Knowledge catalogue
arXiv:2509.24250v3 Announce Type: replace Abstract: Teaching systems physical tasks is a long standing goal in HCI, yet most prior work has focused on non collaborative physical activities. Collaborat
arXiv:2603.11795v2 Announce Type: replace Abstract: Unsupervised Concept Extraction aims to extract concepts from a single image; however, existing methods suffer from the inability to extract composa
arXiv:2604.09309v1 Announce Type: cross Abstract: The Half-Trek Criterion (HTC) is the primary graphical tool for determining generic identifiability of causal effect coefficients in linear structural
Grizzly bears have made such a comeback across eastern Montana that in 2017, the state hired its first-ever prairie-based grizzly manager: wildlife biologist Wesley Sarmento. For some seven years, Sar
arXiv:2604.08607v1 Announce Type: cross Abstract: Precise interference detection and identification are crucial for enhancing the survivability of communication systems in non-cooperative wireless env
Just like us at @thinkymachines, @luke_drago_ @LRudL_ care a lot about amplifying humans, not replacing them. They're joining us to build towards that future! Workshop Labs is joining @thinkymachines.
arXiv:2604.08915v1 Announce Type: cross Abstract: Existing defect/anomaly generation methods often rely on few-shot learning, which overfits to specific defect categories due to the lack of large-scal
arXiv:2506.11552v2 Announce Type: replace-cross Abstract: Quantum error correction is crucial for protecting quantum information against decoherence. Traditional codes like the surface code require su
arXiv:2510.23636v3 Announce Type: replace-cross Abstract: Flight delay prediction has become a key focus in air traffic management (ATM), as delays reflect inefficiencies in the system. This paper pro
arXiv:2604.08752v1 Announce Type: cross Abstract: Relation extraction represents a fundamental component in the process of creating knowledge graphs, among other applications. Large language models (L
arXiv:2510.01767v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has established itself as an efficient representation for real-time, high-fidelity 3D scene reconstruction. However, sc
arXiv:2509.24164v2 Announce Type: replace Abstract: We investigate the mechanistic underpinnings of in-context learning (ICL) in large language models by reconciling two dominant perspectives: the com
arXiv:2604.08816v1 Announce Type: new Abstract: We present Loom, a computer architecture that executes programs compiled from C inside a looped transformer whose weights are derived analytically. The
This Reddit thread on r/MachineLearning discusses CVPR 2026's policy requiring accepted papers to be registered under an in-person author registration, with virtual attendance still permitted if circu
arXiv:2604.08943v1 Announce Type: new Abstract: Reconstructing high-fidelity 3D hands from egocentric monocular videos remains a challenge due to the limitations in capturing high-resolution geometry,
arXiv:2601.04791v3 Announce Type: replace Abstract: While latent diffusion models (LDMs) have emerged as powerful priors for inverse problems, existing LDM-based solvers frequently suffer from instabi
arXiv:2604.09289v1 Announce Type: new Abstract: We propose a hybrid physics-informed framework for solving families of parametric linear partial differential equations (PDEs) by combining a meta-learn
arXiv:2604.08971v1 Announce Type: new Abstract: Edge devices increasingly run multimodal sensing pipelines that must remain accurate despite fluctuating power budgets and unpredictable sensor dropout.
arXiv:2604.09421v1 Announce Type: cross Abstract: Just Recognizable Difference (JRD) boosts coding efficiency for machine vision through visibility threshold modeling, but is currently limited to a si
arXiv:2604.09062v1 Announce Type: new Abstract: Valid segmentation of the optic disc (OD) and optic cup (OC) from fundus photographs is essential for glaucoma screening. Unfortunately, existing deep l
This Japanese blog post explores whether signs and symbols can emerge within a single neural network, drawing on analysis of the 'Neural Computers' paper. The discussion likely examines the intersecti
arXiv:2604.02319v2 Announce Type: replace Abstract: When posed with prompts that permit a large number of valid answers, comprehensively generating them is the first step towards satisfying a wide ran
Ethan Mollick, a Wharton professor and prominent AI researcher, posted commentary on what appears to be a notable AI capability advancement, framing it as a predictable progression along established p
arXiv:2604.09406v1 Announce Type: new Abstract: Training large language models (LLMs) is constrained by memory requirements, with activations accounting for a substantial fraction of the total footpri
arXiv:2604.09423v1 Announce Type: new Abstract: Combinatorial multi-armed bandits provide a fundamental online decision-making environment where a decision-maker interacts with an environment across T
arXiv:2602.01997v2 Announce Type: replace-cross Abstract: Recent work has shown that layer pruning can effectively compress large language models (LLMs) while retaining strong performance on classific
arXiv:2604.09156v1 Announce Type: new Abstract: Parallel kinematics machines (PKM) can exhibit kinematic as well as actuation redundancy. While the meaning of kinematic redundancy has been clarified a
arXiv:2604.08787v1 Announce Type: new Abstract: This paper proposes a common interface for real-time low-level motion planning of collaborative robotic arms, aimed at enabling broader applicability an
arXiv:2604.08969v1 Announce Type: cross Abstract: This paper introduces a projected functional gradient descent algorithm (P-FGD) for training nonparametric additive quantile regression models in onli
arXiv:2604.08759v1 Announce Type: cross Abstract: This paper considers the problem of multi-bit generative watermarking for large language models under a worst-case false-alarm constraint. Prior work
arXiv:2512.03730v2 Announce Type: replace-cross Abstract: Adversarial perturbations are a useful way to expose vulnerabilities in object detectors. Existing perturbation methods are frequently white-b
arXiv:2604.08801v1 Announce Type: cross Abstract: Prompt optimization improves language models without updating their weights by searching for a better system prompt, but its effectiveness varies wide
arXiv:2508.01312v4 Announce Type: replace Abstract: We revisit the classical Perspective-Three-Point (P3P) problem, which aims to recover the absolute pose of a calibrated camera from three 2D-3D corr
arXiv:2506.17310v3 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) demonstrate strong performance across domains, their long-context capabilities are limited by transient neu
arXiv:2604.09058v1 Announce Type: cross Abstract: Long-horizon spatiotemporal prediction remains a challenging problem due to cumulative errors, noise amplification, and the lack of physical consisten
arXiv:2604.09434v1 Announce Type: cross Abstract: Turbulent boundary layers over aerodynamic surfaces are a major source of aircraft drag, yet their control remains challenging due to multiscale dynam
arXiv:2508.05091v2 Announce Type: replace Abstract: Generating temporally coherent, long-duration videos with precise control over subject identity and movement remains a fundamental challenge for con
arXiv:2604.08624v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are naturally suited for speech processing tasks due to their specific dynamics, which allows them to handle temporal d
arXiv:2511.20068v2 Announce Type: replace Abstract: Autoregressive (AR) image generation has recently emerged as a powerful paradigm for image synthesis. Leveraging the generation principle of large l
arXiv:2604.08649v1 Announce Type: cross Abstract: Modern financial systems generate vast quantities of transactional and event-level data that encode rich economic signals. This paper presents PRAGMA,
arXiv:2604.08941v1 Announce Type: new Abstract: Medical Vision Language Models VLMs suffer from two failure modes that threaten safe deployment mis calibrated confidence and sensitivity to question re
arXiv:2604.09123v1 Announce Type: new Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims to extract all sentiment triplets of aspect terms, opinion terms, and sentiment polarities from a senten
arXiv:2604.09111v1 Announce Type: cross Abstract: Recently, artificial intelligence-based dubbing technology has advanced, enabling automated dubbing (AD) to convert the source speech of a video into
arXiv:2604.08781v1 Announce Type: cross Abstract: Purpose: To develop and evaluate a deep learning (DL) method for free-breathing phase-sensitive inversion recovery (PSIR) late gadolinium enhancement
arXiv:2603.19929v2 Announce Type: replace-cross Abstract: RAM incorporates a motion-aware semantic tracker with adaptive Kalman filtering to achieve robust identity association under severe occlusions
arXiv:2604.08572v1 Announce Type: cross Abstract: State-of-the-art post-hoc out-of-distribution detection methods rely on intermediate layer activation editing. However, they exhibit inconsistent perf
arXiv:2604.09429v1 Announce Type: cross Abstract: Recovering camera parameters from images and rendering scenes from novel viewpoints have long been treated as separate tasks in computer vision and gr
arXiv:2601.07663v3 Announce Type: replace Abstract: Hint-based faithfulness evaluations have established that Large Reasoning Models (LRMs) may not say what they think: they do not always volunteer in
arXiv:2604.09494v1 Announce Type: cross Abstract: We propose RecaLLM, a set of reasoning language models post-trained to make effective use of long-context information. In-context retrieval, which ide
arXiv:2604.09019v1 Announce Type: cross Abstract: Two-hop QA retrieval splits queries into two regimes determined by whether the hop-2 entity is explicitly named in the question (Q-dominant) or only i
arXiv:2602.11354v2 Announce Type: replace Abstract: The literature has witnessed an emerging interest in AI agents for automated assessment of scientific papers. Existing benchmarks focus primarily on
arXiv:2604.08592v1 Announce Type: new Abstract: Reservoir observers provide a data-driven approach to the inference of unmeasured variables from observed ones for nonlinear dynamical systems. While pr
arXiv:2604.08880v1 Announce Type: cross Abstract: Chain-of-thought (CoT) distillation transfers reasoning behaviors from a strong teacher to a smaller student, but prior work reports a capacity gap: d
arXiv:2601.08588v4 Announce Type: replace-cross Abstract: This paper investigates symmetric composite binary quantum hypothesis testing (QHT), where the goal is to determine which of two uncertainty s
arXiv:2510.25704v2 Announce Type: replace-cross Abstract: We develop a methodology based on out-of-equilibrium simulations to mitigate topological freezing when approaching the continuum limit of latt
arXiv:2604.09104v1 Announce Type: cross Abstract: Scheming, the covert pursuit of misaligned goals by AI systems, represents a potentially catastrophic risk, yet scheming research suffers from signifi
arXiv:2601.22160v2 Announce Type: replace-cross Abstract: Human animation aims to generate temporally coherent and visually consistent videos over long sequences, yet modeling long-range dependencies
arXiv:2601.22990v2 Announce Type: replace-cross Abstract: Reconstructing 3D fetal MR volumes from motion-corrupted stacks of 2D slices is a crucial and challenging task. Conventional slice-to-volume r
arXiv:2604.09412v1 Announce Type: cross Abstract: We study the population loss landscape of two-layer ReLU networks of the form sum_{k=1}^K ReLU(w_k^op x) in a realisable teacher-student setting with
arXiv:2604.08760v1 Announce Type: new Abstract: Recent progress in text-to-3D object generation enables the synthesis of detailed geometry from text input by leveraging 2D diffusion models and differe