All entries

Evidence-Grounded Forensic Reasoning for Detecting and Grounding Multi-Modal Media Manipulation

arXiv:2608.08009v1 Announce Type: cross Abstract: Fake news increasingly relies on cross-modal image-text forgeries, making transparent and verifiable reasoning chains an urgent need for Detecting and

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Clear filters

Evidence-RL: Towards Evidence-intensive Visual Reasoning

DGX agent

arXiv:2608.08021v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context.

local-aiarxiv-cs-ai

11 Aug 2026

Evo-Bench: Can Language Models Improve Agent Harness?

model-releasesarxiv-cs-cl

arXiv:2608.09096v1 Announce Type: new Abstract: Large Language Models (LLMs) have driven rapid progress in autonomous agents, yet standard evaluations remain confined to static task solving. An emergi

Evolving Safety Landscape of Multi-modal Large Language Models: A Survey of Emerging Threats and Safeguards

arXiv:2608.07535v1 Announce Type: cross Abstract: Multi-modal large language models (MLLMs) integrate heterogeneous modalities through modality alignment and fusion, enabling stronger understanding an

safetyarxiv-cs-ai

EvoTrustRAG: Evolution-Aware Conflict Attribution and Evidence Handling for Reliable Retrieval-Augmented Generation

model-releasesarxiv-cs-cl

arXiv:2608.07933v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) improves the factuality of large language models with external knowledge, yet conflicting evidence remains a fund

Applications

EvTrajGS: Accurate and Efficient 3D Gaussian Splatting from Unposed Event Streams

arXiv:2608.08585v1 Announce Type: new Abstract: Event cameras, with high temporal resolution, high dynamic range, and asynchronous sensing characteristics, have shown great potential for dense 3D reco

applicationsarxiv-cs-cv

Exact Contraction Rates via the Berkson--Porta Representation: A Sharp Threshold and Its Herglotz-Kernel Obstruction

model-releasesarxiv-cs-ro

arXiv:2608.07552v1 Announce Type: cross Abstract: Semigroups of holomorphic self-maps of the unit disc with an interior fixed point are, by the classical Berkson--Porta representation, entirely determ

Exact Network Surgery: Functional Invariance and Gradient Plasticity in Reactive Computational Graphs

arXiv:2607.16568v2 Announce Type: replace Abstract: Function-preserving network growth techniques such as Net2Net and progressive stacking expand a model's capacity without destroying its learned func

Exact Rank and Convex Calibration Dimension Lower Bounds for the Multi-Label F1 Loss

arXiv:2608.08399v1 Announce Type: new Abstract: The instance-wise F_1 measure is a central performance measure for multi-label classification. For a problem with s labels, it defines a 2^simes 2^s los

Exact Rank-Space KL Projection for Shared-Marginal Low-Rank Factors: Application to Doubly Stochastic Clustering

arXiv:2608.08642v1 Announce Type: new Abstract: We study exact Kullback--Leibler (KL) projection for low-rank factorizations whose two nonnegative factors have prescribed row marginals and a shared, l

Exact Zarankiewicz Values On Two Finite Frontier Slices

arXiv:2608.08154v1 Announce Type: cross Abstract: The Zarankiewicz number Z(m,n,s,t) is the maximum number of edges in a bipartite graph with parts of orders m and n containing no copy of Ks,t. We giv

Tutorials

Experience-Sensitive Game Learning: A Behavioral Study of Humans and Language Agents

arXiv:2608.07490v1 Announce Type: cross Abstract: Large language model agents are increasingly evaluated through games, but most benchmarks emphasize final outcomes rather than how players learn from

tutorialsarxiv-cs-ai

Expert-Guided Multimodal Fusion for Unified Emotion and Sentiment Analysis

arXiv:2601.07565v2 Announce Type: replace-cross Abstract: Multimodal emotion understanding requires the integration of heterogeneous data sources, including text, audio, and visual modalities, while s

Applications

Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems

arXiv:2403.09752v4 Announce Type: replace-cross Abstract: The Internet of Medical Things transcends traditional medical boundaries, enabling a transition from reactive treatment to proactive preventio

applicationsarxiv-cs-ai

Explainable Machine Learning in Healthcare: Methods, Interpretation, and Applications for Clinical Research

arXiv:2608.07522v1 Announce Type: cross Abstract: We present a structured review of commonly used Explainable machine learning (XML) methodologies, including global and local interpretability tools su

local-aiarxiv-cs-lg

Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings

arXiv:2603.26798v2 Announce Type: replace-cross Abstract: Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image-text embedding space,

safetyarxiv-cs-ai

Explicit Boundary Markers for Subword Vocabularies

arXiv:2608.08847v1 Announce Type: new Abstract: Subword tokenizers represent many common words twice in space-using writing systems, once with a leading space and once without. The two entries have se

researcharxiv-cs-cl

Exploiting chemical shift variability enables recovery of overlapping metabolites from 1H nuclear magnetic resonance spectra

arXiv:2608.07610v1 Announce Type: cross Abstract: Overlapping peaks and sample-dependent chemical shift variability prevent reliable metabolite recovery from complex biological spectra. This problem i

Explore, Map, Remember, Decide: Are Embodied VLMs Ready for Safety-Critical Scenarios?

arXiv:2608.08077v1 Announce Type: new Abstract: Theory of Space framework (ToS) assesses the spatial understanding of curiosity-driven Vision-Language Models (VLMs) under partial observability. As AI

safetyarxiv-cs-ai

Applications

Exploring LLM Capabilities for Situational Understanding and COLREG compliance on real-world maritime navigation scenarios

arXiv:2608.08281v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have shown considerable capability for situational understanding, reasoning, and decision making in different dom

applicationsarxiv-cs-ai

exttt{DisMorph}: learning to disentangle technical distortions from true biological change

arXiv:2608.08173v1 Announce Type: cross Abstract: Longitudinal MRI enables sensitive measurement of structural brain change for studying aging and neurodegenerative disease. Deformable image registrat

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting

arXiv:2608.09082v1 Announce Type: new Abstract: Spatiotemporal prediction on graph-structured data is central to traffic forecasting and environmental monitoring, yet decentralized and heterogeneous d

safetyarxiv-cs-lg

FactorDrive: Adaptive Multi-Step Reasoning Driven by Planning-Critical Factors for End-to-End Autonomous Driving

arXiv:2608.09591v1 Announce Type: cross Abstract: Vision-language models (VLMs) have advanced scene understanding and enabled explicit reasoning in end-to-end autonomous driving. However, existing met

safetyarxiv-cs-cv

Agents

FailForge: Distilling Procedural Competence from Persistent Failures into Code Agents

arXiv:2608.08570v1 Announce Type: new Abstract: Rejection sampling fine-tuning (RFT) is widely used to train code agents by generating trajectories on verifiable software engineering tasks, retaining

agentsarxiv-cs-ai

Failure-Aware Long-Form Translation: Design and Implementation of a Recoverable LLM Translation System

arXiv:2608.09187v1 Announce Type: new Abstract: A long-form translation request can succeed at the API layer and still produce an unusable result. The output may be empty, truncated, filtered, dominat

researcharxiv-cs-cl

Failure-Mechanism Transferability of Cumulative-Damage Features for Health State Estimation of SiC Power Modules

arXiv:2608.08365v1 Announce Type: cross Abstract: Data-driven health-state estimators for SiC (Silica-Carbide) power modules typically report their performance on a single accelerated-aging campaign,

Fair on the Surface? Benchmarking Hidden-Output Fairness Gaps in LLM Recommenders

arXiv:2608.08284v1 Announce Type: new Abstract: Fairness audits for LLM-based recommenders have largely focused on observable outputs, implicitly assuming that stable recommendations reflect stable in

Fairness in Link Prediction Beyond Demographic Parity: A Reproducibility Study

arXiv:2608.09899v1 Announce Type: new Abstract: In fair ranked link prediction, demographic parity (Delta_DP) is a common fairness metric. Yet, Mattos et al. (2025) argue that it fails to detect expos

safetyarxiv-cs-lg

FaLCon: Facet-Anchored Retrieval with Late Consensus for Sim2Real Text-Based Person Anomaly Search

model-releasesarxiv-cs-cv

arXiv:2608.09474v1 Announce Type: new Abstract: Text-based person anomaly search requires retrieving real-world pedestrian images from detailed natural-language descriptions using models trained prima

FanarGuard: A Culturally-Aware Moderation Filter for Arabic Language Models

model-releasesarxiv-cs-cl

arXiv:2511.18852v2 Announce Type: replace Abstract: Content moderation filters are a critical safeguard against alignment failures in language models. Yet most existing filters focus narrowly on gener

FEAST: Federated Shared-Space Training for Resource-Heterogeneous Clients

arXiv:2608.09250v1 Announce Type: new Abstract: Federated learning (FL) must serve devices with varying computational capabilities. A fixed model cannot suit all devices, while training one model per

FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning

arXiv:2608.09208v1 Announce Type: cross Abstract: Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL). Ho

Federated Attention Autoencoders with a Stochastic Aggregation Scheme for Anomaly Detection

arXiv:2608.08906v1 Announce Type: new Abstract: Outlier detection in decentralized data environments is a challenging task for many machine learning implementations, particularly in settings where dat

FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations

arXiv:2608.09687v1 Announce Type: new Abstract: Federated learning (FL) in Low Earth Orbit (LEO) satellite constellations is affected by non-IID data and irregular ground-station visibility, both driv

FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning

arXiv:2608.09221v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed client devices while preserving data privacy. However, FL faces signif

local-aiarxiv-cs-ai

FeedbackTrack: Visual-Cortex-Inspired Cross-Frame Feedback for Transformer Tracking

arXiv:2608.09369v1 Announce Type: cross Abstract: Visual object tracking requires effective temporal integration, yet most Transformer trackers still rely on predominantly feed-forward feature extract

FemWear: A Specialized Wearable Foundation Model for Women's Health

arXiv:2608.08244v1 Announce Type: new Abstract: General wearable foundation models are pretrained across broad sensor streams and populations, but are not designed around women's-health tasks. We intr

Financial Numerical Prediction and Allocation as Token Generation

arXiv:2608.09880v1 Announce Type: new Abstract: Financial prediction typically relies on task-specific regression, ranking, or policy heads, separating the language model from the numerical object ult

safetyarxiv-cs-cv

Findings of the First Teaching Monster Challenge: A Benchmark of Pedagogical Content Knowledge in AI Agents

arXiv:2608.08852v1 Announce Type: new Abstract: AI agents can now solve problems, answer like subject experts, and generate long-form multimodal content. However, whether they can adapt a lesson to fi

Finite basis physics-informed neural networks with hard constraints for viscous fluid flow in highly perforated domains

arXiv:2608.08114v1 Announce Type: cross Abstract: In this work, viscous fluid flow governed by the Stokes equations in highly perforated domains is studied using physics-informed neural networks (PINN

safetyarxiv-cs-lg

Finite Constant Frontiers and Auditable Regret Certificates for Average-Reward Reinforcement Learning

arXiv:2608.07725v1 Announce Type: new Abstract: Average-reward reinforcement-learning regret is known up to logarithmic factors, but the numerical content of published guarantees is difficult to compa

FiRe: Fixed-Noise Refinement for Visual Counterfactual Explanations

arXiv:2608.08664v1 Announce Type: new Abstract: Visual counterfactual explanations aim to change classifier decisions through realistic and localized edits while preserving decision-irrelevant content

local-aiarxiv-cs-cv

FitAQA: A Benchmark of Fitness Action Quality Assessment for Multimodal Large Language Models

arXiv:2608.08736v1 Announce Type: new Abstract: Fitness Action Quality Assessment (AQA) is important for intelligent sports training, yet the capabilities of Multimodal Large Language Models (MLLMs) i

Hardware

FlashRT: Agent Harness for Guiding Agents to Deploy Real-Time Multimodal Applications

arXiv:2607.18171v2 Announce Type: replace Abstract: Real-time multimodal applications, including voice agents and interactive video generation, compose heterogeneous models into pipelines whose effici

hardwarearxiv-cs-lg

FlexSplat: Flexible Feed-Forward 3D Gaussian Splatting without Point Cloud Correspondence

arXiv:2608.07937v1 Announce Type: new Abstract: We present FlexSplat, a feed-forward framework for novel view synthesis (NVS) from uncalibrated, object-centric multi-view image collections. A recent l

researcharxiv-cs-cv

Floating-Point Neural Network Verification at the Software Level

arXiv:2510.23389v2 Announce Type: replace-cross Abstract: The behaviour of neural network components must be proven correct before deployment in safety-critical systems. Unfortunately, existing neural

Flow-based conditional cardiac anatomy generation for virtual cohorts

arXiv:2608.09460v1 Announce Type: cross Abstract: Cardiac digital twin research is moving from subject-specific anatomical replicas toward virtual cohorts that represent clinically relevant population

researcharxiv-cs-cv

Flow-by-Flow:Content-Judgment Bypass for Governing AI Output in High-Loss Domains

arXiv:2608.07474v1 Announce Type: new Abstract: Prior work showed that human-in-the-loop oversight becomes structurally untenable in high-loss domains when AI output velocity V exceeds human cognitive