Search: “evaluation”

How Sensitive Are Safety Benchmarks to Judge Configuration Choices?

arXiv:2604.24074v1 Announce Type: new Abstract: Safety benchmarks such as HarmBench rely on LLM judges to classify model responses as harmful or safe, yet the judge configuration, namely the combinati

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Human-AI Governance (HAIG): A Trust-Utility Approach

DGX agent

arXiv:2505.01651v4 Announce Type: replace Abstract: This paper introduces the Human-AI Governance (HAIG) framework, contributing to the AI Governance (AIG) field by foregrounding the relational dynami

agentsarxiv-cs-ai

28 Apr 2026

Hybrid JIT-CUDA Graph Optimization for Low-Latency Large Language Model Inference

arXiv:2604.23467v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance across natural language and multimodal tasks, yet their practical deployment remains con

Industry

I post-trained Qwen3-Coder to fix bugs using an actual debugger. The result: Solve rate: 70% → 89% Median turns to fix: 46 → 19 (-59%) Inste…

I post-trained Qwen3-Coder to fix bugs using an actual debugger. The result: Solve rate: 70% → 89% Median turns to fix: 46 → 19 (-59%) Instead of just reading code or print-debugging, it: - reasons fr

industryclem-delangue--x

ImmerIris: A Large-Scale Dataset and Benchmark for Off-Axis and Unconstrained Iris Recognition in Immersive Applications

model-releasesarxiv-cs-cv

arXiv:2510.10113v3 Announce Type: replace Abstract: Recently, iris recognition is regaining prominence in immersive applications such as extended reality as a means of seamless user identification. Th

IMPA-Net: Meteorology-Aware Multi-Scale Attention and Dynamic Loss for Extreme Convective Radar Nowcasting

model-releasesarxiv-cs-lg

arXiv:2604.24224v1 Announce Type: new Abstract: Short-range prediction of convective precipitation from weather radar observations is essential for severe weather warnings. However, deep learning mode

Indirect Question Answering in English, German and Bavarian: A Challenging Task for High- and Low-Resource Languages Alike

arXiv:2603.15130v2 Announce Type: replace Abstract: Indirectness is a common feature of daily communication, yet is underexplored in NLP research for both low-resource as well as high-resource languag

INHerit-SG: Incremental Hierarchical Semantic Scene Graphs with RAG-Style Retrieval

model-releasesarxiv-cs-ro

arXiv:2602.12971v2 Announce Type: replace Abstract: Driven by recent advancements in foundation models, semantic scene graphs have emerged as a promising paradigm for high-level 3D environmental abstr

INSIGHT: Indoor Scene Intelligence from Geometric-Semantic Hierarchy Transfer for Public~Safety

arXiv:2604.23095v1 Announce Type: new Abstract: Indoor environments lack the spatial intelligence infrastructure that GPS provides outdoors; first responders arriving at unfamiliar buildings typically

Interoceptive machine framework: Toward interoception-inspired regulatory architectures in artificial intelligence

arXiv:2604.24527v1 Announce Type: new Abstract: This review proposes an integrative framework grounded on interoception and embodied AI-termed the interoceptive machine framework-that translates biolo

researcharxiv-cs-ai

Into the Omniverse: Manufacturing’s Simulation-First Era Has Arrived

Manufacturing is shifting from relying solely on real-world testing, as high-fidelity simulation now produces synthetic training data accurate enough for production-grade AI. NVIDIA Omniverse librarie

applicationsnvidia-blog

IntrAgent: An LLM Agent for Content-Grounded Information Retrieval through Literature Review

arXiv:2604.22861v1 Announce Type: cross Abstract: Scientific research relies on accurate information retrieval from literature to support analytical decisions. In this work, we introduce a new task, I

Jailbreaking Frontier Foundation Models Through Intention Deception

arXiv:2604.24082v1 Announce Type: cross Abstract: Large (vision-)language models exhibit remarkable capability but remain highly susceptible to jailbreaking. Existing safety training approaches aim to

KLong: Training LLM Agent for Extremely Long-horizon Tasks

arXiv:2602.17547v3 Announce Type: replace Abstract: This paper introduces KLong, an open-source LLM agent trained to solve extremely long-horizon tasks. The principle is to first cold-start the model

Knee-xRAI: An Explainable AI Framework for Automatic Kellgren-Lawrence Grading of Knee Osteoarthritis

arXiv:2604.23435v1 Announce Type: cross Abstract: Radiographic grading of knee osteoarthritis (KOA) with the Kellgren-Lawrence (KL) system is limited by inter-reader variability and the opacity of cur

researcharxiv-cs-ai

Large language model-enabled automated data extraction for concrete materials informatics

arXiv:2604.22938v1 Announce Type: cross Abstract: The promise of data-driven materials discovery remains constrained by the scarcity of large, high-quality, and accessible experimental datasets. Here,

Lattice-to-Total Thermal Conductivity Ratio: A Phonon-Glass Electron-Crystal Descriptor for Data-Driven Thermoelectric Design

arXiv:2511.21213v2 Announce Type: replace-cross Abstract: Thermoelectrics (TEs) are promising candidates for energy harvesting with performance quantified by figure of merit, ZT. To accelerate the dis

applicationsarxiv-cs-lg

Layerwise Convergence Fingerprints for Runtime Misbehavior Detection in Large Language Models

arXiv:2604.24542v1 Announce Type: cross Abstract: Large language models deployed at runtime can misbehave in ways that clean-data validation cannot anticipate: training-time backdoors lie dormant unti

Learning-Augmented Robust Algorithmic Recourse

arXiv:2410.01580v3 Announce Type: replace Abstract: Algorithmic recourse provides individuals who receive undesirable outcomes from machine learning systems with minimum-cost improvements to achieve a

researcharxiv-cs-lg

Learning Evidence of Depression Symptoms via Prompt Induction

arXiv:2604.24376v1 Announce Type: new Abstract: Depression places substantial pressure on mental health services, and many people describe their experiences outside clinical settings in high-volume us

Learning from Demonstration with Failure Awareness for Safe Robot Navigation

arXiv:2604.23360v1 Announce Type: new Abstract: Learning from demonstration is widely used for robot navigation, yet it suffers from a fundamental limitation: demonstrations consist predominantly of s

safetyarxiv-cs-ro

Learning from Imperfect Text Guidance: Robust Long-Tail Visual Recognition with High-Noise Label

arXiv:2604.23125v1 Announce Type: new Abstract: Real-world data often exhibit long-tailed distributions with numerous noisy labels, substantially degrading the performance of deep models. While prior

Tutorials

Learning Scene-Level Signed Directional Distance Function with Ellipsoidal Priors and Neural Residuals

arXiv:2503.20066v2 Announce Type: replace-cross Abstract: Dense reconstruction and differentiable rendering are fundamental tightly connected operations in 3D vision and computer graphics. Recent neur

tutorialsarxiv-cs-cv

Learning Selective LLM Autonomy from Copilot Feedback in Enterprise Customer Support Workflows

arXiv:2604.23855v1 Announce Type: new Abstract: We present a deployed system that automates end-to-end customer support workflows inside an enterprise Business Process Management (BPM) platform. The a

safetyarxiv-cs-cl

Learning to Rotate: Temporal and Semantic Rotary Encoding for Sequential Modeling

arXiv:2604.24717v1 Announce Type: new Abstract: Every Transformer architecture dedicates enormous capacity to learning rich representations in semantic embedding space -- yet the rotation manifold act

applicationsarxiv-cs-ai

Learning Under Low Illumination: A Dataset and Algorithm for Traffic Sign Recognition

model-releasesarxiv-cs-cv

arXiv:2511.17183v2 Announce Type: replace Abstract: Traffic signboards are vital for road safety and intelligent transportation systems, enabling navigation and autonomous driving. Yet, recognizing tr

Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks

arXiv:2604.22784v1 Announce Type: new Abstract: State estimation is a cornerstone of power system control-center operations, and its robust operation is increasingly a cyber-physical security concern

researcharxiv-cs-lg

LEGO: An LLM Skill-Based Front-End Design Generation Platform

arXiv:2604.23355v1 Announce Type: new Abstract: Existing LLM-based EDA agents are often isolated task-specific systems. This leads to repeated engineering effort and limited reuse of successful design

Less is More in Semantic Space: Intrinsic Decoupling via Clifford-M for Fundus Image Classification

arXiv:2603.20806v2 Announce Type: replace Abstract: Multi-label fundus diagnosis requires features that capture both fine-grained lesions and large-scale retinal structure. Many multi-scale medical vi

Leveraging Spatial Transcriptomics as Alternative to Manual Annotations for Deep Learning-Based Nuclei Analysis

arXiv:2604.23481v1 Announce Type: new Abstract: Deep learning-based nuclei segmentation and classification in pathology images typically rely on large-scale pixel-level manual annotations, which are c

researcharxiv-cs-cv

Light 'em Up: Enabling Few-Shot Low-Light 3D Gaussian Splatting with Multi-Scale Explicit Retinex Illumination Decoupling

arXiv:2604.24053v1 Announce Type: new Abstract: Full 360^irc novel view synthesis under low-light conditions remains challenging. Insufficient illumination, noise amplification, and view-dependent pho

researcharxiv-cs-cv

LLM-Auction: Generative Auction towards LLM-Native Advertising

arXiv:2512.10551v2 Announce Type: replace-cross Abstract: The commercialization of LLM applications is the next frontier in online advertising, with LLM-native advertising emerging as a promising para

safetyarxiv-cs-ai

LLM-Augmented Traffic Signal Control with LSTM-Based Traffic State Prediction and Safety-Constrained Decision Support

arXiv:2604.23902v1 Announce Type: new Abstract: Traffic signal control is a critical task in intelligent transportation systems, yet conventional fixed-time and rule-based methods often struggle to ad

safetyarxiv-cs-ai

LLMind: Bio-inspired Training-free Adaptive Visual Representations for Vision-Language Models

arXiv:2603.14882v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) typically assume a uniform spatial fidelity across the entire field of view of visual inputs, dedicating equal precisi

researcharxiv-cs-cv

LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory

arXiv:2603.03269v2 Announce Type: replace Abstract: Feedforward geometric foundation models achieve strong short-window reconstruction, yet scaling them to minutes-long videos is bottlenecked by quadr

Long-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling

arXiv:2604.24715v1 Announce Type: new Abstract: Hybrid sequence models that combine efficient Transformer components with linear sequence modeling blocks are a promising alternative to pure Transforme

LongFlow: Efficient KV Cache Compression for Reasoning Models

arXiv:2603.11504v2 Announce Type: replace-cross Abstract: Recent reasoning models such as OpenAI-o1 and DeepSeek-R1 have shown strong performance on complex tasks including mathematical reasoning and

Looking for the Bottleneck in Fine-grained Temporal Relation Classification

arXiv:2604.24620v1 Announce Type: new Abstract: Temporal relation classification is the task of determining the temporal relation between pairs of temporal entities in a text. Despite recent advanceme

Lost in Decoding? Reproducing and Stress-Testing the Look-Ahead Prior in Generative Retrieval

arXiv:2604.23396v1 Announce Type: cross Abstract: Generative retrieval (GR) ranks documents by autoregressively generating document identifiers. Because many GR methods rely on trie-constrained beam s

Machine learning models for estimating counterfactuals in a single-arm inflammatory bowel disease study

arXiv:2604.23465v1 Announce Type: new Abstract: Single-arm trials accelerate study timelines by reducing the number of patients that must be recruited for a concurrent control group. However, these de

researcharxiv-cs-lg

Magnetic Indoor Localization through CNN Regression and Rotation Invariance

arXiv:2604.22896v1 Announce Type: cross Abstract: Indoor positioning is an essential technology for a wide range of applications in GNSS-denied environments, including indoor navigation and IoT system

safetyarxiv-cs-lg

Mapping License Plate Recoverability Under Extreme Viewing Angles for Oppor-tunistic Urban Sensing

arXiv:2604.23814v1 Announce Type: cross Abstract: Urban environments contain many imaging sensors built for specific purposes, including ATM, body-worn, CCTV, and dashboard cameras. Under the opportun

Agents

Measuring Successful Cooperation in Human-AI Teamwork: Development and Validation of the Perceived Cooperativity and Teaming Perception Scales

arXiv:2604.24461v1 Announce Type: cross Abstract: As human-AI cooperation becomes increasingly prevalent, reliable instruments for assessing the subjective quality of cooperative human-AI interaction

agentsarxiv-cs-ai

Measuring Temporal Linguistic Emergence in Diffusion Language Models

arXiv:2604.23235v1 Announce Type: new Abstract: Diffusion language models expose an explicit denoising trajectory, making it possible to ask when different kinds of information become measurable durin

MEMCoder: Multi-dimensional Evolving Memory for Private-Library-Oriented Code Generation

arXiv:2604.24222v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at general code generation, but their performance drops sharply in enterprise settings that rely on internal privat

MIRAGE: A Micro-Interaction Relational Architecture for Grounded Exploration in Multi-Figure Artworks

arXiv:2604.23788v1 Announce Type: new Abstract: Appreciating multi-figure paintings requires understanding how characters relate through subtle cues like gaze alignment, gesture, and spatial arrangeme

Mixture of Heterogeneous Grouped Experts for Language Modeling

arXiv:2604.23108v1 Announce Type: cross Abstract: Large Language Models (LLMs) based on Mixture-of-Experts (MoE) are pivotal in industrial applications for their ability to scale performance efficient

ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs

arXiv:2604.23053v1 Announce Type: new Abstract: Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization. As solving large-scale combinatorial

applicationsarxiv-cs-lg