Search: “papers”

Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPO

arXiv:2608.02031v1 Announce Type: cross Abstract: This paper investigates collaborative mobile edge computing (MEC) servers for large language model (LLM) inference under soft deadline constraints. In

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Learning-Based Stochastic Optimal Control with Infinite-Horizon Probabilistic Constraints

DGX agent

arXiv:2608.01151v1 Announce Type: cross Abstract: In this paper, we consider stochastic optimal control problems with infinite-horizon joint chance constraints. By means of an appropriate state augmen

safetyarxiv-cs-lg

4 Aug 2026

Learning to Predict Contact Force Distributions from Vision Leveraging Object Geometry Priors

arXiv:2608.00464v1 Announce Type: new Abstract: Based on vision and prior experience, humans can make rough physical predictions and adjust their manipulation strategies. This paper aims to endow robo

applicationsarxiv-cs-ro

Localization in Spatiotemporal Fields via Environmental PDEs

arXiv:2608.00272v1 Announce Type: new Abstract: This paper proposes a localization framework that uses spatiotemporal fields governed by partial differential equations (PDEs) as localization signature

safetyarxiv-cs-ro

SCHEDBench: A Benchmark for Evaluating LLM Constraint Faithfulness in Natural-Language Combinatorial Scheduling

model-releasesarxiv-cs-cl

arXiv:2608.00991v1 Announce Type: cross Abstract: This paper introduces SCHEDBench, a natural-language benchmark for evaluating combinatorial scheduling constraint faithfulness under surface-form vari

Local Ai

Sharp Root Anti-Concentration via Projective Incidence and Ordered Root Laws

arXiv:2608.01670v1 Announce Type: new Abstract: This paper answers the one-dimensional local root anti-concentration questions posed by Balcan, Pegden, and Sharma in the context of online optimization

local-aiarxiv-cs-lg

Tutorials

UniqueSplat: View-conditioned 3D Gaussian Splatting for Generalizable 3D Reconstruction

arXiv:2608.02145v1 Announce Type: new Abstract: In this paper, we propose UniqueSplat, a view-conditioned feed-forward 3D Gaussian Splatting model to reconstruct customized 3D radiance fields for each

tutorialsarxiv-cs-cv

Unsupervised Multidomain Approaches to Named Entity Recognition with Small Datasets

arXiv:2608.00984v1 Announce Type: new Abstract: This paper explores the challenges and the methodologies associated with learning quality representations in scenarios with unlabelled small or limited

researcharxiv-cs-cl

ConnectED: A Curriculum-Aligned AI System for Vietnamese Instructional Lesson Planning and Student Learning

arXiv:2607.28647v1 Announce Type: cross Abstract: This paper presents ConnectED, a human-centered AI system that supports the full instructional lifecycle in Vietnamese education by linking curriculum

safetyarxiv-cs-ai

Convergence and Regret of the Policy Gradient for Multi-Armed Bandits in Diffusion Environment

arXiv:2607.29593v1 Announce Type: new Abstract: This paper studies the policy gradient update for a multi-arm bandit problem in diffusion environment that is described by a stochastic differential equ

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml

arXiv:2602.15751v2 Announce Type: replace-cross Abstract: This paper presents an end-to-end demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which coul

Tutorials

Fast Feature Field (ext{F}^3): A Predictive Representation of Events

arXiv:2509.25146v2 Announce Type: replace-cross Abstract: This paper develops a mathematical argument and algorithms for building representations of data from event-based cameras, that we call Fast Fe

tutorialsarxiv-cs-ai

Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM

arXiv:2607.28635v1 Announce Type: new Abstract: In Natural Language Processing (NLP), dealing with underrepresented topics is challenging, especially in unsupervised tasks where clustering might not a

researcharxiv-cs-cl

JoVA: Unified Multimodal Learning for Joint Video-Audio Generation and Editing

arXiv:2512.13677v2 Announce Type: replace Abstract: In this paper, we present JoVA, a streamlined framework that unifies joint video-audio generation and editing. While existing methods often rely on

safetyarxiv-cs-cv

Learning Manifolds in High-D Point Embedding for Anisotropic Surface Approximation from Unstructured Point Clouds

arXiv:2607.28855v1 Announce Type: cross Abstract: Dense 3D sensors in various real-world fields produce point clouds that are geometrically redundant for real-time processing. In this paper, we propos

applicationsarxiv-cs-cv

Revisiting Multi-Permutation Equivariance through the Lens of Irreducible Representations

model-releasesarxiv-cs-ai

arXiv:2410.06665v4 Announce Type: replace-cross Abstract: This paper explores the characterization of equivariant linear layers for representations of permutations and related groups. Unlike tradition

Unified continuous-time q-learning for mean-field game and mean-field control problems

arXiv:2407.04521v3 Announce Type: replace-cross Abstract: This paper studies the continuous-time q-learning in mean-field jump-diffusion models in a setting where the environment simulator does not pr

ZeroR@CHiPSAL 2026: Two-Stage Vision-Language Adaptation with Contrastive Learning for Nepali Meme Classification

arXiv:2607.28637v1 Announce Type: new Abstract: This paper presents our system for the CHiPSAL 2026 shared task on multimodal hate speech and sentiment detection in Nepali memes. We address both subta

researcharxiv-cs-cl

Agents

A Methodology for Designing Knowledge-Driven Missions for Robots

arXiv:2601.20797v1 Announce Type: cross Abstract: This paper presents a comprehensive methodology for implementing knowledge graphs in ROS 2 systems, aiming to enhance the efficiency and intelligence

agentsarxiv-cs-ai

An Efficient Solution to the 2D Visibility Problem in Cartesian Grid Maps and its Application in Heuristic Path Planning

arXiv:2403.06494v2 Announce Type: replace-cross Abstract: This paper introduces a novel, lightweight method to solve the visibility problem for 2D grids. The proposed method evaluates the existence of

researcharxiv-cs-ro

Challenges in annotations by humans and LLMs: A case study of evaluative language

arXiv:2607.28119v1 Announce Type: new Abstract: In this paper, we draw a comparison between linguists in training, a trained linguist, and annotations generated by large language models (LLMs) to find

applicationsarxiv-cs-cl

Continuous-time reinforcement learning for optimal switching over multiple regimes

model-releasesarxiv-cs-lg

arXiv:2512.04697v3 Announce Type: replace-cross Abstract: This paper studies the continuous-time reinforcement learning (RL) for optimal switching problems across multiple regimes. We consider a type

EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents

model-releasesarxiv-cs-cl

arXiv:2607.28229v1 Announce Type: new Abstract: The web is increasingly accessed by AI agents rather than humans. Every agent needs knowledge, especially in the life-sciences, where agentic pipelines

Face and Voice Cross-modal Association with Learning Convex Feature Embedding

arXiv:2607.28129v1 Announce Type: new Abstract: Face-and-voice association learning is one of the most challenging tasks in deep learning. In this paper, we propose a simple but powerful cross-modal f

researcharxiv-cs-cv

Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras

arXiv:2607.28293v1 Announce Type: new Abstract: While depth sensors have the potential to complement RGB data for affordance segmentation in wearable robots, their usage seems to remain underexplored.

applicationsarxiv-cs-cv

Learning-Augmented and Randomized Algorithms for Line Aggregation with Delays

model-releasesarxiv-cs-lg

arXiv:2607.27807v1 Announce Type: new Abstract: This paper studies learning-augmented and randomized online aggregation with delays on a line metric. We consider advice given as online suggested servi

Learning features from Newton's algorithm: a way to accelerate nonlinear parametrized PDE solvers

model-releasesarxiv-cs-lg

arXiv:2607.28036v1 Announce Type: new Abstract: It is well known that Newton's method converges faster when the initial guess is closer to a root of a system of nonlinear equations. In this paper, a t

Optimizing Regret

arXiv:2607.18866v2 Announce Type: replace-cross Abstract: Building on the identity that expected regret equals the covariance between costs and decisions, this paper develops a derivative theory of th

Physical prior guided cooperative learning framework for joint turbulence degradation estimation and infrared video restoration

arXiv:2408.04227v2 Announce Type: replace-cross Abstract: Infrared imaging and turbulence strength measurements are in widespread demand in many fields. This paper introduces a Physical Prior Guided C

researcharxiv-cs-cv

Reviewer Scores Are Not Comparable Across Research Areas in ML Peer Review

arXiv:2607.27209v1 Announce Type: cross Abstract: Peer review at ML conferences increasingly relies on reviewer scores as the primary decision instrument. As submissions have scaled from thousands to

A fundamental flaw leaves LLMs strikingly vulnerable to attack

It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a team of researchers argue in a paper presented at the International Conferen

safetymit-tech-review

Calibri: Enhancing Diffusion Transformers via Parameter-Efficient Calibration

model-releasesarxiv-cs-cv

arXiv:2603.24800v2 Announce Type: replace Abstract: In this paper, we uncover the hidden potential of Diffusion Transformers (DiTs) to significantly enhance generative tasks. Through an in-depth analy

Controlled Experiments on Lane Changing by Transitional Autonomous Vehicle: Dataset and Behavioral Insights

arXiv:2607.27085v1 Announce Type: new Abstract: This paper presents the North Carolina Transitional Autonomous Vehicle Lane-Changing (NC-tALC) dataset and uses it to characterize mandatory lane-changi

safetyarxiv-cs-ro

Field Codes for Distributed Coupling Samplers and Certified Empirical Transport

arXiv:2607.27078v1 Announce Type: cross Abstract: In this paper, we formulate three communication tasks for empirical optimal transport: distributed coupling sampling, cost-evaluable coupling output,

Ontology-driven personalized information retrieval for XML documents

arXiv:2603.21139v2 Announce Type: replace-cross Abstract: This paper addresses the challenge of improving information retrieval from semi-structured eXtensible Markup Language (XML) documents. Traditi

Risk-Aware Motion Planning with Learned Trajectory Primitives and Probabilistic Safety Assessment

model-releasesarxiv-cs-ro

arXiv:2607.26802v1 Announce Type: new Abstract: This paper presents a radial basis function network (RBFN)-informed motion planning framework for safe and efficient urban autonomous driving. The propo

RVC-NMPC: Nonlinear Model Predictive Control with Reciprocal Velocity Constraints for Mutual Collision Avoidance in Agile UAV Flight

arXiv:2512.08574v2 Announce Type: replace Abstract: This paper presents an approach to mutual collision avoidance based on Nonlinear Model Predictive Control (NMPC) with time-dependent Reciprocal Velo

applicationsarxiv-cs-ro

A Human-in-the-Loop Corpus for LLM-Based Simplification of Scientific Summaries

arXiv:2607.25630v1 Announce Type: cross Abstract: Interdisciplinary research is accelerating, yet scientific papers remain difficult to understand outside their home fields. We study large language mo

researcharxiv-cs-ai

A Support-Set Algorithm for Optimization Problems with Nonnegative and Orthogonal Constraints

arXiv:2511.03443v2 Announce Type: replace-cross Abstract: In this paper, we investigate optimization problems with nonnegative and orthogonal constraints, where any feasible matrix of size n imes p ex

applicationsarxiv-cs-lg

A Unified Algorithmic Framework for Hybrid Reinforcement Learning in Tabular MDPs with Shifted Transition Dynamics

arXiv:2607.25207v1 Announce Type: new Abstract: This paper investigates a hybrid reinforcement learning setting in tabular Markov Decision Processes (MDPs), where an agent aims to learn an optimal pol

Adversarial Deepfake Generation and an Investigation of Purification-Based Adversarial Detection

arXiv:2607.25842v1 Announce Type: new Abstract: This paper describes the participation of team 'Go To Germany' in the ImageCLEF 2026 Deepfake Detection and Generation Task. For the image generation ta

researcharxiv-cs-cv

An Embarrassingly Simple Rule-based Visiting Circulation Approach to Trip Destination Prediction

arXiv:2607.25751v1 Announce Type: new Abstract: In this paper, we propose the Rule-based Visiting Circulation (RVC) model in tackling the challenge in the IEEE Big Data Cup 2022: Trip Destination Pred

COCO-OLAC: A Benchmark for Occluded Panoptic Segmentation and Image Understanding

model-releasesarxiv-cs-cv

arXiv:2409.12760v3 Announce Type: replace Abstract: To help address the occlusion problem in panoptic segmentation and image understanding, this paper proposes a new large-scale dataset named COCO-OLA

Computational Extraction of Legal Causes via al-Sabr wa al-Taqsim: A Set-Theoretic Formalization for Closed Fiqh Chapters

arXiv:2607.25605v1 Announce Type: new Abstract: This paper presents a set-theoretic formalization of the classical usuli method of al-Sabr wa al-Taqsim (Examination and Division) for extracting legal

applicationsarxiv-cs-ai

ContractHIL-HLS: Contract-Aligned Multi-Agent Workflow with Hardware-in-the-Loop Feedback for HLS Design

arXiv:2607.25283v1 Announce Type: new Abstract: This paper presents ContractHIL-HLS, a contract-aligned multi-agent workflow for practical high-level synthesis (HLS) engineering. The workflow makes th

safetyarxiv-cs-ai

Device Invariance using Domain Adaptation on Acoustic Scene Classification

arXiv:2607.25887v1 Announce Type: cross Abstract: This paper explores the effectiveness of domain adaptation techniques when using convolutional neural network (CNN)-based and transformer-based featur

researcharxiv-cs-ai

Tutorials

Diffusion Disambiguation Models for Partial Label Learning

arXiv:2507.00411v2 Announce Type: replace Abstract: Learning from ambiguous labels is a long-standing problem in practical machine learning applications. The purpose of partial label learning (PLL) is

tutorialsarxiv-cs-lg

DS@GT ARC at CheckThat! 2026: LLM-Based Trace Ranking and Grouped Reward Modeling for Multilingual Numerical Claim Verification

arXiv:2607.25069v1 Announce Type: cross Abstract: Automated verification of numerical claims is a challenging problem, as it requires both language understanding and quantitative reasoning. This paper

researcharxiv-cs-ai