Knowledge catalogue
arXiv:2610.19121v1 Announce Type: cross Abstract: Structured pruning reduces LLM inference costs by removing model components to produce smaller dense models. However, existing methods can cause reaso
arXiv:2610.15231v1 Announce Type: new Abstract: Generative artificial intelligence (GenAI) models, particularly large language models (LLMs), are increasingly used in educational settings for tasks su
arXiv:2610.20516v1 Announce Type: new Abstract: On-policy self-distillation provides dense supervision for reasoning models via a privileged self-teacher. However, this supervision reflects local toke
arXiv:2512.23044v3 Announce Type: replace Abstract: Autonomous agents are shifting information seeking from passive retrieval toward proactive, open-ended research, yet this progress has largely bypas
arXiv:2610.19731v1 Announce Type: new Abstract: Goal misgeneralization is a common failure mode in which an agent behaves competently out of distribution but does not pursue the intended goal. However
arXiv:2610.17365v1 Announce Type: new Abstract: As LLM agents take on more autonomous roles, they may interact with multiple clients and counterparties whose goals and incentives conflict. If an agent
arXiv:2610.15459v1 Announce Type: cross Abstract: LLM-based multi-agent systems (LLM-MAS) use specialized agents that exchange messages to accomplish complex tasks. Their reliance on networked protoco
arXiv:2610.10226v2 Announce Type: replace-cross Abstract: Can AI make Software Engineering (SE) -- the discipline -- obsolete? And can it make software engineers -- the professionals -- redundant? Thi
arXiv:2610.15998v1 Announce Type: new Abstract: DNA methylation is a critical epigenetic modification that regulates gene expression and genome stability, primarily catalyzed by the DNA methyltransfer
arXiv:2610.15862v1 Announce Type: new Abstract: Elastic buckling remains a critical concern in the design of deep spherical shell caps, given that experiments reveal a significant reduction of critica
arXiv:2610.17495v1 Announce Type: new Abstract: In humans, proprioception corresponds to the sense of self-movement or position, e.g. understanding the position of one's arm. This sense is often less
arXiv:2610.15343v1 Announce Type: new Abstract: World models and video understanding models are increasingly deployed in dynamic visual environments, yet their robustness to physical adversarial threa
arXiv:2610.18305v1 Announce Type: new Abstract: The world encompasses both physical environments and human actors. Predicting its evolution requires understanding both physical laws and how emotions s
arXiv:2610.14238v1 Announce Type: cross Abstract: LLM data engineering requires iterative experimentation to determine how data selection, rewards, and curricula affect model learning, making it a nat
arXiv:2610.17881v1 Announce Type: new Abstract: Can an AI agent turn high-level user intent into a functioning robot and teach it how to behave? We study this question from two complementary aspects:
arXiv:2610.17970v1 Announce Type: cross Abstract: Long-running agents retain KV caches across turns and sessions for hours or days. Compressing these persistent caches requires reducing storage while
arXiv:2512.08659v2 Announce Type: replace-cross Abstract: Clinical communication is central to patient outcomes, yet large-scale human annotation of patient-provider conversation remains labor-intensi
arXiv:2610.15328v1 Announce Type: new Abstract: We introduce Anchor-and-Harvest Curriculum (AHC), a domain-informed, rule-based training system for Craftax-Full. AHC combines achievement-oriented task
arXiv:2610.16324v1 Announce Type: new Abstract: Training-free language-based video anomaly detection (VAD) uses frozen vision--language models (VLMs) and large language models (LLMs) to localize abnor
Are legions of smart devices - fridges, ovens, vacuum cleaners, lawnmowers, security cameras, and TVs - conspiring to take over the internet? No. But they could soon be unwitting hosts to self-replica
arXiv:2607.19198v2 Announce Type: replace-cross Abstract: Amorphous materials exhibit exceptional mechanical and functional properties, yet their rugged energy landscapes are notoriously difficult to
arXiv:2610.14063v1 Announce Type: new Abstract: LLM program-search agents are usually judged by the score that drives their search. We study LLM-driven search over Python exercise policies for Bermuda
arXiv:2610.16232v1 Announce Type: new Abstract: In-silico optimization of nucleic acids relies on predictive oracles that imperfectly capture biological function, so sequences optimized against one mo
arXiv:2610.14155v1 Announce Type: cross Abstract: Reliable manipulation requires policies that generalize across tasks, layouts, and object interactions. Vision-language-action (VLA) models combine pr
arXiv:2610.14017v1 Announce Type: cross Abstract: We propose Barycentric Anchored Search, a post-hoc weight-space procedure for improving fine-tuned vision models under distribution shift without addi
arXiv:2610.15818v1 Announce Type: new Abstract: Modern cloud platforms generate high-dimensional, noisy telemetry from thousands of microservices. Unlike classical anomaly detection benchmarks, cloud
arXiv:2610.18161v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated strong zero-shot reasoning across diverse domains, but their potential for Human-Object Inter
Benchmarks are becoming the core competency of any company. Our company mission is to build the world’s best models for document processing. This also means that we have to build the world’s best benc
arXiv:2610.16074v1 Announce Type: cross Abstract: Co-circulating influenza, COVID-19, and respiratory syncytial virus (RSV) create a shared hospital-admission burden, because beds, staff, testing capa
arXiv:2610.18543v1 Announce Type: new Abstract: Foundation models are increasingly used as feature extractors for downstream tasks with limited data, typically through representations obtained from th
arXiv:2610.18399v1 Announce Type: new Abstract: Visual token pruning for multimodal large language models (MLLMs) appears highly effective, with existing methods retaining over 95% of unpruned perform
arXiv:2409.17397v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly being integrated into various medical fields, including mental health support systems. However,
arXiv:2610.17509v1 Announce Type: cross Abstract: Active learning changes the representations used to acquire subsequent labels. We study this interaction through Collapse-Based Active Neural Learning
arXiv:2610.14647v1 Announce Type: new Abstract: Synthetic UAV imagery can expand pre-deployment test evidence when task-specific Real data are scarce, but visual plausibility or semantic label alignme
arXiv:2610.16813v1 Announce Type: new Abstract: Vision foundation models (VFMs) provide transferable visual representations, but their inference cost limits deployment in many settings. Fine-tuning sp
arXiv:2610.14856v1 Announce Type: new Abstract: This paper studies the problem of recovering motion from monocular video, even when the target skeleton topology differs from the subject observed in th
arXiv:2610.14564v1 Announce Type: cross Abstract: Model-based reinforcement learning has shown superior sample efficiency by leveraging the learned latent dynamics either in the form of derivative-fre
arXiv:2506.17585v4 Announce Type: replace-cross Abstract: Large language models can answer questions correctly and still hallucinate their citations. Most citation systems address this by retrieving d
arXiv:2610.14395v1 Announce Type: new Abstract: Multimodal prediction models add a data source, such as imaging or an assay, to clinical information, and health systems must decide whether that added
arXiv:2610.14413v1 Announce Type: new Abstract: Computational coarse-graining aims to replace a sequence of updates with a cheaper module while preserving task performance. We present a continuation-r
arXiv:2610.16587v1 Announce Type: new Abstract: Financial candlestick understanding requires locating visual elements, aligning them across trading dates and panels, and combining observations through
arXiv:2610.14006v1 Announce Type: new Abstract: Since compact, interpretable classifiers correspond to simple programs, it is natural to ask if LLM-based coding agents can be used to learn interpretab
arXiv:2610.18696v1 Announce Type: new Abstract: Particle picking in cryo-electron microscopy is commonly formulated as an object-detection problem in which individual particles are represented by boun
arXiv:2610.18556v1 Announce Type: new Abstract: Deep learning for medical image restoration is often constrained by the need for paired training data. Acquiring perfectly registered clean-noisy radiog
arXiv:2610.15932v1 Announce Type: new Abstract: Computing equilibria in high-dimensional continuous games requires learning and representing rich, multi-modal strategy distributions, something convent
arXiv:2610.17576v1 Announce Type: cross Abstract: Answering questions from radiology reports involves two decisions: whether the report contains an answer and which span gives it. A false answer prese
arXiv:2610.14112v1 Announce Type: cross Abstract: Calibrated typed-decision ('System One') models return a label and a probability in a single forward pass at a small fraction of the cost of large lan
arXiv:2610.15696v1 Announce Type: new Abstract: We present DRanker (Disney+ Ranker), a transformer-based multitask ranking model serving personalized recommendations on the Disney+ homepage. We descri
arXiv:2610.16866v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training with generative reward models (GenRM) has become a central recipe for improving large language models (LLMs),
arXiv:2511.20088v3 Announce Type: replace Abstract: In recent years, VAD (Visual Anomaly Detection) has attracted significant attention due to its high interpretability, as it provides visual explanat
arXiv:2610.16329v1 Announce Type: new Abstract: Accurate breast cancer risk prediction from screening mammography is essential for improved risk stratification and personalized screening. It can benef
arXiv:2511.07923v3 Announce Type: replace Abstract: Accurate segmentation of marine organisms is vital for biodiversity monitoring and ecological assessment, yet existing datasets and models remain la
arXiv:2610.18650v1 Announce Type: new Abstract: Vision-language-action (VLA) policies perform manipulation tasks using camera images and language instructions, but object slip or visual occlusion can
arXiv:2610.16744v1 Announce Type: new Abstract: Video large language models (Video LLMs) encode each frame into hundreds of visual tokens, making video inference expensive. Existing methods reduce thi
arXiv:2610.18023v1 Announce Type: new Abstract: Graph fraud explanation generation emerges as an important task, which requires jointly leveraging graph structure and content information to produce hu
arXiv:2610.17859v1 Announce Type: new Abstract: Thermal-to-visible (T2V) face translation has important applications in nighttime biometric recognition, low-light surveillance, and human interpretatio
arXiv:2610.15390v1 Announce Type: cross Abstract: Audio Large Language Models (Audio LLMs) are embedded in agents for smart homes, vehicles, wearables, and embodied systems, where acoustic inputs can
arXiv:2610.17630v1 Announce Type: cross Abstract: In outdoor environments, varying terrain and contact conditions can cause identical action commands to produce different motion outcomes for quadruped
arXiv:2610.14622v1 Announce Type: new Abstract: Extreme rainfall can rapidly degrade road-network functionality (RNF), disrupting emergency mobility, accessibility, evacuation, logistics, and infrastr
arXiv:2610.16236v1 Announce Type: new Abstract: Morphogenesis is driven by the interplay of genetic regulation and tissue mechanics. In vivo video data is limited, particularly under genetic mutations