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From Small Talk to Rapport: Exploring Robot Self-Disclosure in Collaborative Tasks

arXiv:2608.28154v1 Announce Type: new Abstract: People naturally chat while collaborating and share personal information (i.e., self-disclose) to build rapport and maintain social connections. As robo

researcharxiv-cs-ro

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Model Releases

Fully Unleashing the Multimodal Attacker: Meta-Adaptive Jailbreaking of Vision-Language Models

DGX agent

arXiv:2608.27531v1 Announce Type: cross Abstract: The safety of large vision-language models is increasingly stress-tested by multimodal jailbreaks, yet existing attacks remain largely static at the m

model-releasesarxiv-cs-cv

31 Aug 2026

FUSED: Forensic-Semantic Mixture-of-Experts for AI Inpainting Detection and Localization

arXiv:2608.28302v1 Announce Type: new Abstract: Diffusion-based inpainting models modify only a localized part of an image, while many AI-image detectors rely on global artifacts and do not localize.

FVeinSyn: Synthetic Finger Vein Image Generator

arXiv:2608.27527v1 Announce Type: new Abstract: A major challenge in finger vein recognition is the lack of large-scale public datasets. Existing datasets contain few identities and limited samples pe

researcharxiv-cs-cv

GAAT: Geometry-Aware Alignment Transformer for Multimodal UAV Perception

arXiv:2608.27971v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) multimodal perception integrates visible (RGB), infrared (IR), synthetic aperture radar (SAR), and depth sensors for scene

GAN-Based Semantic Communication for Image Transmission in IoV

arXiv:2608.27989v1 Announce Type: new Abstract: For cooperative perception in the internet of vehicles, this paper proposes a generative adversarial network-based semantic communication framework to a

safetyarxiv-cs-cv

GeBDA: Building Damage Assessment as Text-Based Sequence Prediction

arXiv:2608.28567v1 Announce Type: new Abstract: Conventionally, Building Damage Assessment (BDA) is tackled either with dedicated network architectures or by fine-tuning geospatial image foundation mo

Tutorials

Generalized Context in Cross Attention for Transfer Learning of Disjoint Tabular Data

arXiv:2608.28209v1 Announce Type: new Abstract: Unlike images and text, applying transfer learning to tabular data is challenging due to heterogeneity in feature types, structures, and semantics acros

tutorialsarxiv-cs-lg

Generalized Gibbs Ensemble Weighting for Forecast Combination

arXiv:2608.28116v1 Announce Type: new Abstract: Forecast combination is a reliable way to improve predictive performance when several forecasting models are available. Simple aggregation rules such as

Generalized Splines and Gaussian Processes

arXiv:2608.28446v1 Announce Type: cross Abstract: For finite-dimensional linear inverse problems where the variables are Gaussian, it is well-known that the minimum-mean-square error estimator takes t

GeoFF3D: Coordinate-Anchored Feed-Forward Reconstruction for Large-Scale UAV Mapping

arXiv:2608.28288v1 Announce Type: new Abstract: Existing feed-forward 3D reconstruction methods typically process a bounded number of images and recover cameras and geometry in local or internally nor

GRACE:Gradient-guided Coreset Selection for LLM Unlearning

arXiv:2608.28361v1 Announce Type: cross Abstract: Machine Unlearning methods for Large Language Models typically assume pre-specified forget and retain sets. In realistic settings, however, requests m

GraspHOI: Full-Body 3D Human-Object Reconstruction with Finger-Level Grasps from a Single In-the-Wild Image

arXiv:2608.28386v1 Announce Type: new Abstract: Existing monocular full-body 3D human-object interaction (HOI) methods do not combine explicit finger-level grasp optimization with category-agnostic ob

H-Scale: Hessian-Guided Scale Refinement for NVFP4 Sub-Byte LLM Inference

arXiv:2608.28113v1 Announce Type: new Abstract: The NVIDIA Blackwell architecture, with native support for the ultra-fine-grained NVFP4 format, opens new opportunities for accelerating large language

local-aiarxiv-cs-cl

HARTS: Efficient Agentic Reinforcement Learning for Hybrid-Attention Models over Arbitrary Rollout Trees

arXiv:2608.28158v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) often produces irregular rollout trees with shared histories. Training root-to-leaf trajectories independently recom

Agents

Hierarchical Feature-level Reverse Propagation for Post-Training Neural Networks

arXiv:2506.07188v2 Announce Type: replace Abstract: End-to-end neural networks have become a dominant paradigm in autonomous driving, where reliable deployment requires controllable post-training adap

agentsarxiv-cs-cv

How Do Linear Probes Emerge? A Circuit-Tracing Framework with Concept-Targeted Attribution

arXiv:2608.27510v1 Announce Type: new Abstract: Transcoder attribution graphs are usually trained to explain why a model assigns high probability to a particular next token. We introduce Concept-Targe

How Far Can 5,500 Hours of Driving Take You? A Scaling Law Analysis of Video Diffusion Models

arXiv:2608.28404v1 Announce Type: new Abstract: Video generation for autonomous driving cannot follow the web-scale route: driving data is expensive to collect, bound by privacy requirements, and cann

How Proper Scoring Rules Shape LLM Forecasting

arXiv:2608.28482v1 Announce Type: new Abstract: This paper evaluates how reward function choice shapes the performance and behavior of LLM forecasters. We compare five proper scoring rules as training

Agents

Hypothesize, Evaluate, Refine: A Scientific Agent for PDE Discovery with Unknown Spatial Coefficient Fields

arXiv:2608.27475v1 Announce Type: cross Abstract: Discovering PDEs in heterogeneous media requires jointly identifying the governing operator and the unknown spatial fields that parameterize it. These

agentsarxiv-cs-lg

I-FLOP: Fast Learning of Order and Parents from Interventional Data

arXiv:2608.28245v1 Announce Type: cross Abstract: We extend the FLOP (fast learning of order and parents) algorithm recently proposed by Wienobst et al. (2026) from observational to interventional dat

Image Augmentation as Test Generation for Deep Learning-Based Image Retrieval Systems

arXiv:2608.27502v1 Announce Type: cross Abstract: Ensuring the reliability of deep learning-based image retrieval systems is a software engineering challenge. This paper presents a dual contribution:

researcharxiv-cs-cv

Improved off-policy training of diffusion samplers

arXiv:2402.05098v5 Announce Type: replace Abstract: We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We benchmark s

InfoMamba: An Attention-Free Hybrid Mamba-Transformer Model

arXiv:2603.18031v2 Announce Type: replace Abstract: Balancing fine-grained local modeling with long-range dependency capture under computational constraints remains a central challenge in sequence mod

Informational Antilocality and the Locality Bias in LLMs

arXiv:2608.27760v1 Announce Type: new Abstract: We consider the ability of transformer-based language models (LLMs) to learn what we call k-antilocal languages, i.e., languages that have no mutual inf

Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization

arXiv:2608.27791v1 Announce Type: new Abstract: Short-term load forecasting (STLF) provides essential information for numerous applications in modern power systems. However, accurate STLF often relies

local-aiarxiv-cs-lg

INSPIRE: An Internalize-Then-Improve Approach for Example-Driven Mathematical Reasoning

arXiv:2608.27501v1 Announce Type: new Abstract: Mathematical reasoning has seen rapid progress in large language models (LLMs), yet existing methods optimize predominantly for final-answer correctness

iOSPointMapper: RealTime Pedestrian and Accessibility Mapping with Mobile AI

arXiv:2512.22392v2 Announce Type: replace Abstract: Accurate, up-to-date sidewalk data is essential for building accessible and inclusive pedestrian infrastructure, yet current approaches to data coll

Iron: Intent-Aligned and Retrospective Dual Learning Framework for Enhancing Generalist Virtual Agents

arXiv:2608.27866v1 Announce Type: new Abstract: Achieving virtual agents capable of automating tasks across diverse digital environments remains a pivotal challenge in Embodied AI. While Multimodal La

safetyarxiv-cs-cv

Is Monte Carlo Tree Search Just Every-Visit Monte Carlo Control?

arXiv:2608.27985v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) and every-visit Monte Carlo (MC) control are usually presented as different methods. MCTS is described in the language of

Is Prosody Lost in Translation? Fine-Grained Cross-Lingual Prosody Similarity Across Languages

arXiv:2608.27848v1 Announce Type: cross Abstract: Prosody plays an important role in speech translation, conveying information such as emphasis, emotion, and intent beyond lexical content. However, de

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network

arXiv:2305.19366v3 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied to the p

Knowing Before Answering: Decoding Language Models for Reliable RAG

model-releasesarxiv-cs-cl

arXiv:2608.27661v1 Announce Type: new Abstract: In Retrieval-Augmented Generation (RAG), retrieval may provide insufficient or conflicting information needed to answer a question. The system should no

Ladders in Chaos: When, How, (and Perhaps Why) Does Test-Time Scaling Improve LLM Machine Translation

arXiv:2608.28496v1 Announce Type: new Abstract: Two forms of test-time scaling for Large Language Models (LLMs) have emerged as effective and widely adopted paradigms: sequential, in which later answe

researcharxiv-cs-cl

Landau theory of quenched criticality in linear in-context learning

arXiv:2608.28059v1 Announce Type: cross Abstract: In-context learning (ICL) allows a pretrained model to infer a new task from examples supplied in its prompt without updating its parameters. In linea

Agents

LandingAgent: A Reference-Annotated Dataset and Agentic Generation Framework for Landing Pages

arXiv:2608.27902v1 Announce Type: new Abstract: Landing pages are goal-oriented web interfaces that must communicate a target-specific value proposition while organizing information flow, visual hiera

agentsarxiv-cs-cl

Large Reasoning Models Struggle to Transfer Parametric Knowledge Across Scripts

arXiv:2603.17070v2 Announce Type: replace Abstract: In this work, we analyze shortcomings in cross-lingual knowledge transfer in large, modern reasoning LLMs. We demonstrate that the perceived gap in

local-aiarxiv-cs-cl

Layered LLM Defenses as an Ensemble: Access Tiers, Inference Cost, and the Measured Failure Correlation Between Defense Layers

arXiv:2608.28327v1 Announce Type: cross Abstract: Practitioners defend large language models (LLMs) by stacking defenses, assuming the layers compound. A stack is an ensemble, and ensembles compound o

researcharxiv-cs-cl

LayerRecall: A State-Conditioned Memory Router for Long-Horizon Consistency in Video Generation

arXiv:2608.28460v1 Announce Type: new Abstract: Autoregressive video diffusion enables scalable long-video generation by producing chunks from a bounded recent context. While recency-based caching pre

Learning a Single Token to Replace Long System Prompts in LLMs

model-releasesarxiv-cs-cl

arXiv:2511.23271v2 Announce Type: replace Abstract: Long system prompts are widely used to steer Large Language Models (LLMs), but repeatedly processing them at inference time is inefficient and consu

Learning a Size-Weight Frontier for Synthetic-Augmented Inference

arXiv:2608.28576v1 Announce Type: cross Abstract: Synthetic data can improve statistical inference when real data are scarce, but naively treating synthetic samples as real data can introduce bias and

Learning between the peaks: sharp asymptotics for kernel ridge regression under power-law anisotropy

arXiv:2608.28564v1 Announce Type: cross Abstract: We study kernel ridge regression under anisotropic Gaussian data, where the input covariance decays as a power law with exponent alphageq 0 for polyno

Learning Fast Monomial Orders for Grobner Basis Computations

arXiv:2602.02972v2 Announce Type: replace-cross Abstract: The efficiency of Grobner basis computation, the standard engine for solving systems of polynomial equations, depends on the choice of monomia

Learning the Target Priors Before Image Translation: A Decoupled Training Paradigm for Cross-Modal Image Translation in Remote Sensing

arXiv:2608.28517v1 Announce Type: new Abstract: Cross-modal image translation in remote sensing must preserve source-observed content while matching the target-domain distribution. Existing methods jo

Learning to Difference: Adaptive Reversible Differencing (AdaRDiff) for Time Series Forecasting

arXiv:2608.28134v1 Announce Type: new Abstract: Reliable long-horizon time series forecasting is an important yet difficult problem. Trends and seasonality introduce complex temporal structure that ch

Applications

Learning to Transfer Across Modes: Towards Unified Urban Mobility Forecasting

arXiv:2608.28273v1 Announce Type: new Abstract: Urban transportation systems consist of multiple mobility modes that coexist within the same city and exhibit complex interdependencies, leading to corr

applicationsarxiv-cs-lg

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

arXiv:2305.17010v4 Announce Type: replace Abstract: Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact algorithms, making them a tempting domain to apply machin

Leveraging a Foundation Model for the EEG-Based Diagnosis of Alzheimer's Disease

arXiv:2608.27719v1 Announce Type: new Abstract: Biological heterogeneity in Alzheimer's Disease (AD) poses a critical diagnostic challenge, particularly for traditional linear methods that fail to cap