Knowledge catalogue
A lot of people are being deliberately obtuse about this tweet. Would it help if I said 'theory' instead of 'idea'? Because it would still mean the exact same. FYI reading comprehension is about inter
arXiv:2605.08072v1 Announce Type: cross Abstract: L_1-Approximating polynomials, i.e., polynomials that approximate indicator functions in L_1-norm under certain distributions, are widely used in comp
arXiv:2605.07596v1 Announce Type: cross Abstract: Contrastive Representation Learning (CRL) has achieved strong empirical success in multiple machine learning disciplines, yet its theoretical sample c
arXiv:2503.12285v2 Announce Type: replace-cross Abstract: We study bi-criteria combinatorial optimization under noisy function evaluations. While resilience and black-box offline-to-online reductions
arXiv:2605.06821v1 Announce Type: cross Abstract: Cohen et al. (arXiv:2207.14484) observed that adaptive gradient methods such as Adam operate at the edge of stability. While there has been significan
arXiv:2601.16736v5 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time novel view synthesis. As an explicit representation optimized through
arXiv:2605.06819v1 Announce Type: new Abstract: Autoregressive generation lies at the heart of the mechanism of large language models. It can be viewed as the repeated application of a next-token gene
arXiv:2605.07466v1 Announce Type: new Abstract: Non-alcoholic fatty pancreas disease (NAFPD) is an underdiagnosed condition associated with metabolic syndrome, insulin resistance, and increased risk o
arXiv:2605.06829v1 Announce Type: cross Abstract: We survey continuous-time generative modeling methods based on transporting a simple reference distribution to a data distribution via stochastic or d
arXiv:2605.06678v1 Announce Type: new Abstract: According to the United Nations Office for Disaster Risk Reduction (2025), the average annual cost of natural catastrophes increased from 70--80 billion
arXiv:2605.08011v1 Announce Type: new Abstract: Recent efforts to improve the reasoning abilities of Large Language Models (LLMs) have focused on integrating formal logic solvers within neurosymbolic
arXiv:2605.07444v1 Announce Type: cross Abstract: The simulation of fluid flows is computationally expensive due to the complexity of its governing partial differential equations. Machine learning mod
arXiv:2605.08020v1 Announce Type: new Abstract: We present an active embodiment identification method for legged robots that jointly learns information-seeking behavior and explicit embodiment predict
arXiv:2602.13357v2 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) achieve state-of-the-art performance in high-fidelity image and video generation but suffer from expensive infer
arXiv:2604.02525v2 Announce Type: replace Abstract: Hadamard transforms have become a key tool for stabilizing low-precision training, but existing methods apply them uniformly across tensors and comp
arXiv:2605.07002v1 Announce Type: new Abstract: A major bottleneck in characterizing the failure modes of generative AI systems is the cost and time of annotation and evaluation. Consequently, adaptiv
arXiv:2605.08028v1 Announce Type: new Abstract: Traffic state estimation from sparse fixed sensors is challenging because physics-informed neural networks (PINNs) tend to over-smooth the shockwaves ad
arXiv:2605.07137v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a highly effective method for improving the reasoning abilities of Large Language Mod
arXiv:2605.07121v1 Announce Type: new Abstract: Temporal knowledge graphs (TKGs) represent time-stamped relational facts and support a wide range of reasoning tasks over evolving events. However, exis
arXiv:2605.06876v1 Announce Type: new Abstract: Adaptive density control in 3D Gaussian Splatting (3DGS) repeatedly grows the Gaussian population through fixed-cardinality random splitting to discover
arXiv:2605.06912v1 Announce Type: new Abstract: The proliferation of generative video technologies has intensified the need for reliable methods to detect and characterize synthetic media. To address
arXiv:2605.07885v1 Announce Type: new Abstract: The robustness of event cameras to high dynamic range and motion blur holds the potential to improve visual odometry systems in challenging environments
arXiv:2605.06218v2 Announce Type: replace Abstract: Piecewise affine neural networks (PANNs) provide a principled geometric perspective on neural network expressivity by characterizing the input--outp
arXiv:2605.07142v1 Announce Type: new Abstract: Accurate 3D brain MRI subtype classification benefits from both localized anatomical cues and long-range contextual reasoning. We present AGA3DNet, a re
arXiv:2605.07963v1 Announce Type: new Abstract: Aggregating conformal predictors is a standard way of balancing their predictive and computational efficiency while retaining their validity, at least a
arXiv:2605.06965v1 Announce Type: cross Abstract: As language-based AI systems become more anthropomorphic, the question of whether they can have subjective experience is increasingly pressing. I focu
arXiv:2605.07003v1 Announce Type: new Abstract: The interaction of robots with bendable objects in midair presents significant challenges in control, often resulting in performance degradation and pot
arXiv:2605.07104v1 Announce Type: new Abstract: Establishing almost sure convergence rates for stochastic approximation and reinforcement learning under Markovian noise is a fundamental theoretical ch
arXiv:2605.07317v1 Announce Type: cross Abstract: Vision Transformers (ViTs) achieve strong performance across vision tasks, yet their deployment with low-precision early exiting remains fragile. Exis
arXiv:2504.12922v3 Announce Type: replace-cross Abstract: We provide a general theorem on the asymptotic behavior of stochastic processes that conform to a relaxed supermartingale condition. The disti
arXiv:2605.07653v1 Announce Type: new Abstract: Underwater environments impose severe constraints on conventional imaging systems and demand solutions that balance high-quality sensing with strict res
arXiv:2605.07204v1 Announce Type: new Abstract: We introduce Arrow, a foundation model for zero-shot causal discovery on observational tabular data. Arrow factorizes a directed acyclic graph into an u
arXiv:2605.07964v1 Announce Type: cross Abstract: Confidence sequences based on test martingales provide time-uniform uncertainty quantification for the mean of bounded IID observations without parame
arXiv:2605.07192v1 Announce Type: new Abstract: 3D reconstruction methods such as 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) achieve impressive photorealism but fail when input ima
arXiv:2603.18636v2 Announce Type: replace Abstract: Diffusion Transformers (DiTs) achieve strong video generation quality but suffer from high inference cost due to dense 3D attention, motivating spar
arXiv:2510.00436v2 Announce Type: replace Abstract: Automated approaches to answer patient-posed health questions are rising, but selecting among systems requires reliable evaluation. The current gold
arXiv:2604.01619v3 Announce Type: replace-cross Abstract: Morphological traits are physical characteristics of biological organisms that provide vital clues on how organisms interact with their enviro
arXiv:2605.07394v1 Announce Type: cross Abstract: Image captioning is one of the most fundamental tasks in computer vision. Owing to its open-ended nature, it has received significant attention in the
arXiv:2505.22842v4 Announce Type: replace Abstract: Transformer-based language models rely on positional encoding (PE) to handle token order and support context length extrapolation. However, existing
arXiv:2602.03331v2 Announce Type: replace Abstract: We propose Bayesian Conformal Prediction (BCP), a framework that combines Bayesian posterior predictive distributions with PAC-style conformal risk
arXiv:2605.07590v1 Announce Type: new Abstract: Despite extensive progress in point cloud robustness, existing methods primarily improve performance through augmentation or defense mechanisms, while o
arXiv:2605.07557v1 Announce Type: new Abstract: Semi-supervised learning faces significant challenges in realistic scenarios where labeled data is scarce and unlabeled data follows unknown, arbitrary
arXiv:2605.07153v1 Announce Type: new Abstract: Reinforcement learning (RL) has achieved remarkable success in LLM reasoning, but whether it can also improve direct recall of parametric knowledge rema
arXiv:2605.07577v1 Announce Type: new Abstract: Bilevel graph structure learning is widely understood to improve graph neural networks by jointly optimizing model parameters and a learned graph struct
arXiv:2605.07878v1 Announce Type: new Abstract: Access to modern generative systems is often restricted to querying an API (the ``black-box' setting) and many properties of the system are unknown to t
arXiv:2512.19991v2 Announce Type: replace Abstract: We present a method that uses a Bloom filter transform to preprocess data for machine learning. Each sample is encoded into a compact bit-array repr
arXiv:2605.07452v1 Announce Type: new Abstract: Bounded fitting is an attractive paradigm for learning logical formulas from labeled data examples that offers PAC-style generalization guarantees and c
arXiv:2605.07473v1 Announce Type: cross Abstract: To overcome the limitations of classical partially connected Boltzmann machines and mainstream quantum Boltzmann machines (QBMs), this work extends th
arXiv:2605.06679v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reli
arXiv:2605.06981v1 Announce Type: cross Abstract: Personalized systems rely on user representations to connect behavioral history with downstream recommendation applications. Existing methods typicall
arXiv:2605.06878v1 Announce Type: cross Abstract: This paper presents CARMEN, a runtime-adaptive, CORDIC-accelerated multi-precision vector engine for resource-efficient deep learning inference. The k
arXiv:2605.07230v1 Announce Type: cross Abstract: Autoregressive generation is a powerful approach for high-fidelity image synthesis, but it remains computationally demanding and slow even on the most
arXiv:2605.07335v1 Announce Type: new Abstract: Virtual Cell Modeling (VCM) requires models that not only predict perturbation responses, but also support targeted revision when predictions fail. Curr
arXiv:2602.01621v3 Announce Type: replace-cross Abstract: Homomorphic encryption (HE) is a prominent framework for privacy-preserving machine learning, enabling inference directly on encrypted data. H
arXiv:2311.08433v3 Announce Type: replace-cross Abstract: Few studies have investigated the diagnostic utilities of biomarkers for predicting bacteremia among septic patients admitted to intensive car
arXiv:2605.07162v1 Announce Type: new Abstract: Personalized LLMs can significantly enhance user experiences by tailoring responses to preferences such as helpfulness, conciseness, and humor. However,
arXiv:2510.04606v2 Announce Type: replace Abstract: Neural networks are typically optimized with variants of stochastic gradient descent. Under a squared loss, however, the optimal solution to the lin
arXiv:2605.07499v1 Announce Type: new Abstract: Accurate four-dimensional (4D) precipitation information is essential for understanding the Earth's energy and water cycles, yet remains observationally
arXiv:2403.18149v3 Announce Type: replace Abstract: Model-predictive control (MPC) is a state-of-the-art control method for constrained robotic systems, yet deployment on resource-limited hardware rem
arXiv:2510.07926v2 Announce Type: replace Abstract: Despite demonstrating remarkable performance across a wide range of tasks, large language models (LLMs) have also been found to frequently produce o