Knowledge catalogue
arXiv:2609.14717v1 Announce Type: cross Abstract: Speculative decoding accelerates LLM inference by verifying multiple drafted tokens in parallel, allowing a single target forward pass to accept sever
arXiv:2609.13876v1 Announce Type: new Abstract: Personal robots should adapt their co-speech gesture style to a new user without requiring model retraining. We present a training-free personalization
arXiv:2609.14339v1 Announce Type: new Abstract: Decentralized training enables large-model training over low-end GPUs and internet-grade connections, but communication along both data-parallel and pip
arXiv:2601.13885v2 Announce Type: replace-cross Abstract: Computerized Adaptive Testing (CAT) has proven effective for efficient LLM evaluation on multiple-choice benchmarks, but modern LLM evaluation
arXiv:2609.14226v1 Announce Type: new Abstract: Automated radiology report generation has advanced rapidly in diagnostic accuracy, yet generated reports frequently diverge from the stylistic conventio
arXiv:2609.13842v1 Announce Type: cross Abstract: Recent advances in speech synthesis and voice conversion have made deepfake speech increasingly realistic, making generalization to unseen spoofing at
arXiv:2609.15485v1 Announce Type: new Abstract: Avalanche forecasting requires knowledge of snowpack conditions and recent avalanche activity, but field observations are sparse across large mountain r
arXiv:2609.14193v1 Announce Type: cross Abstract: On-policy distillation (OPD) has become a standard component of frontier post-training pipelines, yet how much its training data actually contributes
arXiv:2606.22906v2 Announce Type: replace-cross Abstract: Large language models have shown strong performance on software engineering (SE) tasks, yet understanding large industrial repositories remain
arXiv:2609.15744v1 Announce Type: new Abstract: Advancements in data fusion and real-time analytics technologies have opened new avenues for addressing complex domain challenges. Financial risk early
arXiv:2609.14312v1 Announce Type: new Abstract: Multi-shell diffusion MRI (dMRI) unlocks more expressive microstructural modeling than single-shell scans, yet its longer acquisition time hinders deplo
arXiv:2609.13636v1 Announce Type: cross Abstract: Privacy-preserving inference via Torus Fully Homomorphic Encryption (TFHE) provides strong protection for sensitive data in outsourced deep learning a
arXiv:2609.15161v1 Announce Type: cross Abstract: Large language model (LLM) driven multi-agent systems have shown promise in complex clinical reasoning, yet existing approaches rely on static strateg
arXiv:2609.15521v1 Announce Type: new Abstract: Federated learning enables multiple parties to train a shared model without centralizing raw data with the help of an aggregator, but introduces integri
arXiv:2609.13250v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) cannot process every frame of a long video because of limitations in visual-token and computational budgets.
arXiv:2609.14539v1 Announce Type: new Abstract: This work proposes a model-based fault detection and diagnosis framework for underwater vehicles subject to actuator faults that explicitly accounts for
arXiv:2609.15015v1 Announce Type: new Abstract: Synthetic perturbations appear to offer inexpensive calibration data for LLM evaluators in biomedical ML, where expert review is scarce. Yet a planted m
arXiv:2609.13617v1 Announce Type: new Abstract: We evaluate whether the improvements advertised for ChatGPT Images 2.5 translate into better performance on forgery tasks with predetermined answers. We
arXiv:2512.15971v2 Announce Type: replace Abstract: Multispectral object detection is critical for safety-sensitive applications such as autonomous driving and surveillance, where robust perception un
arXiv:2609.13406v1 Announce Type: new Abstract: When we speak of recursive self-improvement (RSI), are we speaking of a phenomenon, a mechanism, or a prospect? Towards autonomous and evolving intellig
arXiv:2609.15094v1 Announce Type: cross Abstract: In industrial recommendation feeds, presenting a static headline for an item often fails to satisfy the diverse, multimodal interests of the user popu
arXiv:2609.13875v1 Announce Type: cross Abstract: We introduce cvxgenrust, an open-source tool for generating custom Rust code that solves families of parameterized convex optimization problems modele
arXiv:2609.13812v1 Announce Type: new Abstract: We present GeomVLA, a Vision-Language-Action (VLA) model that unifies perception, latent scene motion prediction, and action generation within a shared
arXiv:2609.14735v1 Announce Type: cross Abstract: Deep Learning (DL)-based channel estimation has shown high accuracy and low latency in terrestrial 5G NR, but Low Earth Orbit (LEO) Non-Terrestrial Ne
arXiv:2609.15471v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a central approach for improving mathematical reasoning in language models, but long-fo
arXiv:2602.07273v2 Announce Type: replace Abstract: Immersive applications such as virtual and augmented reality impose stringent requirements on frame rate, latency, and synchronization between physi
arXiv:2609.13975v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for code generation but can produce fluent yet functionally incorrect outputs, which limits trust i
arXiv:2609.15713v1 Announce Type: new Abstract: Recent approaches to 30-day hospital readmission prediction rely on pre-trained language models applied to discharge summaries. Although these methods a
arXiv:2609.13235v1 Announce Type: cross Abstract: Networked manipulation endpoints couple perception to actuation across compute- and bandwidth-limited links, yet commonly exchange dense geometric sta
arXiv:2609.13954v1 Announce Type: new Abstract: Ensemble sampling offers a practical approach to randomized exploration by maintaining a collection of models, but how small an ensemble can be while re
arXiv:2609.13805v1 Announce Type: new Abstract: In the era of the Internet of Things (IoT), coordinating connected electric vehicle (EV) charging scheduling to balance EV charging satisfaction, statio
arXiv:2502.15007v2 Announce Type: replace-cross Abstract: We introduce methods to quantify how Large Language Models (LLMs) encode and store contextual information, revealing that tokens often seen as
arXiv:2609.14849v1 Announce Type: cross Abstract: We characterize how people are turning to LLMs as oracles: all-knowing authorities on subjective personal questions. Motivated by risks to users' auto
arXiv:2609.15859v1 Announce Type: new Abstract: Extracting informative representations from longitudinal data that can predict future outcomes remains a critical challenge in medicine. Medical dataset
arXiv:2609.13585v1 Announce Type: cross Abstract: Communication has become a bottleneck in distributed training and inference of large models. Overlapping communication with computation at the granula
arXiv:2609.15148v1 Announce Type: new Abstract: Transfer learning is an effective technique for addressing data scarcity in deep learning for time series classification, but its success depends on the
arXiv:2609.15800v1 Announce Type: new Abstract: Visual Retrieval-Augmented Generation (VRAG) empowers models to navigate and answer queries about visually rich documents by retrieving relevant page im
arXiv:2609.14906v1 Announce Type: cross Abstract: The Hohenberg-Kohn theorem establishes that, in principle, the ground state (GS) charge density contains all GS information of a many-electron system,
arXiv:2609.14448v1 Announce Type: cross Abstract: Hallucination in large language models reduces their reliability and slows adoption. Various white-box studies have used internal representations to d
arXiv:2512.16565v2 Announce Type: replace-cross Abstract: Reinforcement learning has gained attention for modern Large Language Model post-training. The actor-only variants of Proximal Policy Optimiza
Nuance Labs, a Seattle-based startup that’s trying to make artificial intelligence models better at face-to-face conversations, said today it has closed on a 50 million early-stage funding round to ma
arXiv:2609.14734v1 Announce Type: cross Abstract: Federated learning enables collaborative model development without centralizing patient data, but annotation reliability at participating institutions
arXiv:2609.15128v1 Announce Type: new Abstract: Streaming omni-modal models must decide what and when to answer from the video chunks and synchronized audio observed so far. Visual cues often support
arXiv:2603.28156v2 Announce Type: replace Abstract: While robot action planning based on large language models (LLMs) has advanced remarkably, continuing a task after a physical action failure during
arXiv:2609.13477v1 Announce Type: new Abstract: Predictive Process Monitoring (PPM) forecasts how ongoing organizational processes unfold, enabling information systems to move beyond execution support
arXiv:2609.14591v1 Announce Type: new Abstract: Participation in federated learning (FL) comes at a cost. Clients trade off privacy, communication, and compute costs for potentially greater gains in m
arXiv:2609.13246v1 Announce Type: new Abstract: Plane segmentation from a single RGB image remains challenging due to imprecise region grouping and geometrically inconsistent supervision, often leadin
arXiv:2609.13523v1 Announce Type: cross Abstract: We develop a conceptual and operational account of Positive Topology starting from a basic relation between points or models and observable properties
I am a beginner, Took a while to get started, get everything right. This setup is native not container. Still not sure if I did this right, or if I can tune this more. Environment=HF_HUB_OFFLINE=1 Env
arXiv:2609.14036v1 Announce Type: cross Abstract: Fresh fruits and vegetables (FFVs) are highly perishable, and cold-chain breaks contribute significantly to global food waste. While Machine Learning
arXiv:2603.26788v3 Announce Type: replace-cross Abstract: Zero-shot object navigation requires agents to locate unseen targets in unfamiliar environments without prior maps or task-specific training.
arXiv:2609.14485v1 Announce Type: new Abstract: Retrieval-Guided Fine-Tuning (RAG-FT) incorporates retrieved data directly into the training objective, but the statistical consequences of noisy retrie
arXiv:2609.13389v1 Announce Type: new Abstract: Mapping textual specifications into formal representations is essential for ensuring the correctness of protocol designs and implementations. LLM-genera
arXiv:2609.15488v1 Announce Type: new Abstract: Machine learning models process large amounts of data, and Principal Component Analysis (PCA) is a widely used technique to reduce the dimensionality of
arXiv:2609.14422v1 Announce Type: new Abstract: Agentic Network Operations (NetOps) are an emerging paradigm promising to enable workload-aware, self-adjustable, and reliable autonomous networks. Whil
arXiv:2410.14083v2 Announce Type: replace Abstract: This paper describes a new spatial correspondence representation based on paired regions-of-interest (ROIs), for medical image registration. The dis
arXiv:2609.14015v1 Announce Type: new Abstract: Schizophrenia is a serious psychiatric disorder that affects millions of people worldwide, and its diagnosis remains primarily dependent on clinical ass
arXiv:2609.15065v1 Announce Type: new Abstract: Autonomous agents operating on multisensory data cannot assume that all sensory modalities remain consistently informative. In real environments, sensor
arXiv:2609.13966v1 Announce Type: cross Abstract: Video highlight detection aims to identify temporally important segments that capture the most informative or engaging events in a video. Reliable pre
arXiv:2609.14530v1 Announce Type: cross Abstract: Development and evaluation of computational pathology methods require access to large and diverse datasets. Over the past decade, various initiatives