Knowledge catalogue
arXiv:2512.07019v4 Announce Type: replace-cross Abstract: The proliferation of Large Language Models (LLMs) necessitates valid evaluation methods to provide guidance for both downstream applications a
arXiv:2608.28860v1 Announce Type: new Abstract: Large Language Models (LLMs) often answer the same factual question differently across languages. We study whether cross-lingual latent-space interventi
arXiv:2608.30303v1 Announce Type: new Abstract: Search agents reduce hallucination by grounding answers in retrieved web evidence. Yet reliance on retrieval also creates an attack surface: poisoned co
arXiv:2608.30679v1 Announce Type: cross Abstract: Large Reasoning Models produce Long Chains-of-Thought (LCoTs) which involve breaking down the problem into smaller reasoning steps before reaching the
arXiv:2608.30955v1 Announce Type: new Abstract: Accurate action models are critical for effective planning. Existing action-model learning methods largely assume simple action representations or becom
arXiv:2608.29769v1 Announce Type: new Abstract: Traversing sparse 3D structures requires humanoid robots to perceive thin, overhanging geometry while executing agile, accurate whole-body motions. We s
arXiv:2608.30520v1 Announce Type: new Abstract: Automated material handling systems in semiconductor fabs are operated by a material control system (MCS) that must schedule a relay route for every tra
arXiv:2505.22635v2 Announce Type: replace-cross Abstract: A common approach for teaching large language models (LLMs) to reason is to train on chain-of-thought (CoT) traces of in-distribution reasonin
arXiv:2608.30584v1 Announce Type: new Abstract: Despite the impressive progress of recent MLLMs on spatio-temporal video grounding (STVG), existing evaluations and training data focus primarily on sim
arXiv:2405.18540v3 Announce Type: replace Abstract: Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large langu
arXiv:2608.30699v1 Announce Type: cross Abstract: Long-tailed distributions are prevalent in real-world semi-supervised learning (SSL), where pseudo-labels tend to favor majority classes, leading to d
arXiv:2412.18707v3 Announce Type: replace Abstract: Multiple valid translations of a single literary work naturally exist. We investigate strategies for leveraging these multi-reference datasets to im
arXiv:2608.30753v1 Announce Type: cross Abstract: In large-scale e-commerce retrieval, dual-encoder retrievers are op- timized for contrastive similarity, whereas downstream rerankers capture finer-gr
arXiv:2608.29494v1 Announce Type: new Abstract: Much of human health and function unfolds beyond the clinic, through the movements of everyday life. Wrist-worn accelerometers capture these movements c
arXiv:2608.30682v1 Announce Type: cross Abstract: Learning materials properties from scarce labels and unlabeled crystals is a central challenge for data-driven materials discovery. We present SemiMat
arXiv:2608.30328v1 Announce Type: new Abstract: Classical numerical solvers for partial differential equations (PDEs) are computationally expensive to solve repeatedly across varying initial condition
arXiv:2412.15957v2 Announce Type: replace-cross Abstract: The rapid development of large language models (LLMs) has transformed many industries, including healthcare. In practice, hospitals and patien
arXiv:2608.30072v1 Announce Type: cross Abstract: Token prediction is a central pre-training objective for modern language models. Despite its empirical success, why token prediction learns broadly us
arXiv:2608.29305v1 Announce Type: cross Abstract: Large language model (LLM) agents have demonstrated remarkable proficiency in manually constructed environments, yet their performance frequently coll
arXiv:2608.31028v1 Announce Type: cross Abstract: Scientific discovery often requires reasoning over competing hypotheses that are consistent with experimental observations. For mixed-variable and com
arXiv:2608.28910v1 Announce Type: new Abstract: Coded computing has emerged as a powerful paradigm for mitigating the impact of straggling workers in distributed computing systems. However, existing c
arXiv:2608.31076v1 Announce Type: cross Abstract: Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experim
arXiv:2607.10851v2 Announce Type: replace Abstract: Medical image classification models are ideally expected to identify diagnostically relevant regions while making predictions, yet standard classifi
arXiv:2608.29030v1 Announce Type: new Abstract: In-context watermarking (ICW) prepends an instruction to a query asking the model to embed a statistically detectable signal in its response. It thus eq
arXiv:2608.29268v1 Announce Type: new Abstract: PCB engineering drawings mix sparse graphics, dense tables, and text whose meaning depends on page position. Localizing the regions and sending crops to
arXiv:2608.30773v1 Announce Type: new Abstract: In animals such as elephants and octopuses, acquiring non-visual information about an object and physically engaging with it are inseparable processes m
arXiv:2608.30426v1 Announce Type: new Abstract: Current dialogue systems struggle with dynamic information retrieval, often leading to hallucinations and lower response accuracy. We address this by ad
arXiv:2608.30305v1 Announce Type: new Abstract: Image restoration models are typically trained with a fixed set of capabilities. When new restoration requirements emerge, existing solutions usually tr
arXiv:2608.30457v1 Announce Type: cross Abstract: Multimodal geometry reasoning requires VLMs to extract precise visual relations and preserve them through multi-step deduction. Existing free-form tra
arXiv:2602.03493v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) methods have emerged as crucial techniques for adapting large pre-trained models to downstream tasks under computational
arXiv:2510.26824v2 Announce Type: replace-cross Abstract: Wide access to advanced experimental methods in materials science has given rise to an abundance of procedural knowledge, which is scattered a
arXiv:2608.29054v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a cornerstone for representing complex relational dependencies in diverse multimedia tasks, particularly in
arXiv:2608.28601v1 Announce Type: new Abstract: Interactive visualizations support conceptual understanding in undergraduate mathematics, but building them has required programming expertise most inst
arXiv:2608.28926v1 Announce Type: new Abstract: We propose a multi-task learning approach for multi-party dialogue intent recognition that leverages an auxiliary task that models turn-taking dynamics.
arXiv:2606.09389v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly applied to real-world legal tasks, evaluating the reliability of their open-ended legal responses h
arXiv:2603.28301v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models achieve strong performance in robotic manipulation by leveraging pre-trained vision-language backbones. However,
arXiv:2608.30428v1 Announce Type: cross Abstract: Strategic deception by LLM and VLM agents has emerged as a central AI alignment and safety concern. Social-deduction games (where each player holds a
arXiv:2608.29269v1 Announce Type: new Abstract: Relightable interactive scene reconstruction aims to build an editable 3D model from scans of different object arrangements and render new layouts under
arXiv:2608.30935v1 Announce Type: cross Abstract: Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions across tasks, environments, and robot embodi
arXiv:2608.30853v1 Announce Type: new Abstract: While automatic speech recognition (ASR) models have achieved remarkable improvements in recent years, performance disparities persist across different
arXiv:2608.30695v1 Announce Type: new Abstract: Real-world time series often exhibit irregular sampling and extended temporal horizons, requiring models to capture continuous-time dynamics across arbi
arXiv:2608.31073v1 Announce Type: new Abstract: Whole-heart segmentation (WHS) in computed tomography (CT) and magnetic resonance imaging (MRI) is affected by acquisition shifts and heterogeneous card
arXiv:2608.29357v1 Announce Type: new Abstract: Multimodal search agents answer visual questions by interleaving image understanding, web retrieval, tool use, and evidence synthesis. Strong systems ex
arXiv:2608.30235v1 Announce Type: new Abstract: Knowledge graph completion requires models to use both textual descriptions and relational structure. Existing LLM-based methods either encode KG struct
arXiv:2608.29517v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as essay graders in learning analytics, evaluated almost exclusively with agreement statistics. Educa
arXiv:2608.31016v1 Announce Type: cross Abstract: Ambient AI scribes draft clinical notes, and published audits find their dominant error is omission: information the encounter established that the no
arXiv:2608.31102v1 Announce Type: cross Abstract: Industrial post-training is a brownfield regime. Teams inherit a deployed checkpoint and must land targeted improvements under fixed compute and mixtu
arXiv:2608.29640v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise for spatio-temporal forecasting, but existing approaches often rely on regularly sampled token sequenc
arXiv:2601.06973v2 Announce Type: replace Abstract: As LLMs move from text completion toward autonomous agents, they remain constrained by the standard chat interface, which lacks private working memo
arXiv:2608.29612v1 Announce Type: new Abstract: Sustained scientific work requires a knowledge substrate that carries interpretation across tasks and preserves paths to source evidence. We call this p
arXiv:2510.09347v2 Announce Type: replace Abstract: Unlike Business-to-Consumer e-commerce platforms (e.g., Amazon), inexperienced individual sellers on Consumer-to-Consumer platforms (e.g., eBay) oft
arXiv:2608.29228v1 Announce Type: new Abstract: Failures in agentic AI systems can arise from interactions among messages exchanged by multiple large language model (LLM) agents. Pointwise attribution
arXiv:2608.30406v1 Announce Type: new Abstract: Goal-conditioned reinforcement learning struggles with long horizons when rewards are sparse. While a planner can provide subgoals to guide a low-level
arXiv:2608.30959v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) still struggle on tasks requiring complex visual understanding. We argue that the core issue is not high-level reasoning
arXiv:2601.07533v3 Announce Type: replace-cross Abstract: Tracing connections between historical texts is an important part of intertextual research, enabling scholars to reconstruct the virtual libra
arXiv:2608.29188v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) substantially improves single-sample accuracy (pass@1) but causes the policy's solution space to
arXiv:2608.29539v1 Announce Type: new Abstract: As context lengths scale, attention increasingly becomes a primary computational bottleneck in large language models. Standard Transformers remain power
arXiv:2608.28553v2 Announce Type: replace Abstract: Modern agent systems assemble capabilities at runtime, and this dynamic composition has recently received a complete formal treat ment in the spatio
arXiv:2608.27580v2 Announce Type: replace Abstract: Safety guardrails serve as the last line of defense against harmful inputs and outputs of large language models (LLMs), yet they are trained and eva
arXiv:2511.18931v2 Announce Type: replace-cross Abstract: Modern large language models increasingly integrate internal web-based retrieval to provide real-time answers, yet it remains unclear how effe