1 Announce Type: new Abstract: Conventional push-broom hyperspectral imaging suffers from slow acquisition speeds, precluding real-time object detection; in contrast, snapshot spectra
arXiv:2604.27918v1 Announce Type: new Abstract: Existing talking avatar methods typically adopt an image-to-video pipeline conditioned on a static reference image within the same scene as the target g
arXiv:2604.27914v1 Announce Type: new Abstract: When language models lack relevant knowledge for a given query, they frequently generate plausible responses that can be hallucinations, rather than adm
arXiv:2604.27175v1 Announce Type: new Abstract: Contact-rich manipulation is challenging due to its high dimensionality, the requirement for long time horizons, and the presence of hybrid contact dyna
arXiv:2604.27764v1 Announce Type: new Abstract: Mango cultivation is crucial in the agricultural sector, significantly contributing to economic development and food security. However, diseases affecti
arXiv:2604.27895v1 Announce Type: new Abstract: As one of the mainstream models of artificial intelligence, world models allow agents to learn the representation of the environment for efficient predi
arXiv:2509.16248v3 Announce Type: replace-cross Abstract: This paper presents GRAPHMEND, a high-level compiler technique that eliminates FX graph breaks in PyTorch 2 programs. Although PyTorch 2 intro
arXiv:2601.10702v2 Announce Type: replace-cross Abstract: Deploying large language models in long-horizon, goal-oriented interactions remains challenging because similar entities and facts recur under
arXiv:2604.28111v1 Announce Type: new Abstract: End-to-end (E2E) autonomous driving presents a promising approach for translating perceptual inputs directly into driving actions. However, prohibitive
arXiv:2503.12844v2 Announce Type: replace Abstract: For people affected by blindness and low vision (BLV), safe and independent navigation remains a major challenge, impacting over 2.2 billion individ
arXiv:2603.26571v3 Announce Type: replace-cross Abstract: At ultra-low bitrates, high-fidelity reconstruction requires sampling plausible videos from the posterior rather than regressing to oversmooth
arXiv:2604.28196v1 Announce Type: new Abstract: Driving world models serve as a pivotal technology for autonomous driving by simulating environmental dynamics. However, existing approaches predominant
arXiv:2604.27351v1 Announce Type: new Abstract: Agentic large language model systems have demonstrated strong capabilities. However, their reliance on language as the universal interface fundamentally
arXiv:2604.27037v1 Announce Type: cross Abstract: The Hypencoder, proposed by Killingback et al., is a retrieval framework that replaces the fixed inner-product scoring function used in standard bi-en
arXiv:2604.16399v2 Announce Type: replace-cross Abstract: The widespread adoption of AI-assisted development tools in 2025 -- and the emergence of vibe coding, a practice of generating complete applic
arXiv:2501.19143v2 Announce Type: replace Abstract: As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial a
arXiv:2503.01611v3 Announce Type: replace Abstract: Instruction following is a critical ability for Large Language Models to perform downstream tasks. The standard approach to instruction tuning has r
arXiv:2604.27891v1 Announce Type: new Abstract: Agent orchestration frameworks -- LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others -- place an external orchestrator above the LLM, tracking
arXiv:2604.27766v1 Announce Type: cross Abstract: Poetry has long been a central art form for Arabic speakers, serving as a powerful medium of expression and cultural identity. While modern Arabic spe
arXiv:2604.27122v1 Announce Type: new Abstract: Text-to-image person re-identification (TI-ReID) relies on natural-language text description to retrieve top matching individuals from a large gallery o
arXiv:2604.27335v1 Announce Type: new Abstract: Web filtering systems rely on accurate web content classification to block cyber threats, prevent data exfiltration, and ensure compliance. However, cla
arXiv:2604.27607v1 Announce Type: new Abstract: We present JaiTTS-v1.0, a state-of-the-art Thai voice cloning text-to-speech model built through continual training on a large Thai-centric speech corpu
arXiv:2604.27366v1 Announce Type: new Abstract: Recent advances in vision language action (VLA) models have shown remarkable potential for autonomous driving by directly mapping multimodal inputs to c
arXiv:2310.02277v4 Announce Type: replace-cross Abstract: We present Junk DNA Hypothesis by adopting a novel task-centric angle for the pre-trained weights of large language models (LLMs). It has been
arXiv:2604.27713v1 Announce Type: new Abstract: The risks posed by AI features are increasing as they are rapidly integrated into software applications. In response, regulations and standards for safe
arXiv:2604.27661v1 Announce Type: new Abstract: Detecting language ideologies is a valuable yet complex task for understanding how identities are constructed through discourse. In Luxembourg's multicu
arXiv:2604.28129v1 Announce Type: cross Abstract: Multi-turn prompt injection follows a known attack path -- trust-building, pivoting, escalation but text-level defenses miss covert attacks where indi
arXiv:2604.27564v1 Announce Type: new Abstract: Face recognition from a single image per person is a challenging problem because the training sample is extremely small. We consider a variation of this
arXiv:2604.27224v1 Announce Type: new Abstract: Quadrupedal loco-manipulation is commonly built on visual perception and proprioception. Yet reliable contact-rich manipulation remains difficult: visio
arXiv:2603.20965v2 Announce Type: replace-cross Abstract: This paper studies whether a lightweight supervised aggregator can combine diverse zero-shot large language model outputs into a stronger down
arXiv:2604.27759v1 Announce Type: cross Abstract: Integrating domain knowledge into deep neural networks is a promising way to improve generalization. Existing methods either encode prior knowledge in
arXiv:2604.27505v1 Announce Type: new Abstract: While Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm for text-to-image generation, its application to image editing rem
arXiv:2604.27712v1 Announce Type: cross Abstract: Scene-text image captioning requires fusing three information streams -- visual features, OCR-detected text, and linguistic knowledge -- to generate d
arXiv:2502.14541v3 Announce Type: replace Abstract: The rapid advancement of Large Language Models (LLMs) has opened new opportunities in recommender systems by enabling zero-shot recommendation witho
arXiv:2512.03449v3 Announce Type: replace Abstract: Background and Objective: Radiomics of knee MRI requires robust, anatomically meaningful regions of interest (ROIs) that jointly capture cartilage a
arXiv:2604.27487v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA), which leverages the insight that model updates typically reside in a low-dimensional space, has significantly improved the t
arXiv:2604.27297v1 Announce Type: new Abstract: Deriving governing equations from empirical observations is a longstanding challenge in science. Although artificial intelligence (AI) has demonstrated