Knowledge catalogue
arXiv:2605.29005v1 Announce Type: cross Abstract: Diffusion-based neural solvers for combinatorial optimization repeatedly re-evaluate dense edge/factor interactions, making inference expensive in wal
arXiv:2605.29798v1 Announce Type: new Abstract: Reliable identification of fracture origins in alumina matrix composite hip and knee implants is critical for quality assurance and patient safety, yet
arXiv:2601.06431v3 Announce Type: replace Abstract: Instruction following is critical for large language models, yet real-world instructions often involve multiple constraints with logical structures,
arXiv:2603.14644v3 Announce Type: replace-cross Abstract: Publicly available full-field digital mammography (FFDM) datasets remain limited in size, clinical annotations, and vendor diversity, hinderin
arXiv:2601.04633v2 Announce Type: replace Abstract: Machine-Generated Text (MGT) is becoming increasingly difficult to distinguish from Human-Written Text (HWT). This trend has exacerbated malicious a
arXiv:2605.29940v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted for synthetic data generation, significantly reducing annotation costs. However, most existing stu
arXiv:2605.28980v1 Announce Type: cross Abstract: Given a matrix X, and two ranks r_1 and r_2, the Hadamard decomposition (HD) looks for two low-rank matrices, X_1 of rank r_1 and X_2 of rank r_2, bot
arXiv:2605.30218v1 Announce Type: new Abstract: Temperature-zero BF16 LLM inference is often treated as reproducible, yet the same request can emit different tokens when decoded alone or inside a larg
arXiv:2605.29766v1 Announce Type: new Abstract: Imitation learning has become a cornerstone for solving complex robotic manipulation tasks. In particular, multimodality, which enables robots to captur
arXiv:2605.29647v1 Announce Type: new Abstract: Aerial navigation on Mars requires vision-based pipelines that are robust to the diverse illumination conditions and terrain morphology of the Martian s
arXiv:2605.29498v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become one of the most widely used fine-tuning mechanisms for adapting large language models to new domains, tasks, and u
arXiv:2605.30046v1 Announce Type: cross Abstract: Anomaly detection aims to identify samples that deviate from the nominal data distribution and is central to many safety-critical applications. Howeve
arXiv:2605.29858v1 Announce Type: new Abstract: Temporal action localization (TAL) requires recognizing the target event and localizing its start and end times precisely in untrimmed videos. Recent vi
arXiv:2601.03729v2 Announce Type: replace Abstract: Fine-grained recognition of marine organisms is important for ecological research, biodiversity monitoring, habitat conservation, and evidence-based
arXiv:2605.29642v1 Announce Type: cross Abstract: In federated language modeling, K nodes each hold n samples but cannot pool data or exchange full-precision gradients or weights. We study the minimax
arXiv:2306.10356v3 Announce Type: replace-cross Abstract: Accurate forecasting of renewable generation is crucial to facilitate the integration of Renewable Energy Sources into the power system. Focus
arXiv:2401.08197v3 Announce Type: replace Abstract: This paper considers the problem of completing a rating matrix based on sub-sampled matrix entries as well as observed social graphs and hypergraphs
arXiv:2605.30283v1 Announce Type: new Abstract: MCP Server Proto-OKN (mcp-proto-okn) is a Python-based Model Context Protocol server that enables AI assistants to discover, inspect, query and integrat
arXiv:2605.30190v1 Announce Type: new Abstract: Diffusion-based planning has achieved strong results in single-agent offline reinforcement learning, yet scaling to many-agent systems remains intractab
arXiv:2603.20329v2 Announce Type: replace-cross Abstract: We study the ill-posed problem of recovering a probability measure flow from finitely many moving localized sensors using a Bayes Hilbert fram
arXiv:2605.28999v1 Announce Type: cross Abstract: LLMs are vulnerable to prompt injection attacks. However, this vulnerability has been primarily demonstrated conceptually in academic studies or throu
arXiv:2604.05446v2 Announce Type: replace-cross Abstract: Obtaining high-quality labels is costly, whereas unlabeled covariates are often abundant, motivating semi-supervised inference methods with re
arXiv:2601.14758v4 Announce Type: replace-cross Abstract: Post-training pretrained autoregressive models (ARMs) into masked diffusion models (MDMs) has emerged as a cost-effective way to overcome the
arXiv:2605.28860v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) frequently induces catastrophic forgetting of prior capabilities. Recent work has shown that reinforcement le
arXiv:2605.28825v1 Announce Type: new Abstract: Large language models (LLMs) frequently encode factual and reasoning knowledge in their internal representations that is not faithfully reflected in the
arXiv:2605.30295v1 Announce Type: cross Abstract: Large language models (LLMs) show promise for clinical reasoning and decision support, but evaluation in realistic, electronic health record-congruent
arXiv:2603.27150v2 Announce Type: replace Abstract: Large language models (LLMs) have revolutionized medical reasoning tasks, yet single-agent systems often falter on complex, interdisciplinary proble
arXiv:2605.29859v1 Announce Type: cross Abstract: Recent speech language models rely on encoders that are optimized separately from autoregressive models. Since these encoders are unaware of the downs
arXiv:2603.23234v2 Announce Type: replace Abstract: LLM agents increasingly rely on memory mechanisms to reuse knowledge from past problem-solving experiences. However, existing methods typically cons
arXiv:2605.29795v1 Announce Type: new Abstract: Real-world tasks often lack large labeled datasets, motivating extensive work on learning in low-data regimes. However, existing approaches such as few-
arXiv:2604.14889v2 Announce Type: replace Abstract: While chain-of-thought (CoT) reasoning enables LLMs to solve challenging reasoning tasks, the linear growth of the KV cache leads to substantial mem
arXiv:2507.09574v3 Announce Type: replace-cross Abstract: Recent text-to-image models produce high-quality results but still struggle with precise visual control, balancing multimodal inputs, and requ
arXiv:2605.29953v1 Announce Type: new Abstract: Multi-view multi-person 3D pose estimation in team sports scenarios remains challenging due to player occlusions, appearance similarity caused by team u
arXiv:2605.30159v1 Announce Type: new Abstract: Memory-augmented LLM agents tackle complex long-horizon tasks by recursively summarizing interaction trajectories into compact memory. However, existing
arXiv:2605.29965v1 Announce Type: new Abstract: The development of temporal extensions of Answer Set Programming (ASP) has led to the emergence of non-monotonic linear-time (TEL), dynamic (DEL), and m
arXiv:2605.29212v1 Announce Type: new Abstract: Image quality in modern imaging systems emerges from the coupled effects of the sensor, optics, and computational reconstruction. Ultra-thin metalenses
arXiv:2605.29797v1 Announce Type: new Abstract: When annotators disagree on a label, the disagreement itself carries signal -- and the number of annotators needed to capture it depends on the evaluati
arXiv:2511.14426v2 Announce Type: replace-cross Abstract: In recent years, diffusion-based models have demonstrated exceptional performance in searching for simultaneously stable, unique, and novel (S
arXiv:2605.29987v1 Announce Type: cross Abstract: Although multi-scales representation learning enables elastic-dimension embeddings, nested subspaces often suffer from dimensional redundancy and spec
arXiv:2605.28828v1 Announce Type: cross Abstract: Large Language Models (LLMs) achieve impressive performance across many tasks but remain prone to hallucination, especially in long-form generation wh
arXiv:2605.29920v1 Announce Type: new Abstract: We introduce Midpoint Generative Models (MGM), a principled framework for training one-step generative models. MGM is based on a simple symmetry of Flow
arXiv:2605.29591v1 Announce Type: new Abstract: Modeling the interplay between external stimuli and internal neural representations is a pivotal research area for Brain-Computer Interfaces (BCIs). A m
arXiv:2605.29027v1 Announce Type: new Abstract: The use of Large Language Models (LLMs) is proliferating, yet their performance is observed to vary based on prompting styles and tones. In this study,
arXiv:2605.29512v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as interactive agents, yet their capacity for social and strategic reasoning over extended intera
arXiv:2512.17220v2 Announce Type: replace Abstract: Humans understand long and complex texts by relying on a holistic semantic representation of the content. This global view helps organize prior know
arXiv:2605.29737v1 Announce Type: cross Abstract: LLM-based coding assistants are seeing rapid adoption, offering substantial gains in developer productivity. As organizations increasingly ship code t
arXiv:2602.05370v3 Announce Type: replace Abstract: Iterative Direct Preference Optimization (DPO) has emerged as a widely used paradigm for aligning Large Language Models on reasoning tasks. Existing
arXiv:2605.30263v1 Announce Type: new Abstract: Recent video diffusion foundation models have achieved remarkable progress in high-quality video generation, yet turning them into real-time interactive
arXiv:2605.30288v1 Announce Type: new Abstract: Mid-training has become an important stage in modern LLM development, using large-scale curated mixtures to strengthen capabilities before final post-tr
arXiv:2605.29360v1 Announce Type: new Abstract: Action-conditioned world models are increasingly used as scalable simulators for robot learning, yet current evaluations provide limited evidence that t
arXiv:2605.29850v1 Announce Type: new Abstract: Recent progress in task-optimized neural networks has established encoding models as a powerful tool for predicting brain responses to naturalistic stim
arXiv:2605.29881v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) often hallucinate objects that are not present in the input image, largely because visual grounding weakens as de
arXiv:2605.29577v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a promising framework that unifies perception, reasoning, and control for robot manipulation by adap
arXiv:2605.29862v1 Announce Type: cross Abstract: AI-driven respiratory sound classification (RSC) is promising for automated pulmonary disease detection, yet multi-site deployment is hindered by inte
arXiv:2510.06182v2 Announce Type: replace Abstract: A key component of in-context reasoning is the ability of language models (LMs) to bind entities for later retrieval. For example, an LM might repre
arXiv:2605.29329v1 Announce Type: cross Abstract: A novel two-phase molecule inference framework, mol-infer, has recently been developed to infer chemical graphs with prescribed abstract structures an
arXiv:2605.29765v1 Announce Type: new Abstract: We introduce MMTM, a modular pipeline for topic discovery in long-form video that integrates speech recognition, audio and visual embeddings, and BERTop
arXiv:2510.27391v2 Announce Type: replace Abstract: Modality alignment is critical for vision-language models (VLMs) to effectively integrate information across modalities. However, existing methods e
arXiv:2507.00037v2 Announce Type: replace-cross Abstract: Model fusion seeks to combine independently trained neural networks into a single model without retraining, but is complicated by representati
arXiv:2605.29101v1 Announce Type: new Abstract: Model merging combines fine-tuned checkpoints into a single multi-task model without retraining. Existing methods - such as task arithmetic, model soups