Knowledge catalogue
arXiv:2509.14093v3 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting can substantially improve the reasoning ability of large language models (LLMs), but it often comes with high
arXiv:2610.09282v1 Announce Type: new Abstract: Common-image gathers (CIGs) contain physically meaningful information about velocity-model errors through reflector focusing and residual moveout, but i
arXiv:2609.33181v2 Announce Type: replace-cross Abstract: Recent advances in online policy self-distillation (OPSD) have demonstrated that large language models (LLMs) can improve their capabilities b
arXiv:2610.09886v1 Announce Type: cross Abstract: Bayesian neural networks need not be fully stochastic to be universal conditional density approximators, but it remains open which parameters should b
arXiv:2610.09418v1 Announce Type: new Abstract: Pointing-gesture visual grounding requires connecting hand geometry with the visual identity and extent of a referred object. A central challenge for co
arXiv:2610.09848v1 Announce Type: new Abstract: Inverse design in engineering often runs into a simple problem. Each labeled training sample must be produced through expensive simulation, so building
arXiv:2610.09514v1 Announce Type: new Abstract: Physical world modeling requires predicting how interactions change a scene, not merely generating coherent motion. We propose STRIKE, a framework that
arXiv:2610.09230v1 Announce Type: new Abstract: Partial identification (PI) entails estimating bounds on causal effects by encoding different assumptions on data generation as a constrained optimizati
arXiv:2610.09566v1 Announce Type: new Abstract: Imitation learning policies that integrate multiple sensory modalities are prone to overreliance on a dominant modality, such as vision, during training
Hey everyone, Jovan from UkisAI (Swift Qwen) here! For those who don't know us, UkisAI is a small lab making tiny frontier LLMs, tools and datasets (+doing it open-source!). I'm one of the guys runnin
arXiv:2610.09186v1 Announce Type: new Abstract: We argue that pattern recognition and step-by-step reasoning are two ends of a spectrum. A large language model (LLM) learns to reason step-by-step when
arXiv:2610.09343v1 Announce Type: new Abstract: Tiled 3D Gaussian Splatting rasterizers often use one scene-wide contribution cutoff for tile enumeration, although content differs in its sensitivity t
arXiv:2610.09858v1 Announce Type: new Abstract: Large language model agents tackle multi-step tasks by interleaving reasoning and tool calls with observations from the environment. Prior work has show
arXiv:2603.15614v2 Announce Type: replace Abstract: Recent video diffusion models achieve strong visual quality and temporal coherence, but still lack coordinated control over scene layout, customized
arXiv:2610.09785v1 Announce Type: new Abstract: World models can enable autonomous ultrasound scanning by predicting the outcomes of probe motions from local observations. Learning this action--observ
arXiv:2610.09703v1 Announce Type: cross Abstract: Token-Pruning accelerates Vision-Language Models by removing redundant visual tokens, yet its safety implications remain underexplored. In this work,
arXiv:2610.10261v1 Announce Type: cross Abstract: Context: Once defined a taxonomy of stages structuring Machine Learning (ML) pipelines (e.g. Data Preprocessing, Modeling...), extracting these stages
arXiv:2602.22647v3 Announce Type: replace-cross Abstract: Generative retrieval has emerged as a powerful paradigm for LLM-based recommendation. However, industrial recommender systems often benefit fr
arXiv:2604.01569v2 Announce Type: replace Abstract: Video multimodal large language models achieve strong results on existing benchmarks, but answer accuracy alone does not establish whether they can
arXiv:2610.08983v1 Announce Type: new Abstract: Generative inpainting of brain MRI volumes is essential for synthesizing healthy tissue in pathological regions, improving the accuracy and reliability
arXiv:2607.10826v2 Announce Type: replace-cross Abstract: Automated evaluation is essential for scaling generative 3D systems, where exhaustive human review is costly and slow. Yet the reliability of
arXiv:2610.07857v1 Announce Type: new Abstract: This study formulates individual route reproduction as a shortest-path problem over learned driver-specific latent link costs. The central idea is that,
arXiv:2610.07544v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to judge how well an advertisement matches a query, but the fairness of these judgments has received
arXiv:2610.07804v1 Announce Type: new Abstract: Prior Fitted Networks (PFNs) such as TabPFN now rival established statistical procedures across prediction and estimation tasks. A natural explanation i
arXiv:2610.08775v1 Announce Type: new Abstract: Large language models (LLMs) can solve many narrow tasks, but querying them separately for millions of related instances can be prohibitively expensive.
arXiv:2610.07829v1 Announce Type: new Abstract: Semantic sensing (SemS) acquires task-relevant information rather than reconstructing complete physical information. Existing SemS formulations typicall
arXiv:2610.08703v1 Announce Type: new Abstract: In K-12 mathematics tutoring, student-tutor dialogue provides rich evidence of learners' problem-solving processes and sources of difficulty. Learning a
arXiv:2610.07271v1 Announce Type: new Abstract: Linkage algorithms for hierarchical clustering (HC) are a powerful and efficient framework for constructing clustering trees, yet it is often unclear wh
arXiv:2610.07029v1 Announce Type: cross Abstract: Resistive random-access memory (ReRAM)-based Processing-in-Memory (PIM) accelerator is a promising platform for processing massively memory intensive
arXiv:2610.08118v1 Announce Type: new Abstract: Covariate-aware time-series foundation models (TSFMs) promise training-free what-if answers for instrumented plants: the change in output that a differe
arXiv:2610.06927v1 Announce Type: cross Abstract: The key-value (KV) cache of autoregressive transformers grows linearly with context length and dominates memory at long context. Most training-free re
arXiv:2610.06972v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in visual understanding and multimodal reasoning, yet they remain fun
arXiv:2610.07019v1 Announce Type: new Abstract: Fiorillo v0.5 is an open model that answers typed questions with a probability for each answer. Its main specialist reads a randomized trial's article,
arXiv:2610.07208v1 Announce Type: new Abstract: Predicting migration flows remains a significant challenge for traditional gravity-based forecasting models, which primarily rely on structured socio-ec
arXiv:2610.07997v1 Announce Type: new Abstract: Molecular encoders contrastively pretrained on paired molecule-morphology data, such as CLOOME and CellCLIP, have been proposed as cheap surrogates for
arXiv:2610.07843v1 Announce Type: new Abstract: In digital pathology, compact evidence is often used to explain or audit predictions made by whole-slide image multiple instance learning models. In hie
arXiv:2610.08592v1 Announce Type: new Abstract: CNet is a C++/CUDA framework for building and training deep complex-valued neural networks (CVNNs) and, more generally, for optimizing complex-valued fu
arXiv:2610.08717v1 Announce Type: new Abstract: We study path-flow alignment as a unified training objective for flow matching. Instead of fixing the interpolation path and learning only the velocity
arXiv:2610.07808v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer sparse and event-driven computation, making them attractive for energy-constrained reinforcement learning (RL) on
arXiv:2610.07469v1 Announce Type: new Abstract: Explicitly eliciting reasoning substantially improves LLM performance. Existing approaches require a predefined characterization of reasoning, whether t
arXiv:2610.07628v1 Announce Type: new Abstract: Out-of-distribution (OOD) generalization remains challenging for graph neural networks (GNNs), as graph distributions can vary substantially across time
arXiv:2610.07948v1 Announce Type: new Abstract: When using an LLM agent in a consequential domain, making an informed decision about whether to trust its output or intervene requires calibrated confid
arXiv:2610.08048v1 Announce Type: new Abstract: LLM agents often lack the operational knowledge to act reliably in new environments, as they must discover specific tool behaviors or environment conven
arXiv:2610.05828v2 Announce Type: replace Abstract: Large language models (LLMs) offer new opportunities for public opinion research by enabling early prediction of survey responses, potentially reduc
arXiv:2610.07048v1 Announce Type: cross Abstract: We study errors-in-variables problems in which a target study contains only a single error-prone surrogate of an unobserved exposure, while an externa
arXiv:2610.07444v1 Announce Type: cross Abstract: A GUI agent decides which action to take and where to take it; we ask how a small grounding model should receive the action type. Fine-tuning Qwen2-VL
arXiv:2610.07489v1 Announce Type: cross Abstract: Increasing connectivity to the outside world and the lack of inbuilt security mechanisms have made legacy intra-vehicular networks vulnerable to cyber
arXiv:2610.08129v1 Announce Type: new Abstract: Cooperation with unfamiliar partners requires adapting to communication conventions that are not known in advance. We study this problem in a controlled
arXiv:2610.07270v1 Announce Type: new Abstract: Internal features in LLMs are often interpreted as mechanisms when they track a concept and their manipulation changes a related behavior. Yet steering
arXiv:2610.07362v1 Announce Type: new Abstract: We evaluate large language models (LLMs) in multi-turn interactions through their time-to-event: the number of interaction steps required to produce an
arXiv:2610.07460v1 Announce Type: cross Abstract: Inserting objects into existing 3D scenes requires more than selecting a plausible location: the inserted object must also fit local geometry while pr
arXiv:2610.05370v2 Announce Type: replace Abstract: Generative reward models (GRMs) are important for LLM optimization. Unlike scalar reward models, GRMs generate natural-language critiques alongside
arXiv:2610.08108v1 Announce Type: new Abstract: Diffusion language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) language models, offering flexible token-update orders
arXiv:2610.07696v1 Announce Type: new Abstract: Generative action models based on diffusion and flow matching have been increasingly adopted in vision-language-action (VLA) policies for their ability
arXiv:2610.07669v1 Announce Type: cross Abstract: We assessed the value of two live human-AI interactions in a precision disease control project in California vineyards. The project tested whether 202
arXiv:2610.06973v1 Announce Type: new Abstract: Melt-pool monitoring is central to qualifying metal additive manufacturing (AM), yet no public event-camera benchmark exists for this domain. Event came
arXiv:2610.08367v1 Announce Type: new Abstract: Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates at low computational cost. High validity al
On October 7, 2026 OpenAI released a session titled “Explore ideas through interactive visuals that respond as you learn, try, and discover.” The webcast showcased real‑time visual tools that adapt to
arXiv:2311.18029v2 Announce Type: replace-cross Abstract: The current trend in the literature on Time Series Classification is to develop increasingly accurate algorithms by combining multiple models
arXiv:2610.08538v1 Announce Type: cross Abstract: Probabilistic load forecasting has been widely studied for power-system operation and planning, but customer- and transformer-level forecasting introd