Knowledge catalogue
arXiv:2506.17251v3 Announce Type: replace-cross Abstract: Although large language models (LLMs) have achieved remarkable performance, the inherent stochasticity of their reasoning processes and varyin
arXiv:2608.30769v1 Announce Type: new Abstract: LLM training is increasingly vulnerable to silent data corruption (SDC), yet existing protection methods largely treat Transformer computations uniforml
arXiv:2608.30512v1 Announce Type: cross Abstract: Large-scale industrial robot fleets share constrained physical infrastructure, making vehicle travel times dependent on safety separation, intersectio
arXiv:2608.29690v1 Announce Type: cross Abstract: Advances in machine learning (ML) have created new opportunities to complement traditional operations research (OR) methods. In particular, transforme
arXiv:2509.17930v3 Announce Type: replace-cross Abstract: Multilingual translation suffers from computational redundancy, especially when translating into multiple languages simultaneously. In additio
arXiv:2601.08058v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting often improves the reasoning performance of large language models (LLMs), but the internal signal that trigge
arXiv:2608.29577v1 Announce Type: new Abstract: Traditional video highlight detection relies on a narrow, event-centric definition of saliency, which often fails to generalize to unconstrained persona
arXiv:2602.09888v2 Announce Type: replace Abstract: Mobile manipulators broaden the operational envelope for robot manipulation. However, the whole-body teleoperation of such robots remains a problem:
arXiv:2608.30924v1 Announce Type: new Abstract: Travel itinerary generation requires balancing strict spatio-temporal constraints with human preferences. Existing LLM-based planners mainly rely on str
arXiv:2503.11841v2 Announce Type: replace-cross Abstract: Machine Learning (ML) malware detectors rely heavily on crowd-sourced AntiVirus (AV) labels, with platforms like VirusTotal serving as trusted
arXiv:2608.30514v1 Announce Type: cross Abstract: We present TSExplorer, a cross-platform tool for interactive annotation and exploration of time-series data. The tool enables users to inspect high-di
arXiv:2608.31013v1 Announce Type: cross Abstract: Designing models that generalize effectively in low- to medium-data regimes remains a primary challenge in medical machine learning, particularly for
arXiv:2608.30704v1 Announce Type: new Abstract: AI-generated video detection, which aims to distinguish AI-generated videos from real ones, has recently received increasing research attention. To perf
arXiv:2608.30567v1 Announce Type: new Abstract: We present Turing-20B-A2B, a 20B-parameter Mixture-of-Experts language model that activates approximately 2B parameters per token, designed for long-con
arXiv:2508.20766v2 Announce Type: replace-cross Abstract: Safety alignment in Large Language Models (LLMs) often involves mediating internal representations to refuse harmful requests. Recent research
arXiv:2608.30485v1 Announce Type: new Abstract: The language a legislature uses to debate women's rights, even in favour of them, encodes systematic patterns of sexism that persist across two centurie
arXiv:2512.06933v4 Announce Type: replace-cross Abstract: Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data or sur
arXiv:2608.31038v1 Announce Type: new Abstract: Incremental Named Entity Recognition (INER) stands as a pivotal task in information extraction, emphasizing the successive identification of new entity
arXiv:2608.30688v1 Announce Type: new Abstract: While face recognition systems are widely deployed, ensuring their demographic reliability and robustness under uncontrolled visual conditions remains a
arXiv:2510.22588v2 Announce Type: replace-cross Abstract: Spoken dialogue models currently lack the ability for fine-grained speech style control, a critical capability for human-like interaction that
arXiv:2608.30795v1 Announce Type: cross Abstract: End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the numer
arXiv:2608.28865v1 Announce Type: cross Abstract: Joint modulation recognition and signal-to-interference-plus-noise ratio (SINR) estimation can reduce duplicated processing in intelligent receivers,
arXiv:2608.29235v1 Announce Type: new Abstract: Anti-UAV perception systems must remain reliable when sensor streams degrade under occlusion, fast motion, or modality-specific failure. Existing multim
arXiv:2608.30899v1 Announce Type: new Abstract: Most trajectory forecasting models are trained on clean annotated histories, and are often evaluated under the same idealized assumption, although pract
arXiv:2608.29860v1 Announce Type: new Abstract: Uncertainty estimation provides promising capabilities for reinforcement learning (RL) agents. Notably, estimating uncertainty can reduce the training t
arXiv:2511.11743v4 Announce Type: replace-cross Abstract: Deploying deep neural networks on resource-constrained devices faces two critical challenges: maintaining accuracy under aggressive quantizati
arXiv:2608.30390v1 Announce Type: new Abstract: Accurate uncertainty estimation is essential for machine learning systems de- ployed in high-stakes domains such as medicine. Traditional approaches pri
arXiv:2608.29279v1 Announce Type: new Abstract: Deep learning is often criticized for its theoretical research lagging behind practice. To make deep learning easier to understand, the entropy space th
arXiv:2408.02111v4 Announce Type: replace-cross Abstract: Despite the extreme popularity of deep learning in science and industry, its formal understanding is limited. This thesis puts forth notions o
arXiv:2601.03468v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a standard approach for post-training large language models and, more recently, for improving image generatio
arXiv:2608.28665v1 Announce Type: new Abstract: Recent open-vocabulary segmentation models have advanced semantic perception for UAVs, but predictions from moving aerial platforms can remain temporall
arXiv:2603.21475v2 Announce Type: replace Abstract: Automatic Multi-Agent Systems (MAS) generation has emerged as a promising paradigm for solving complex reasoning tasks. However, existing frameworks
arXiv:2608.29152v1 Announce Type: cross Abstract: We study the empirical Sinkhorn estimator of the entropic optimal transport potentials under the uniform loss. Since the potentials are only unique up
arXiv:2602.01605v2 Announce Type: replace Abstract: Time Series Foundation Models (TSFMs) leverage extensive pretraining to accurately predict unseen time series during inference, without the need for
arXiv:2608.31067v1 Announce Type: new Abstract: Learning generalizable algorithmic computations remains a challenge for neural networks, as reflected in persistent failures on compositional and length
arXiv:2601.18552v2 Announce Type: replace Abstract: LLMs expand accessibility and provide wide-reaching access to information. Yet these interactions also create opportunities to embed subtle, goal-or
arXiv:2608.29369v1 Announce Type: new Abstract: Spatio-temporal graphs are widely used in modeling complex dynamic processes such as temporal forecasting, molecular dynamics, and healthcare monitoring
arXiv:2409.11018v3 Announce Type: replace Abstract: The LiDAR 3D object detector that balances accuracy and speed is crucial for achieving real-time perception in autonomous driving. However, many exi
arXiv:2608.28840v1 Announce Type: new Abstract: Independently trained neural networks tend to encode the same data with similar latent geometries. These latent geometries are not directly compatible,
arXiv:2608.29635v1 Announce Type: new Abstract: We study unsupervised hypergraph alignment, where the goal is to infer node correspondences between two hypergraphs using only structural information, w
arXiv:2604.03592v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models exhibit striking performance disparities across languages, yet the internal mechanisms driving these gaps rema
arXiv:2602.08336v3 Announce Type: replace Abstract: Unified multimodal models (UMMs) aim to integrate multimodal understanding and generation within a unified architecture, yet it remains unclear to w
arXiv:2608.29227v1 Announce Type: new Abstract: In this work we propose a novel non-linear diffusion filtering approach for images based on their channel representation. To derive the diffusion update
arXiv:2608.30391v1 Announce Type: cross Abstract: Understanding agent behavior requires methods that scale to thousands of trajectories and surface new patterns in long, often unfamiliar tasks where p
arXiv:2608.30260v1 Announce Type: cross Abstract: While it is well-established that prosody carries crucial cues for syntactic structure, the degree and nature of correspondence between these two doma
arXiv:2608.30508v1 Announce Type: new Abstract: Long-term memory is increasingly important for conversational agents, yet existing benchmarks primarily measure memory through pointwise factual recall:
arXiv:2608.28981v1 Announce Type: new Abstract: As air traffic volumes in the National Airspace System continue to expand, in particular in the low altitude airspaces, the need for scalable decision s
arXiv:2608.29249v1 Announce Type: new Abstract: The online culinary ecosystem is increasingly populated by recipe content generated, modified, or summarized by Large Language Models (LLMs). While ofte
arXiv:2608.28706v1 Announce Type: new Abstract: ViT detectors fix a uniform token grid before any learned stage. A native-resolution aerial detector must then choose between resolving few-pixel object
arXiv:2603.23947v2 Announce Type: replace-cross Abstract: Audio fingerprinting converts audio to much lower-dimensional representations, allowing distorted recordings to still be recognized as their o
arXiv:2608.30870v1 Announce Type: new Abstract: Semantic segmentation in 3D Gaussian Splatting (3DGS) is crucial for advancing 3D scene understanding. Existing methods predominantly rely on feature di
arXiv:2505.12196v2 Announce Type: replace Abstract: The impressive linguistic abilities of large language models (LLMs) have recommended them as models of human sentence processing, with some conjectu
arXiv:2608.28723v1 Announce Type: new Abstract: Automated drift controllers commonly track a prescribed drift equilibrium, sideslip reference, or trajectory. These formulations establish how to execut
arXiv:2608.29720v1 Announce Type: new Abstract: Inertial odometry (IO) is critical for aerial robots, where aggressive maneuvers and poor lighting degrade visual sensors. Recent learning-based IO meth
arXiv:2608.30091v1 Announce Type: new Abstract: Modern agent frameworks compose planners, tool agents, remote services, and shared specialists into runtime delegation graphs, but their revocation APIs
arXiv:2608.31107v1 Announce Type: new Abstract: The advent of foundation models have enabled a new era in zero-shot classification. Yet, key challenges persist. Despite their impressive generalization
arXiv:2608.28997v1 Announce Type: new Abstract: In May 2026 an OpenAI model produced a counterexample to the Erdos unit distance conjecture. Five mathematicians published a human-verified version the
arXiv:2608.30135v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference by using a draft model to generate candidate tokens, which are verified by the target mo
arXiv:2608.30846v1 Announce Type: new Abstract: Fairness audits in clinical Artificial Intelligence convert continuous fairness metrics into binary pass-or-fail verdicts against operational thresholds
arXiv:2608.30125v1 Announce Type: cross Abstract: Current video-to-music (V2M) models lack semantic control and fail to penalize instruction violations, largely due to their reliance on reconstruction