All entries

arXiv:2404.11624v2 Announce Type: replace-cross Abstract: We introduce the Token Space, a categorical framework for AI computations. A Token is a finite tuple whose entries are elements of a carrier s

arXiv:2609.38201v1 Announce Type: new Abstract: Long-running tools can dominate coding-agent latency: compilers, test suites, and repository commands take seconds to minutes while the agent idles. Thi

arXiv:2609.39797v1 Announce Type: new Abstract: Autonomous powder weighing is one of many bottlenecks in laboratory automation due to the complex, non-linear dynamics of heterogeneous materials. Robot

arXiv:2609.39792v1 Announce Type: new Abstract: Distinguishing periodic from chaotic dynamics in a time series is a fundamental challenge in both physics and engineering. Yet, end-to-end learned archi

arXiv:2609.39822v1 Announce Type: new Abstract: Vision-language-action (VLA) models face a timing gap between low-rate inference and high-rate robot execution. We characterize this gap through end-to-

arXiv:2609.39611v1 Announce Type: new Abstract: Agile multi-UAV flight requires accurate and low-latency onboard estimation of the kinematic states of neighboring UAVs for collision avoidance, motion

arXiv:2609.39632v1 Announce Type: new Abstract: Test-time scaling improves language model reasoning by spending additional compute at inference. However, both classes of existing methods often fail to

arXiv:2609.38574v1 Announce Type: new Abstract: Large language models are frequently proposed as a route to AI-powered services for African communities, but they are least reliable exactly where the n

arXiv:2504.09192v5 Announce Type: replace Abstract: The primary goal of my Ph.D. study is to develop provably efficient and practical algorithms for data-driven sequential decision-making under uncert

arXiv:2511.17735v2 Announce Type: replace Abstract: Foundation models in several scientific domains, including visual domains, learn representations that capture complex semantics from their respectiv

arXiv:2609.39397v1 Announce Type: cross Abstract: We study data-driven multi-period lost-sales inventory control under censored demand, where a stockout reveals only that demand exceeded the stocking

arXiv:2609.39489v1 Announce Type: new Abstract: Sample-level reliability heterogeneity is common in deep time series learning. Standard training pipelines apply a uniform regularization setting to all

arXiv:2609.39429v1 Announce Type: cross Abstract: Uncertainty Quantification (UQ) is a key requirement for trustworthy AI in high-stakes medical image analysis. In this work, we evaluate UQ in a multi

arXiv:2609.38547v1 Announce Type: cross Abstract: Defining a weighted mean over probability measures under probability metrics is a central tool in probabilistic machine learning. Under the Wasserstei

vLLM's tensor parallel requires that several of the model architecture numbers be evenly divisible by the number of GPUs selected for tensor parallel. This usually means that you can only use a number

arXiv:2609.32923v2 Announce Type: replace Abstract: Wireless digital twins (DTs) rely on 3D environment models to predict radio propagation and support wireless-network decisions, yet these models are

arXiv:2609.38861v1 Announce Type: new Abstract: Finding a relevant paper is not the same as producing a verifiable answer from it. LitTraceQA requires canonical paper identifiers, exact evidence at th

arXiv:2609.39912v1 Announce Type: cross Abstract: Parallel search may generate a correct answer that final-answer voting fails to select. We formulate this consolidation stage as trajectory selection

arXiv:2609.38347v1 Announce Type: new Abstract: Quantifying collective fish behavior requires accurate trajectories, yet multi-view 3D tracking remains challenging due to frequent occlusions, visually

arXiv:2609.38463v1 Announce Type: cross Abstract: Autonomous driving planners are typically evaluated using aggregate metrics such as driving score, destination rate, and collision rate, which do not

arXiv:2609.39162v1 Announce Type: cross Abstract: Speaker diarization systems based on speaker embeddings and neural diarization exploit complementary forms of speaker information, but their intermedi

arXiv:2609.38792v1 Announce Type: new Abstract: We study training LLM judges from natural language feedback, especially for subjective tasks where the verdict depends strongly on which evaluation crit

arXiv:2609.38190v1 Announce Type: cross Abstract: The purpose of this research is to find data and methods using machine learning and deep learning to correctly predict the estimated travel time for t

arXiv:2606.19998v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are increasingly deployed across diverse tasks, yet they remain black boxes whose physical interactions ca

arXiv:2609.39157v1 Announce Type: new Abstract: Video object removal presents a uniquely difficult editing challenge. Because a removal prompt specifies only what to erase rather than what to generate

arXiv:2609.39717v1 Announce Type: new Abstract: Benchmarks, audits, and agent protocols describe performance, permissions, and repair, but not how observed evidence should change an agent's authority

arXiv:2609.39247v1 Announce Type: cross Abstract: Standard language model RL algorithms credit every token of a long rollout with the same advantage determined by the terminal reward. Actor-critic met

arXiv:2609.39086v1 Announce Type: cross Abstract: Runtime error healing lets a crashed program continue by generating code that repairs its live runtime state. Recent work shows that LLMs can generate

arXiv:2609.38635v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) is a popular representation for novel view synthesis. However, 3DGS contains millions of Gaussian primitives, each with r

arXiv:2609.39051v1 Announce Type: new Abstract: Existing multimodal video highlight detectors typically assume that visual, audio, and textual streams are continuously available. In practice, however,

arXiv:2609.39385v1 Announce Type: cross Abstract: Argument Mining (AM) is a critical NLP task that remains significantly under-resourced in Arabic. This paper presents esttt{STAR-Ar}, a BERT-BiLSTM-CR

arXiv:2609.40330v1 Announce Type: new Abstract: Automating the search for effective harnesses is an important step toward enabling agents to recursively self-improve. Existing harness optimizations ty

Turns out sometimes, you can just rebuild things. Congrats to the team on the launch! 🦙 🚀 Today we’re introducing Extract v2.5 - a series of frontier agents tuned for document extraction. The agents (

arXiv:2609.38219v1 Announce Type: new Abstract: Tamazight (Amazigh) is, together with Arabic, one of the two official languages of Morocco, yet it remains severely under-resourced for speech technolog

arXiv:2512.15774v5 Announce Type: replace Abstract: The absence of large-scale masked face datasets challenges masked face detection and recognition. We propose a two-step generative data augmentation

arXiv:2609.39662v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used for AI-generated image (AIGI) detection, providing natural-language explanations for authenticity ju

arXiv:2609.39972v1 Announce Type: new Abstract: Speculative decoding accelerates language model inference by verifying multiple draft tokens in a single target-model pass. Recent parallel drafters hav

arXiv:2609.39089v1 Announce Type: new Abstract: Sparse-view 3D Gaussian Splatting is prone to overfitting because limited observations leave many Gaussian primitives weakly constrained, yet their cont

arXiv:2609.39684v1 Announce Type: new Abstract: Privacy has become an increasingly important concern in the Document Analysis community, to the extent that in many environments such as archives, gover

arXiv:2609.39132v1 Announce Type: new Abstract: We study few-step video generation, i.e., distilling a multi-step video generator, which typically requires tens of sampling steps, incurring substantia

arXiv:2609.38647v1 Announce Type: new Abstract: Direct preference optimization (DPO) models binary preferences through a Bradley-Terry model with a common noise scale, without explicitly accounting fo

arXiv:2609.38802v1 Announce Type: cross Abstract: Uncontrolled repetition can prolong autoregressive generation in large language models (LLMs) and enable resource consumption attacks. Prior analyses

arXiv:2609.39341v1 Announce Type: new Abstract: Does a language model merely predict tokens, or does it understand what it says? We make this question measurable by defining 'understanding' through th

arXiv:2609.39464v1 Announce Type: new Abstract: In federated averaging, local objectives can admit multiple optimal heads, making the aggregate depend on which heads clients return. We study this ambi

arXiv:2609.38666v1 Announce Type: cross Abstract: On-policy distillation (OPD) learns from teacher feedback on student-generated responses and has shown promise in reducing forgetting relative to supe

arXiv:2609.39906v1 Announce Type: cross Abstract: AI could support children's pretend play, but it could also direct the play on behalf of children. Whether AI should have roles in children's lives is