Knowledge catalogue
arXiv:2610.11759v1 Announce Type: cross Abstract: Near-term quantum approaches to combinatorial optimization are limited by qubit counts, circuit fidelity, sampling cost, and the difficulty of encodin
arXiv:2610.11610v1 Announce Type: cross Abstract: Generating ultrasound reports from multiple images requires aggregating clinical evidence across views, yet archived key frames capture only part of t
arXiv:2610.12421v1 Announce Type: new Abstract: Dense correspondence matching has historically been bounded by simplifying spatio-temporal priors, such as smooth motion and rigid geometry. While effec
arXiv:2610.11461v1 Announce Type: cross Abstract: Natural-language style descriptions provide an interpretable interface between large language models (LLMs) and controllable text-to-speech (TTS). How
arXiv:2610.12449v1 Announce Type: cross Abstract: Bifurcations are ubiquitous in physical systems, from structural buckling to fluid and climate dynamics, yet they remain largely unexplored in deep le
arXiv:2602.07535v2 Announce Type: replace-cross Abstract: Acute ischemic stroke requires rapid treatment decisions that are strongly guided by emergency imaging. Admission computed tomography perfusio
arXiv:2610.12370v1 Announce Type: new Abstract: Koopman operator theory provides a linear framework for analyzing nonlinear dynamical systems and has become a major tool for data-driven modeling. A ce
arXiv:2610.11430v1 Announce Type: new Abstract: While domain-specific Large Language Models (LLMs) have encoded vast biomedical knowledge, their limited context windows often hinder a deep understandi
arXiv:2610.11218v1 Announce Type: cross Abstract: Dense associative memory (DAM) provides an energy-based framework for memory retrieval with close connections to attention mechanisms in modern artifi
arXiv:2508.07106v2 Announce Type: replace Abstract: Functional Magnetic Resonance Imaging (fMRI) is an imaging technique widely used to study human brain activity. fMRI signals in areas across the bra
arXiv:2610.10742v1 Announce Type: cross Abstract: AI systems increasingly rely on Large Language Models (LLMs) as core reasoning engines, making them targets for prompt injection and jailbreaks. Guard
arXiv:2610.12307v1 Announce Type: new Abstract: Most image generation models rely on uniform tokenization, allocating the exact same computational budget to equally-sized image patches. This static pa
Bullish on this trend of making post-training more accessible. A new post-training era is upon us. If you work on agentic RL, long-context tasks (a big focus today) are expensive, inefficient, and don
arXiv:2610.11971v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies assume the embodiment on which they were trained and can fail when a joint fault changes how commanded actions a
arXiv:2610.12445v1 Announce Type: cross Abstract: Recent incidents have highlighted the challenge of monitoring LLM agents and the danger of models deceiving people. We show that white-box deception d
arXiv:2610.12016v1 Announce Type: new Abstract: World models for control must capture which aspects of the environment respond to the agent's actions and which are relevant to reward. Generative world
arXiv:2610.11300v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for automated code generation, but generated programs can appear syntactically plausible while stil
arXiv:2610.12361v1 Announce Type: new Abstract: Large language models increasingly justify legal decisions by naming the statute or precedent behind a verdict, treated as evidence that the decision fo
arXiv:2507.13835v2 Announce Type: replace-cross Abstract: The amount of quality data in many machine learning tasks is limited to what is available locally to data owners. The set of quality data can
arXiv:2610.10829v1 Announce Type: cross Abstract: Conformal prediction provides prediction sets with finite-sample guarantees, but the label verification required for calibration can be expensive. We
arXiv:2610.11621v1 Announce Type: new Abstract: Consensus can make pseudo-label learning more reliable, but only when its predictors contribute genuinely different evidence. Multiple models that repea
arXiv:2610.12107v1 Announce Type: new Abstract: Generalized referring expression segmentation (GRES) requires dynamically balancing high-level semantics for identifying a variable number of language-s
arXiv:2610.12431v1 Announce Type: new Abstract: Diffusion models can represent complex, multimodal trajectory distributions, but extracting uncertainty from them typically requires costly Monte Carlo
arXiv:2610.12316v1 Announce Type: new Abstract: Recent motion generative models have demonstrated strong capabilities in synthesizing physically plausible character motion, but often overlook establis
arXiv:2610.11394v1 Announce Type: cross Abstract: Multimodal Alzheimer's disease (AD) diagnosis benefits from integrating heterogeneous clinical, imaging, genomic, and biomarker evidence, but clinical
arXiv:2610.11740v1 Announce Type: new Abstract: Neural networks are typically trained using first-order methods and back-propagation. It is unclear whether this approach is optimal for morphological l
arXiv:2610.12106v1 Announce Type: cross Abstract: Watermarking large language models is popular for tracing chatbot and agentic outputs, yet detectors remain unreleased since exposing them could let a
arXiv:2610.11108v1 Announce Type: new Abstract: Multi-domain physics-informed neural networks (PINNs) flexibly model medium-specific representations to solve fluid--solid conjugate heat transfer (CHT)
arXiv:2610.10766v1 Announce Type: cross Abstract: Lack of effective authentication has resulted in numerous security and privacy breaches, including unauthorized access to protected information, ident
arXiv:2610.10846v1 Announce Type: new Abstract: The diversity of robot embodiments and action spaces makes it challenging to build robot world models that generalize across different embodiments. We i
arXiv:2610.11845v1 Announce Type: new Abstract: Spin in clinical trials includes reporting practices that distort the presentation of results. This is particularly critical in medicine, where spin is
arXiv:2610.10810v1 Announce Type: cross Abstract: Long-horizon robotic manipulation is often built by chaining independently trained skills. Although each skill can be reliable in isolation, performan
arXiv:2610.10550v1 Announce Type: new Abstract: Personalizing text-to-image diffusion models from a few reference images requires preserving subject identity while following prompts that describe new
arXiv:2610.11656v1 Announce Type: new Abstract: Facial attractiveness prediction usually assigns one overall rating, leaving the roles of shape, appearance, and viewing conditions implicit. We propose
arXiv:2610.11141v1 Announce Type: new Abstract: Relative localization is crucial for a multi-robot system to collaboratively perform tasks, such as exploration and formation. However, this is highly c
arXiv:2610.11706v1 Announce Type: new Abstract: Self-blended images are widely used to train face-swap detectors, but primarily capture blending artifacts. We investigate whether adding illumination i
arXiv:2610.10564v1 Announce Type: cross Abstract: Dynamic-point filters are routinely added to feature-based visual SLAM, and several recent systems argue that removing dynamic features can leave too
arXiv:2610.11776v1 Announce Type: new Abstract: Long-term personalization requires language models to use interaction history to track users' preferences across sessions. Parametric memory encodes thi
arXiv:2610.11284v1 Announce Type: cross Abstract: Diffusion-based LLMs (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs by enabling bidirectional parallel refinemen
arXiv:2610.12248v1 Announce Type: new Abstract: Wearable augmented reality (AR) assistants are moving toward continuous real-world interaction, where they perceive the user's activity through first-pe
arXiv:2610.10859v1 Announce Type: new Abstract: Text-to-image diffusion models are increasingly distilled into few-step variants and being deployed to enable fast inference. However, their ability to
arXiv:2610.11651v1 Announce Type: new Abstract: Automated interviewers and conversational agents are increasingly used in research, recruitment, customer service, and education. However, many existing
arXiv:2610.11344v1 Announce Type: new Abstract: Physics-based models connect scientific explanation with quantitative prediction. Constructing them requires selecting physical processes, defining stat
arXiv:2610.12022v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in sociotechnical systems where social attribution, the reasoning process attributing external
arXiv:2610.11418v1 Announce Type: new Abstract: Deploying frozen learned skills, such as Vision-Language-Action (VLA) policies, in new environments requires identifying initiation configurations that
arXiv:2610.10666v1 Announce Type: new Abstract: Understanding why deep neural networks make a given prediction is of great importance for their safe deployment. In computer vision, saliency maps, whic
arXiv:2601.12654v3 Announce Type: replace-cross Abstract: SHAP explanations are widely used in high-stakes settings to justify decisions, yet they can differ substantially across repeated runs, even w
arXiv:2610.11112v1 Announce Type: cross Abstract: Image-generation models can now produce text-rich, natural-looking visual artifacts that are hard to distinguish from real-world evidence, such as new
arXiv:2610.12427v1 Announce Type: cross Abstract: Streaming Video Large Language Models (VLMs) enable continuous video understanding, yet existing benchmarks focus on low-dynamic scenarios. Under boun
arXiv:2610.11412v1 Announce Type: new Abstract: We propose Fresco++, a unified optimization framework for fine-grained and view-consistent head avatar reconstruction. Head avatar optimization is typic
arXiv:2610.11472v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning often improves language-model performance by giving models additional computation before answering. However, explicit C
arXiv:2610.11818v1 Announce Type: cross Abstract: While massive, closed-source Vision-Language Models (VLMs) set strong benchmarks for document understanding, their dependence on commercial APIs limit
**Summary** TinyFish showcases a streamlined workflow for moving their web‑native machine‑learning model from post‑training into production using Databricks AI Runtime. The article details the deploym
arXiv:2610.12230v1 Announce Type: cross Abstract: Text prompting is an indirect interface for poster generation, requiring users to encode inherently two-dimensional composition intent into a one-dime
arXiv:2610.11770v1 Announce Type: new Abstract: AI-Native video creation is shifting from isolated video clips toward iterative video creation workflows. However, existing datasets remain largely vide
arXiv:2610.10897v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) are a widely used data-free method for solving Partial Differential Equations (PDEs) using machine learning. Wi
arXiv:2610.12411v1 Announce Type: new Abstract: Globally consistent, real-time state estimation in large-scale, perceptually degraded environments is essential for autonomous vehicles and aerial robot
arXiv:2610.10959v1 Announce Type: new Abstract: In recent years, persistent homology has seen rapid adoption in deep learning, yet its computation remains a major bottleneck in network training. This
arXiv:2610.11402v1 Announce Type: new Abstract: GoldenViewVQA requires models to jointly answer driving-scene questions and identify the camera view containing the supporting visual evidence, making p
arXiv:2610.11231v1 Announce Type: new Abstract: Small vision-language models may be able to read external evidence yet struggle to obtain it. We introduce Harness Compilation (HC), an offline procedur