Search: “evaluation”

HAF: Adapting Generalist VLAs to Humanoid Whole-Body Loco-manipulation via Hierarchical Action Flow and Spectral Latent RL

arXiv:2608.16837v1 Announce Type: cross Abstract: Humanoid robots hold great promise as general-purpose agents in human-centered environments, yet generalist vision-language-action (VLA) foundation mo

Human

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Safety

Hallucination Span Detection with Input-Side Evidence Alignment

DGX agent

arXiv:2608.15804v1 Announce Type: new Abstract: Hallucinations remain a major obstacle to the reliable use of large language models (LLMs) in conditional text generation. Existing methods primarily as

safetyarxiv-cs-cl

18 Aug 2026

Handover Analysis for Vehicular Communication with Explainability on the Fly

arXiv:2608.14820v1 Announce Type: cross Abstract: Handover (HO) management in vehicular networks requires fast and reliable decision-making under highly dynamic conditions. While machine learning (ML)

applicationsarxiv-cs-ai

Hardware-in-the-Loop Phase-Aware CNN for Real-Time 5G Channel Estimation

arXiv:2608.14709v1 Announce Type: cross Abstract: This demo presents real-time AI-based uplink channel-estimation inference using data collected from a hardware-in-the-loop 5G platform. The data-colle

researcharxiv-cs-cv

HaReCAP: Habitual-action Grounding for Recursive Large Language Model Agents

arXiv:2608.16447v1 Announce Type: new Abstract: Long-horizon embodied tasks require LLM agents to iteratively decompose high-level goals, revise plans in response to environmental feedback, and ground

applicationsarxiv-cs-ai

HarmTrace: Anchor-Calibrated Decoupled Optimization for Fine-Grained Target Identification in Harmful Memes

arXiv:2608.16622v1 Announce Type: cross Abstract: Multimodal harmful meme detection is typically formulated as image--text harmfulness classification. A model may correctly predict harmfulness while m

Agents

Hierarchical Agentic Incident Response with Digital-Twin-Validated Attack Inference

arXiv:2608.15016v1 Announce Type: cross Abstract: Network incident response remains slow and labor-intensive as the defender must infer multi-stage attacks from partial observations and translate reco

agentsarxiv-cs-ai

HistReNeRF: Historic Image Relocalisation within Contemporary Neural Radiance Field Reconstructions

arXiv:2608.15420v1 Announce Type: new Abstract: Relocalising archival photographs within a contemporary scene model is challenging because historic and modern views can differ in photographic appearan

researcharxiv-cs-cv

Hoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning

arXiv:2608.16659v1 Announce Type: cross Abstract: Ensembles of decision trees are well-established methods for data stream classification. In ensemble learning, Hoeffding Trees are widely adopted as b

Hardware

How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit

NVIDIA’s ALCHEMI Toolkit delivers composable, PyTorch‑native GPU‑accelerated workflows for machine learning interatomic potentials (MLIPs), enabling AI coding agents to generate robust simulation code

hardwarenvidia-developer

How Box is unlocking multimodal enterprise agents with Gemini Embeddings 2

model-releasesgoogle-cloud-ai

Enterprise content management is experiencing its biggest architectural shift since the cloud migration era. For years, enterprises have stored trillions of gigabytes of critical data in Box: financia

How Sampling Strategy Affects Imbalance Mitigation in LiDAR Segmentation: A Study of Structured vs. Random Point-Based Architectures

model-releasesarxiv-cs-cv

arXiv:2608.16673v1 Announce Type: new Abstract: Class imbalance in LiDAR point clouds poses challenges for semantic segmentation in autonomous navigation and urban mapping. While 2D vision has numerou

Tutorials

Human Pose Estimation in Trampoline Gymnastics: How to Improve Performance on Extreme Poses

arXiv:2604.01322v2 Announce Type: replace Abstract: Trampoline gymnastics involves extreme human poses and uncommon viewpoints, on which state-of-the art pose estimation models tend to under-perform.

tutorialsarxiv-cs-cv

Hybrid quantum recurrent neural network for remaining useful life prediction of turbofan engines

arXiv:2504.20823v3 Announce Type: replace Abstract: Accurate remaining useful life (RUL) estimation underpins safe operation and cost-effective maintenance of aerospace propulsion systems. We propose

Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinement

arXiv:2608.16628v1 Announce Type: new Abstract: Modern Multimodal Retrieval-Augmented Generation (M-RAG) systems are fundamentally limited by the binary connectivity paradigm of traditional simple gra

Identifying Confusion Trends in Concept-based XAI for Multi-Label Classification

arXiv:2608.15731v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) deployed in high-risk domains, such as healthcare and autonomous driving, must be not only accurate but also understandabl

If alignment issues are becoming big enough that OpenAI is willing to commit 20% of research inference compute to chain-of-thought monitorin…

If alignment issues are becoming big enough that OpenAI is willing to commit 20% of research inference compute to chain-of-thought monitoring, that suggests that alignment issues are becoming a pretty

safetyethan-mollick--x

iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration

arXiv:2608.14646v1 Announce Type: cross Abstract: Interpretable representation learning remains a key challenge in modern neural computation, particularly when models are expected not only to perform

In Defense of OCTA: The Reconstruction-Utility Gap in OCT-to-OCTA Synthesis

arXiv:2608.15626v1 Announce Type: new Abstract: Optical coherence tomography angiography (OCTA) images retinal blood flow, giving capillary-perfusion and foveal-avascular-zone biomarkers that grade di

researcharxiv-cs-lg

Incoherent by Design? On the Moral Self-Consistency of LLMs

arXiv:2608.15354v1 Announce Type: new Abstract: LLMs are increasingly used in morally sensitive contexts, yet it is unclear whether they apply ethical principles consistently across situations. A mode

IndicQE-APE: A Benchmark for Quality Estimation and Automatic Post-Editing for Indic Languages

model-releasesarxiv-cs-cl

arXiv:2608.16344v1 Announce Type: new Abstract: Indic quality estimation (QE) and automatic post-editing (APE) data is spread across separate releases, so no single resource supports training and eval

Inference-Time Mitigation of Adversarial Political Bias in Large Language Models

arXiv:2608.14629v1 Announce Type: cross Abstract: As Large Language Models (LLMs) become the mainstay for information retrieval and summarization tasks, ensuring that they are always non-partisan and

INSPIRE: A Benchmark for Instruction-Aware Speech Retrieval

model-releasesarxiv-cs-cl

arXiv:2608.16203v1 Announce Type: cross Abstract: Existing speech retrieval systems rely on fixed similarity matching and cannot adapt to diverse user intents. We introduce INSPIRE, the first benchmar

Invariant Pretraining for Robust Code Representations

arXiv:2608.15412v1 Announce Type: cross Abstract: Encoder-based code representation models remain widely deployed for discriminative tasks such as clone detection and code classification, where their

Iterative Refinement Diffusion for Super-Resolved Data Assimilation of Multiscale Physical Systems

arXiv:2608.14744v1 Announce Type: new Abstract: Recovering high-resolution states from sparse, low-resolution observations is a central challenge in scientific machine learning and data assimilation.

KeyID: Decoupled Drafting and Keyframe Editing for Identity-Preserving Video Generation

model-releasesarxiv-cs-cv

arXiv:2608.16154v1 Announce Type: new Abstract: Identity-preserving video generation (IPVG) requires synthesizing videos that are faithful to both reference subjects and text prompts. Existing methods

Language-Guided Generation for Personalized Inspection Planning

arXiv:2506.02917v2 Announce Type: replace Abstract: We propose a training-free, Vision-Language Model (VLM)-guided approach for efficiently generating trajectories to facilitate target inspection plan

applicationsarxiv-cs-ro

Agents

LAPF: LLM-Agent-Based Path Finder Using the UAVScenes Dataset

arXiv:2608.15175v1 Announce Type: cross Abstract: Uncrewed aerial vehicles (UAVs) are increasingly deployed for autonomous navigation in complex outdoor environments, where dynamic conditions and miss

agentsarxiv-cs-ai

Large language model-assisted discovery of cohorts from scientific literature

arXiv:2608.15909v1 Announce Type: cross Abstract: Background: Planning multi-study analyses requires identifying cohorts with the relevant participants, phenotypes, and data modalities. This process c

researcharxiv-cs-cl

Large Language Models as Implicit Sociological Models: Reconstructing Voting Behaviour from Sociodemographic Profiles

arXiv:2608.15871v1 Announce Type: cross Abstract: Large language models (LLMs) trained on large-scale internet corpora encode extensive statistical regularities about social identities, attitudes, and

researcharxiv-cs-cl

LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing

arXiv:2608.16763v1 Announce Type: new Abstract: Financial document validation in production, such as payroll auditing, tax compliance, and loan underwriting, demands exceptional accuracy, consistency,

Learning Auditable Classifier Models: Source-Disjoint Tree Ensembles

arXiv:2608.15725v1 Announce Type: new Abstract: Predictive models in clinical and regulated settings must be accurate and fully auditable. Tree ensembles deliver strong accuracy on tabular data, but t

researcharxiv-cs-lg

Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learning

arXiv:2608.15770v1 Announce Type: new Abstract: Designing effective trading strategies using reinforcement learning remains challenging due to delayed and noisy rewards, poor exploration, and the diff

safetyarxiv-cs-lg

Learning Varying Physical Therapist-Patient Interactions for Robot-mediated Upper Limb Task-Specific Training

arXiv:2608.15995v1 Announce Type: cross Abstract: Upper extremity motor function recovery is positively linked to Task-Specific Training (TST) and sufficient therapy dosage. Rehabilitation robots can

Local Ai

LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents

arXiv:2608.16185v1 Announce Type: cross Abstract: LLM agents increasingly answer questions over dynamic raw-document collections, where files may change before preprocessing, and relevant evidence (sp

local-aiarxiv-cs-ai

Lesioned Multimodal Language Models Reproduce Aphasic Picture-Naming Patterns

arXiv:2607.11621v2 Announce Type: replace Abstract: Aphasia following stroke commonly produces systematic naming errors with characteristic profiles, but whether general-purpose language models not de

Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization

arXiv:2608.16539v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) have made rapid progress on standardized benchmarks, yet their deployment in practical media workflows, curation,

Agents

LLM-based Framework for Generating and Verifying Parallel DEVS Statecharts

arXiv:2608.14956v1 Announce Type: new Abstract: The development of models demands sound modeling and simulation knowledge as well as domain knowledge. Every model should accurately represent a system'

agentsarxiv-cs-lg

LLMs Can Predict Failure Risk, But Struggle to Predict Which Collaboration Protocol Pays Off: Cost-Aware Protocol Routing Across Reasoning Tasks

arXiv:2608.14927v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems can improve reasoning by spending more computation, but deployment requires deciding when extra collabora

LLMs for Zero-Shot Threat Detection via Structured Risk Indicators

arXiv:2608.16508v1 Announce Type: cross Abstract: We propose a two-stage large language model (LLM) framework for zero-shot detection of insider threats and advanced persistent threats (APTs) from het

Tutorials

LLMs Get Smarter from Targeted Synthetic Multilingual Data

arXiv:2608.15964v1 Announce Type: cross Abstract: Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt. In other

tutorialsarxiv-cs-ai

Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study

arXiv:2608.14578v1 Announce Type: new Abstract: Early identification of adolescent substance-use risk is an important prevention challenge, yet the relative value of baseline characteristics, longitud

Looks Can be Deceiving: Annotator and Reviewer Performance Across Imagery Sources in Crowd-Sourced Aerial Damage Assessment

arXiv:2608.14942v1 Announce Type: cross Abstract: This paper presents the first known empirical investigation of annotator and reviewer performance across multi-source remotely sensed imagery, evaluat

Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling

arXiv:2608.15465v1 Announce Type: cross Abstract: Identification of IoT device types from passive traffic is increasingly used for security management in enterprise and ISP networks. However, the perf

applicationsarxiv-cs-lg

Making two action heads agree: coordination mechanisms and a runtime collapse certificate for flow-matching policies

arXiv:2608.15748v1 Announce Type: new Abstract: A dual-representation flow-matching policy decodes each predicted motion into joint and end-effector spaces, and the residual between the two kinematica

safetyarxiv-cs-ro

Marker-Constrained Pose-Graph Correction for Cross-Platform Georeferencing in GNSS-Denied Environments

arXiv:2608.16281v1 Announce Type: new Abstract: Autonomous operation in GNSS-denied environments requires heterogeneous mapping pipelines to maintain a consistent spatial reference. This paper present

safetyarxiv-cs-ro

MatchingPolicy: Correspondence-Aware Policy Enables Cross-Object In-Context Learning

arXiv:2608.16715v1 Announce Type: new Abstract: In-context imitation learning enables few-shot policy generalization but struggles to maintain performance on unseen objects and novel scenarios. To add

safetyarxiv-cs-ro

Max-Q Selective Imitation for Human-in-the-Loop Online Robot Learning

arXiv:2608.15088v1 Announce Type: cross Abstract: Human-in-the-loop (HIL) online reinforcement learning for real robots must absorb human interventions quickly while continuing to improve beyond the h