Knowledge catalogue
arXiv:2605.28837v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have demonstrated remarkable capabilities, their reliability is significantly compromised by hallucinations. Existi
arXiv:2601.22274v2 Announce Type: replace Abstract: Real-world federated systems seldom operate on static data: input distributions drift while privacy rules forbid raw-data sharing. We study this set
arXiv:2605.30116v1 Announce Type: new Abstract: Distribution Matching Distillation (DMD) is a widely used paradigm for accelerating inference in few-step video diffusion models. However, DMD-style vid
arXiv:2603.17945v2 Announce Type: replace Abstract: In multilingual pretraining, the test loss of a pretrained model is heavily influenced by the proportion of each language in the pretraining data, n
arXiv:2605.29236v1 Announce Type: new Abstract: Alarm fatigue in intensive care units (ICUs) is a well documented patient safety crisis. Clinical monitors generate 350 or more alarms per patient per d
arXiv:2507.23270v2 Announce Type: replace Abstract: Reconfigurable multi-robot cells offer a promising approach to meet fluctuating assembly demands. However, the recurrent planning of their configura
arXiv:2605.29547v1 Announce Type: cross Abstract: Deep learning optimization relies heavily on the assumption of smooth loss landscapes, a condition systematically violated by modern architectures due
arXiv:2510.15340v2 Announce Type: replace-cross Abstract: State preparation is a cornerstone of quantum technologies, underpinning applications in computation, communication, and sensing. Its importan
arXiv:2602.15239v2 Announce Type: replace Abstract: Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for g
arXiv:2602.01869v3 Announce Type: replace Abstract: LLM-driven agents excel at sequential decision-making but often rely on on-the-fly reasoning, re-deriving solutions even in recurring scenarios. Thi
arXiv:2605.29440v1 Announce Type: cross Abstract: Retrieval-augmented LLM agents increasingly rely on curated skill banks: collections of reusable textual principles that guide decision making on comp
arXiv:2605.29794v1 Announce Type: new Abstract: LLM agents now draw on growing skill libraries to handle complex tasks. However, injecting more skills does not always improve task completion and can e
arXiv:2605.29726v1 Announce Type: new Abstract: In the context of resource-constrained environments such as embedded systems, adapting reduced-size foundation models to downstream tasks has become inc
arXiv:2605.29188v1 Announce Type: new Abstract: Dictionary methods, topic models, and embedding-similarity scorers are widely used in CSS and management research to measure constructs such as 'entrepr
arXiv:2511.17798v2 Announce Type: replace Abstract: Mobile manipulators are designed to perform complex sequences of navigation and manipulation tasks in human-centered environments. While recent opti
arXiv:2602.08013v2 Announce Type: replace Abstract: The rapid adoption of large language models (LLMs) in digital health has been driven by a 'scaling-first' philosophy, i.e., the assumption that clin
arXiv:2605.06322v2 Announce Type: replace Abstract: Language models for molecular design have scaled to hundreds of millions of parameters, yet how they learn chemical grammar is poorly understood. We
arXiv:2502.21004v2 Announce Type: replace Abstract: Dynamic Facial Expression Recognition (DFER) facilitates the understanding of psychological intentions through non-verbal communication. Existing me
arXiv:2508.02537v3 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) offer a powerful framework for solving PDEs by embedding physical laws into the learning process. However,
arXiv:2605.29366v1 Announce Type: new Abstract: Integer Linear Programming (ILP) serves as a versatile framework for modeling a wide range of combinatorial optimization problems, typically addressed b
arXiv:2601.21243v2 Announce Type: replace-cross Abstract: We consider max-min and min-max problems with objective functions that are possibly non-smooth, submodular with respect to the minimiser and c
arXiv:2605.30329v1 Announce Type: new Abstract: Autonomous AI research agents aim to accelerate scientific discovery by automating the research pipeline, from hypothesis generation to peer review. How
arXiv:2605.29502v1 Announce Type: cross Abstract: Low-resource target-language generation is often limited by scarce parallel data, while high-resource source-language monolingual data is abundant but
arXiv:2605.28838v1 Announce Type: cross Abstract: Extracting detailed clinical information from free-text medical narratives remains a practical challenge for researchers and healthcare systems. Termi
arXiv:2605.28900v1 Announce Type: new Abstract: We introduce Spectral Guidance, a framework for controlling diffusion models by leveraging the intrinsic geometry of the generative process. As data is
arXiv:2509.24100v2 Announce Type: replace-cross Abstract: Conformal prediction provides distribution-free prediction sets with finite-sample conditional guarantees. We build upon the RKHS-based framew
arXiv:2605.29678v1 Announce Type: new Abstract: Large language models are highly sensitive to prompts, but this sensitivity is usually studied through task-relevant instructions, demonstrations, or re
arXiv:2605.24700v2 Announce Type: replace Abstract: Creating relightable urban scenes from images or videos is widely useful but highly ill-posed. Urban environments are typically unbounded and extend
arXiv:2605.30257v1 Announce Type: new Abstract: We present Stable-Layers, a reinforcement learning framework that eliminates the need for paired supervision by fine-tuning a pretrained layer decomposi
arXiv:2605.29324v1 Announce Type: new Abstract: Mobile GUI agents excel at immediate reactive control but frequently fail in realistic, long-horizon tasks that require memory. This failure stems from
arXiv:2605.29863v1 Announce Type: new Abstract: Predicting the next mobile application a user will launch is essential for intelligent device resource management and proactive assistance. Existing mod
arXiv:2605.30289v1 Announce Type: new Abstract: Numeric tabular datasets are the dominant data format in scientific practice, yet large language models lack native mechanisms for representing numeric
arXiv:2603.13249v2 Announce Type: replace-cross Abstract: Activation steering offers a computationally efficient mechanism for controlling Large Language Models (LLMs) without fine-tuning. While effec
arXiv:2601.10960v2 Announce Type: replace-cross Abstract: Controllable generation requires language models to realize output characteristics such as reading level, politeness, and toxicity. Existing s
arXiv:2605.29194v1 Announce Type: cross Abstract: Many stochastic physical systems evolve smoothly over time in the sense that the distribution of states changes regularly across time steps. The trans
arXiv:2510.03550v4 Announce Type: replace Abstract: Achieving streaming, fine-grained control over the outputs of autoregressive video diffusion models remains challenging, making it difficult to ensu
arXiv:2605.30112v1 Announce Type: new Abstract: Cross-Reynolds generalisation in neural PDE solvers remains poorly characterised. On the canonical forced 2D Navier-Stokes benchmark, a trained Fourier
arXiv:2603.02803v2 Announce Type: replace Abstract: Recent advances in visual language models (VLMs) have transformed end-to-end document understanding. However, their ability to interpret the complex
arXiv:2605.29076v1 Announce Type: cross Abstract: LLMs have advanced text classification, yet existing paradigms face a trade-off: supervised (label only) fine-tuning is scalable but offers limited re
arXiv:2605.29703v1 Announce Type: cross Abstract: The study of brain morphology changes in normal individuals may capture aspects of functionally-relevant brain aging not fully indicated by gross volu
arXiv:2605.30328v1 Announce Type: new Abstract: Efficient and robust 3D scene representation is crucial in autonomous driving, robotics, and related fields. While RGB images provide valuable content f
arXiv:2605.29655v1 Announce Type: new Abstract: Autoregressive multimodal large language models (MLLMs) enable 3D generation but struggle to scale to high-resolution shapes due to inadequate 3D tokeni
arXiv:2605.29240v1 Announce Type: new Abstract: AI-augmented classrooms generate rich teacher and student feedback before graded outcomes become available, yet these signals can be difficult to transl
arXiv:2512.03010v2 Announce Type: replace Abstract: LiDAR-captured point clouds are often considered the gold standard in active 3D reconstruction. While their accuracy is exceptional in flat regions,
arXiv:2605.29368v1 Announce Type: cross Abstract: The intricate nature of modern surgical care necessitates intelligent systems that can synthesize extensive patient records, support collaborative dec
arXiv:2505.10975v3 Announce Type: replace-cross Abstract: Monaural multi-speaker automatic speech recognition (ASR) remains challenging due to data scarcity and the intrinsic difficulty of recognizing
arXiv:2605.29179v1 Announce Type: cross Abstract: Metal-organic frameworks (MOFs) are excellent candidates for water harvesting due to their tunable pore environments, which can be precisely engineere
arXiv:2605.29954v1 Announce Type: new Abstract: Sparse vision transformers have gained popularity as efficient encoders for medical volumetric segmentation, with Swin emerging as a prominent choice. S
arXiv:2605.29558v1 Announce Type: new Abstract: Severe image degradation under low-light nighttime conditions constitutes a core bottleneck preventing all-day applications for UAV-based single object
arXiv:2605.29229v1 Announce Type: new Abstract: Reasoning distillation transfers complex reasoning abilities from large language models (LLMs) to smaller ones, yet its success depends on how well the
arXiv:2507.06092v4 Announce Type: replace-cross Abstract: Machine learning-based supervised classifiers are widely used for security tasks, and their improvement has been largely focused on algorithmi
arXiv:2601.11178v2 Announce Type: replace Abstract: Social media platforms are increasingly dominated by long-form multimodal content, where harmful narratives are constructed through a complex interp
arXiv:2605.28868v1 Announce Type: cross Abstract: Metagenomic taxonomic annotation aims to identify the microbial origins of DNA fragments in environmental samples. Traditional methods that rely on se
arXiv:2605.29712v1 Announce Type: cross Abstract: Grounded claim factuality checking is important for large language model (LLM) applications such as retrieval-augmented generation, as it helps users
arXiv:2605.30036v1 Announce Type: new Abstract: Large Language Models (LLMs) demonstrate a remarkable capacity to adopt different personas and roles; however, it remains unclear whether they can manif
arXiv:2605.29526v1 Announce Type: cross Abstract: Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addr
arXiv:2605.30151v1 Announce Type: new Abstract: As AI tools become increasingly integrated into educational contexts, questions arise about both their stability over time and their responsiveness to p
arXiv:2605.30015v1 Announce Type: cross Abstract: Supervised Causal Learning (SCL) has shown promise in causal discovery by framing it as a supervised learning problem. However, it suffers from signif
arXiv:2605.29000v1 Announce Type: new Abstract: Traditional lossless text compression preserves every byte, but its gains on natural language are often modest in realistic operating regimes. We study
arXiv:2601.03134v2 Announce Type: replace Abstract: As LLMs gain persuasive capabilities through extended dialogues, they create new opportunities for studying adversarial conversational behavior in e