Search: “deployment”

AI generates well-liked but templatic empathic responses

arXiv:2604.08479v1 Announce Type: new Abstract: Recent research shows that greater numbers of people are turning to Large Language Models (LLMs) for emotional support, and that people rate LLM respons

applicationsarxiv-cs-cl

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Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference

DGX agent

arXiv:2604.08133v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) has become a dominant architecture for scaling large language models due to their sparse activation mechanism. However, the s

model-releasesarxiv-cs-cl

10 Apr 2026

An empirical study of LoRA-based fine-tuning of large language models for automated test case generation

arXiv:2604.06946v1 Announce Type: cross Abstract: Automated test case generation from natural language requirements remains a challenging problem in software engineering due to the ambiguity of requir

Broken by Default: A Formal Verification Study of Security Vulnerabilities in AI-Generated Code

arXiv:2604.05292v2 Announce Type: replace-cross Abstract: AI coding assistants are now used to generate production code in security-sensitive domains, yet the exploitability of their outputs remains u

Can Vision Language Models Judge Action Quality? An Empirical Evaluation

arXiv:2604.08294v1 Announce Type: cross Abstract: Action Quality Assessment (AQA) has broad applications in physical therapy, sports coaching, and competitive judging. Although Vision Language Models

Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutions

arXiv:2604.06435v1 Announce Type: cross Abstract: Visual Anomaly Detection (VAD) is a critical task for many applications including industrial inspection and healthcare. While VAD has been extensively

Cost-Efficient Multi-Scale Fovea for Semantic-Based Visual Search Attention

arXiv:2604.03836v2 Announce Type: replace-cross Abstract: Semantics are one of the primary sources of top-down preattentive information. Modern deep object detectors excel at extracting such valuable

DHFP-PE: Dual-Precision Hybrid Floating Point Processing Element for AI Acceleration

arXiv:2604.04507v2 Announce Type: replace-cross Abstract: The rapid adoption of low-precision arithmetic in artificial intelligence and edge computing has created a strong demand for energy-efficient

researcharxiv-cs-ro

Do We Need Distinct Representations for Every Speech Token? Unveiling and Exploiting Redundancy in Large Speech Language Models

arXiv:2604.06871v1 Announce Type: cross Abstract: Large Speech Language Models (LSLMs) typically operate at high token rates (tokens/s) to ensure acoustic fidelity, yet this results in sequence length

researcharxiv-cs-ai

Drift-Based Policy Optimization: Native One-Step Policy Learning for Online Robot Control

arXiv:2604.03540v2 Announce Type: replace Abstract: Although multi-step generative policies achieve strong performance in robotic manipulation by modeling multimodal action distributions, they require

safetyarxiv-cs-ro

Dual-Pool Token-Budget Routing for Cost-Efficient and Reliable LLM Serving

arXiv:2604.08075v1 Announce Type: new Abstract: Production vLLM fleets typically provision each instance for the worst-case context length, leading to substantial KV-cache over-allocation and under-ut

EchoTorrent: Towards Swift, Sustained, and Streaming Multi-Modal Video Generation

arXiv:2602.13669v4 Announce Type: replace Abstract: Recent multi-modal video generation models have achieved high visual quality, but their prohibitive latency and limited temporal stability hinder re

ESOM: Efficiently Understanding Streaming Video Anomalies with Open-world Dynamic Definitions

arXiv:2604.07772v1 Announce Type: new Abstract: Open-world video anomaly detection (OWVAD) aims to detect and explain abnormal events under different anomaly definitions, which is important for applic

FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs

model-releasesarxiv-cs-lg

arXiv:2604.06297v1 Announce Type: cross Abstract: Given the growing reliance on private data in training Large Language Models (LLMs), Federated Learning (FL) combined with Parameter-Efficient Fine-Tu

Fighting AI with AI: AI-Agent Augmented DNS Blocking of LLM Services during Student Evaluations

arXiv:2604.02360v1 Announce Type: cross Abstract: The transformative potential of large language models (LLMs) in education, such as improving accessibility and personalized learning, is being eclipse

Hardware

Foundry: Template-Based CUDA Graph Context Materialization for Fast LLM Serving Cold Start

arXiv:2604.06664v1 Announce Type: cross Abstract: Modern LLM service providers increasingly rely on autoscaling and parallelism reconfiguration to respond to rapidly changing workloads, but cold-start

hardwarearxiv-cs-lg

From Classical Machine Learning to Tabular Foundation Models: An Empirical Investigation of Robustness and Scalability Under Class Imbalance in Emergency and Critical Care

arXiv:2512.21602v2 Announce Type: replace-cross Abstract: Millions of patients pass through emergency departments and intensive care units each year, where clinicians must make high-stakes decisions u

From experimentation to engagement: on the paradox of participatory AI and power in contexts of forced displacement and humanitarian crises

arXiv:2604.06219v1 Announce Type: cross Abstract: Across the Global North, calls for participatory artificial intelligence (AI) to improve the responsible, safe, and ethical use of AI have increased,

safetyarxiv-cs-ai

Hardware

Full State-Space Visualisation of the 8-Puzzle: Feasibility, Design, and Educational Use

arXiv:2604.06186v1 Announce Type: cross Abstract: Search algorithms are a foundational topic in artificial intelligence education, yet even simple domains can generate large state spaces that challeng

hardwarearxiv-cs-ai

Graph Neural Networks for Misinformation Detection: Performance-Efficiency Trade-offs

arXiv:2604.08131v1 Announce Type: new Abstract: The rapid spread of online misinformation has led to increasingly complex detection models, including large language models and hybrid architectures. Ho

HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment

arXiv:2604.08435v1 Announce Type: new Abstract: It remains challenging to assess driver fatigue from untrimmed videos under constrained computational budgets, due to the difficulty of modeling long-ra

HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents

arXiv:2604.07430v1 Announce Type: new Abstract: We introduce HY-Embodied-0.5, a family of foundation models specifically designed for real-world embodied agents. To bridge the gap between general Visi

KEO: Knowledge Extraction on OMIn via Knowledge Graphs and RAG for Safety-Critical Aviation Maintenance

arXiv:2510.05524v2 Announce Type: replace Abstract: We present Knowledge Extraction on OMIn (KEO), a domain-specific knowledge extraction and reasoning framework with large language models (LLMs) in s

Knowledge Graphs Generation from Cultural Heritage Texts: Combining LLMs and Ontological Engineering for Scholarly Debates

arXiv:2511.10354v1 Announce Type: cross Abstract: Cultural Heritage texts contain rich knowledge that is difficult to query systematically due to the challenges of converting unstructured discourse in

Tutorials

Learning is Forgetting: LLM Training As Lossy Compression

arXiv:2604.07569v1 Announce Type: cross Abstract: Despite the increasing prevalence of large language models (LLMs), we still have a limited understanding of how their representational spaces are stru

tutorialsarxiv-cs-cl

LLM-Based Data Generation and Clinical Skills Evaluation for Low-Resource French OSCEs

arXiv:2604.08126v1 Announce Type: new Abstract: Objective Structured Clinical Examinations (OSCEs) are the standard method for assessing medical students' clinical and communication skills through str

applicationsarxiv-cs-cl

Lost in the Hype: Revealing and Dissecting the Performance Degradation of Medical Multimodal Large Language Models in Image Classification

arXiv:2604.08333v1 Announce Type: new Abstract: The rise of multimodal large language models (MLLMs) has sparked an unprecedented wave of applications in the field of medical imaging analysis. However

MedRoute: RL-Based Dynamic Specialist Routing in Multi-Agent Medical Diagnosis

arXiv:2604.06180v1 Announce Type: cross Abstract: Medical diagnosis using Large Multimodal Models (LMMs) has gained increasing attention due to capability of these models in providing precise diagnose

agentsarxiv-cs-lg

MedVR: Annotation-Free Medical Visual Reasoning via Agentic Reinforcement Learning

arXiv:2604.08203v1 Announce Type: new Abstract: Medical Vision-Language Models (VLMs) hold immense promise for complex clinical tasks, but their reasoning capabilities are often constrained by text-on

MemReader: From Passive to Active Extraction for Long-Term Agent Memory

arXiv:2604.07877v1 Announce Type: new Abstract: Long-term memory is fundamental for personalized and autonomous agents, yet populating it remains a bottleneck. Existing systems treat memory extraction

safetyarxiv-cs-cl

Mina: A Multilingual LLM-Powered Legal Assistant Agent for Bangladesh for Empowering Access to Justice

arXiv:2511.08605v3 Announce Type: replace Abstract: Bangladesh's low-income population faces major barriers to affordable legal advice due to complex legal language, procedural opacity, and high costs

agentsarxiv-cs-cl

Mitigating Domain Drift in Multi Species Segmentation with DINOv2: A Cross-Domain Evaluation in Herbicide Research Trials

arXiv:2508.07514v3 Announce Type: replace Abstract: Reliable plant species and damage segmentation for herbicide field research trials requires models that can withstand substantial real-world variati

applicationsarxiv-cs-cv

Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection

arXiv:2405.11619v2 Announce Type: replace-cross Abstract: Phishing emails continue to pose a significant threat, causing financial losses and security breaches. This study addresses limitations in exi

applicationsarxiv-cs-ai

Orion-Lite: Distilling LLM Reasoning into Efficient Vision-Only Driving Models

arXiv:2604.08266v1 Announce Type: new Abstract: Leveraging the general world knowledge of Large Language Models (LLMs) holds significant promise for improving the ability of autonomous driving systems

Physical Knot Classification Beyond Accuracy: A Benchmark and Diagnostic Study

arXiv:2603.23286v3 Announce Type: replace Abstract: Physical knot classification is a fine-grained task in which the intended cue is rope crossing structure, but high accuracy may still come from appe

PriPG-RL: Privileged Planner-Guided Reinforcement Learning for Partially Observable Systems with Anytime-Feasible MPC

arXiv:2604.08036v1 Announce Type: cross Abstract: This paper addresses the problem of training a reinforcement learning (RL) policy under partial observability by exploiting a privileged, anytime-feas

safetyarxiv-cs-ro

Privacy Attacks on Image AutoRegressive Models

arXiv:2502.02514v5 Announce Type: replace Abstract: Image AutoRegressive generation has emerged as a new powerful paradigm with image autoregressive models (IARs) matching state-of-the-art diffusion m

Local Ai

Quantifying Explanation Consistency: The C-Score Metric for CAM-Based Explainability in Medical Image Classification

arXiv:2604.08502v1 Announce Type: new Abstract: Class Activation Mapping (CAM) methods are widely used to generate visual explanations for deep learning classifiers in medical imaging. However, existi

local-aiarxiv-cs-cv

Rethinking Entropy Allocation in LLM-based ASR: Understanding the Dynamics between Speech Encoders and LLMs

arXiv:2604.08003v1 Announce Type: cross Abstract: Integrating large language models (LLMs) into automatic speech recognition (ASR) has become a dominant paradigm. Although recent LLM-based ASR models

Robust Multi-Agent Target Tracking in Intermittent Communication Environments via Analytical Belief Merging

arXiv:2604.07575v1 Announce Type: new Abstract: Autonomous multi-agent target tracking in GPS-denied and communication-restricted environments (e.g., underwater exploration, subterranean search and re

agentsarxiv-cs-ro

SALLIE: Safeguarding Against Latent Language & Image Exploits

arXiv:2604.06247v1 Announce Type: cross Abstract: Large Language Models (LLMs) and Vision-Language Models (VLMs) remain highly vulnerable to textual and visual jailbreaks, as well as prompt injections

Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems

arXiv:2604.08366v1 Announce Type: cross Abstract: Large-scale deep learning models for physical AI applications depend on diverse training data collection efforts. These models and correspondingly, th

agentsarxiv-cs-cv

Shortcut Learning in Glomerular AI: Adversarial Penalties Hurt, Entropy Helps

arXiv:2604.07936v1 Announce Type: new Abstract: Stain variability is a pervasive source of distribution shift and potential shortcut learning in renal pathology AI. We ask whether lupus nephritis glom

SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds

arXiv:2604.08544v1 Announce Type: cross Abstract: Robotic manipulation with deformable objects represents a data-intensive regime in embodied learning, where shape, contact, and topology co-evolve in

SkillClaw: Let Skills Evolve Collectively with Agentic Evolver

arXiv:2604.08377v1 Announce Type: cross Abstract: Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after depl

agentsarxiv-cs-cl

SOLAR: Communication-Efficient Model Adaptation via Subspace-Oriented Latent Adapter Reparametrization

arXiv:2604.08368v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, enable scalable adaptation of foundation models by injecting low-rank adapters. However,

Squeeze Evolve: Unified Multi-Model Orchestration for Verifier-Free Evolution

arXiv:2604.07725v1 Announce Type: cross Abstract: We show that verifier-free evolution is bottlenecked by both diversity and efficiency: without external correction, repeated evolution accelerates col

researcharxiv-cs-cl

SyncBreaker:Stage-Aware Multimodal Adversarial Attacks on Audio-Driven Talking Head Generation

arXiv:2604.08405v1 Announce Type: new Abstract: Diffusion-based audio-driven talking-head generation enables realistic portrait animation, but also introduces risks of misuse, such as fraud and misinf