Search: “deployment”

CrossPan: A Comprehensive Benchmark for Cross-Sequence Pancreas MRI Segmentation and Generalization

model-releasesarxiv-cs-cv

arXiv:2604.18797v1 Announce Type: new Abstract: Automatic pancreas segmentation is fundamental to abdominal MRI analysis, yet deep learning models trained on one MRI sequence often fail catastrophical

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Cyber Defense Benchmark: Agentic Threat Hunting Evaluation for LLMs in SecOps

DGX agent

arXiv:2604.19533v1 Announce Type: cross Abstract: We introduce the Cyber Defense Benchmark, a benchmark for measuring how well large language model (LLM) agents perform the core SOC analyst task of th

model-releasesarxiv-cs-ai

22 Apr 2026

Design Rules for Extreme-Edge Scientific Computing on AI Engines

arXiv:2604.19106v1 Announce Type: cross Abstract: Extreme-edge scientific applications use machine learning models to analyze sensor data and make real-time decisions. Their stringent latency and thro

Disparities In Negation Understanding Across Languages In Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2604.18942v1 Announce Type: new Abstract: Vision-language models (VLMs) exhibit affirmation bias: a systematic tendency to select positive captions ('X is present') even when the correct descrip

Distillation Traps and Guards: A Calibration Knob for LLM Distillability

arXiv:2604.18963v1 Announce Type: cross Abstract: Knowledge distillation (KD) transfers capabilities from large language models (LLMs) to smaller students, yet it can fail unpredictably and also under

EfficientPENet: Real-Time Depth Completion from Sparse LiDAR via Lightweight Multi-Modal Fusion

model-releasesarxiv-cs-cv

arXiv:2604.18790v1 Announce Type: new Abstract: Depth completion from sparse LiDAR measurements and corresponding RGB images is a prerequisite for accurate 3D perception in robotic systems. Existing m

EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale

arXiv:2604.17406v2 Announce Type: replace Abstract: The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. While the scientific method is

agentsarxiv-cs-ai

ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors

arXiv:2603.15956v2 Announce Type: replace-cross Abstract: Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data. While human demonstrations (

Four-Axis Decision Alignment for Long-Horizon Enterprise AI Agents

arXiv:2604.19457v1 Announce Type: new Abstract: Long-horizon enterprise agents make high-stakes decisions (loan underwriting, claims adjudication, clinical review, prior authorization) under lossy mem

GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search

arXiv:2602.15423v3 Announce Type: replace-cross Abstract: As the burgeoning power requirements of sophisticated neural architectures escalate, the information retrieval community has recognized ecolog

researcharxiv-cs-lg

Gated Memory Policy

arXiv:2604.18933v1 Announce Type: cross Abstract: Robotic manipulation tasks exhibit varying memory requirements, ranging from Markovian tasks that require no memory to non-Markovian tasks that depend

GRAIL:Learning to Interact with Large Knowledge Graphs for Retrieval Augmented Reasoning

arXiv:2508.05498v2 Announce Type: replace Abstract: Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) techniques have exhibited remarkable performance across a wide ran

Ground-Level Near Real-Time Modeling for PM2.5 Pollution Prediction

arXiv:2604.18973v1 Announce Type: cross Abstract: Air pollution is a worldwide public health threat that can cause or exacerbate many illnesses, including respiratory disease, cardiovascular disease,

safetyarxiv-cs-lg

Has Automated Essay Scoring Reached Sufficient Accuracy? Deriving Achievable QWK Ceilings from Classical Test Theory

arXiv:2604.19131v1 Announce Type: new Abstract: Automated essay scoring (AES) is commonly evaluated on public benchmarks using quadratic weighted kappa (QWK). However, because benchmark labels are ass

Idle is the New Sleep: Configuration-Aware Alternative to Powering Off FPGA-Based DL Accelerators During Inactivity

arXiv:2407.12027v2 Announce Type: replace-cross Abstract: In the rapidly evolving Internet of Things (IoT) domain, we concentrate on enhancing energy efficiency in Deep Learning accelerators on FPGA-b

Improved Anomaly Detection in Medical Images via Mean Shift Density Enhancement

arXiv:2604.19191v1 Announce Type: cross Abstract: Anomaly detection in medical imaging is essential for identifying rare pathological conditions, particularly when annotated abnormal samples are limit

Integrating Anomaly Detection into Agentic AI for Proactive Risk Management in Human Activity

arXiv:2604.19538v1 Announce Type: new Abstract: Agentic AI, with goal-directed, proactive, and autonomous decision-making capabilities, offers a compelling opportunity to address movement-related risk

MacroNav: Multi-Task Context Representation Learning Enables Efficient Navigation in Unknown Environments

arXiv:2511.04320v2 Announce Type: replace Abstract: Autonomous navigation in unknown environments requires multi-scale spatial understanding that captures geometric details, topological connectivity,

safetyarxiv-cs-ro

Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras

arXiv:2604.18744v1 Announce Type: new Abstract: Event cameras have recently shown promising capabilities in instantaneous motion estimation due to their robustness to low light and fast motions. Howev

researcharxiv-cs-cv

Mechanistic Anomaly Detection via Functional Attribution

model-releasesarxiv-cs-lg

arXiv:2604.18970v1 Announce Type: new Abstract: We can often verify the correctness of neural network outputs using ground truth labels, but we cannot reliably determine whether the output was produce

Mesh Memory Protocol: Semantic Infrastructure for Multi-Agent LLM Systems

arXiv:2604.19540v1 Announce Type: cross Abstract: Teams of LLM agents increasingly collaborate on tasks spanning days or weeks: multi-day data-generation sprints where generator, reviewer, and auditor

agentsarxiv-cs-ai

Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving

arXiv:2604.19368v1 Announce Type: new Abstract: Predicting driver intention from neurophysiological signals offers a promising pathway for enhancing proactive safety in advanced driver assistance syst

safetyarxiv-cs-cv

Multi-Gait Learning for Humanoid Robots Using Reinforcement Learning with Selective Adversarial Motion Prior

arXiv:2604.19102v1 Announce Type: cross Abstract: Learning diverse locomotion skills for humanoid robots in a unified reinforcement learning framework remains challenging due to the conflicting requir

Multimodal embodiment-aware navigation transformer

arXiv:2604.19267v1 Announce Type: new Abstract: Goal-conditioned navigation models for ground robots trained using supervised learning show promising zero-shot transfer, but their collision-avoidance

safetyarxiv-cs-ro

NemeSys: Toward Online Underwater Exploration with Remote Operator-in-the-loop Adaptive Autonomy

model-releasesarxiv-cs-ro

arXiv:2507.11889v2 Announce Type: replace Abstract: Adaptive mission control and dynamic parameter reconfiguration are essential for autonomous underwater vehicles (AUVs) operating in GPS-denied, comm

ODMA: On-Demand Memory Allocation Strategy for LLM Serving on LPDDR-Class Accelerators

arXiv:2512.09427v5 Announce Type: replace-cross Abstract: Existing memory management techniques severely hinder efficient Large Language Model serving on accelerators constrained by poor random-access

Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?

arXiv:2604.19399v1 Announce Type: new Abstract: Federated learning (FL) is a key paradigm for distributed model learning across decentralized data sources. Communication in each FL round typically con

local-aiarxiv-cs-lg

Personalized Embodied Navigation for Portable Object Finding

arXiv:2403.09905v5 Announce Type: replace-cross Abstract: Embodied navigation methods commonly operate in static environments with stationary objects. In this work, we present approaches for tackling

agentsarxiv-cs-cv

Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications

arXiv:2411.06837v2 Announce Type: replace Abstract: The rapid rise of Large Language Models (LLMs) has created new disruptive possibilities for persuasive communication, enabling fully-automated, pers

safetyarxiv-cs-cl

Tutorials

PhysMem: Scaling Test-time Physical Memory for Robot Manipulation

arXiv:2602.20323v5 Announce Type: replace-cross Abstract: Reliable object manipulation requires understanding physical properties that vary across objects and environments. Vision-language model (VLM)

tutorialsarxiv-cs-ai

PLaMo 2.1-VL Technical Report

arXiv:2604.19324v1 Announce Type: cross Abstract: We introduce PLaMo 2.1-VL, a lightweight Vision Language Model (VLM) for autonomous devices, available in 8B and 2B variants and designed for local an

agentsarxiv-cs-ai

QSLM: A Performance- and Memory-aware Quantization Framework with Tiered Search Strategy for Spike-driven Language Models

arXiv:2601.00679v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been emerging as prominent AI models for solving many natural language tasks due to their high performance (

R^2-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction

arXiv:2604.18995v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive generation by enabling parallel token prediction. Ho

RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora

arXiv:2604.19047v1 Announce Type: cross Abstract: Existing QA benchmarks typically assume distinct documents with minimal overlap, yet real-world retrieval-augmented generation (RAG) systems operate o

Safe Continual Reinforcement Learning in Non-stationary Environments

model-releasesarxiv-cs-lg

arXiv:2604.19737v1 Announce Type: new Abstract: Reinforcement learning (RL) offers a compelling data-driven paradigm for synthesizing controllers for complex systems when accurate physical models are

Applications

SAW-INT4: System-Aware 4-Bit KV-Cache Quantization for Real-World LLM Serving

arXiv:2604.19157v1 Announce Type: new Abstract: KV-cache memory is a major bottleneck in real-world LLM serving, where systems must simultaneously support latency-sensitive small-batch requests and hi

applicationsarxiv-cs-lg

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning

arXiv:2604.19254v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) reduces the training cost of full-parameter fine-tuning for large language models (LLMs) by training only a sma

Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers

arXiv:2604.19219v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training among multiple parties without centralizing raw data. There are two main paradigms in FL:

StomaD2: An All-in-One System for Intelligent Stomatal Phenotype Analysis via Diffusion-Based Restoration Detection Network

arXiv:2604.18632v1 Announce Type: new Abstract: Stomata play a crucial role in regulating plant physiological processes and reflecting environmental responses. However, accurate and high-throughput st

researcharxiv-cs-cv

The Alignment Waltz: Jointly Training Agents to Collaborate for Safety

arXiv:2510.08240v2 Announce Type: replace Abstract: Harnessing the power of LLMs requires a delicate dance between being helpful and harmless. This creates a fundamental tension between two competing

safetyarxiv-cs-cl

Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation

arXiv:2407.05102v2 Announce Type: replace-cross Abstract: Executing flow estimation using Deep Learning (DL)-based soft sensors on resource-limited IoT devices has demonstrated promise in terms of rel

TrEEStealer: Stealing Decision Trees via Enclave Side Channels

arXiv:2604.18716v1 Announce Type: cross Abstract: Today, machine learning is widely applied in sensitive, security-related, and financially lucrative applications. Model extraction attacks undermine c

researcharxiv-cs-lg

Uncertainty Quantification in Detection Transformers: Object-Level Calibration and Image-Level Reliability

arXiv:2412.01782v4 Announce Type: replace-cross Abstract: DETR and its variants have emerged as promising architectures for object detection, offering an end-to-end prediction pipeline. In practice, h

UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling

arXiv:2604.19734v1 Announce Type: cross Abstract: Scaling humanoid foundation models is bottlenecked by the scarcity of robotic data. While massive egocentric human data offers a scalable alternative,

VCE: A zero-cost hallucination mitigation method of LVLMs via visual contrastive editing

model-releasesarxiv-cs-cl

arXiv:2604.19412v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) frequently suffer from Object Hallucination (OH), wherein they generate descriptions containing objects that are

VDPP: Video Depth Post-Processing for Speed and Scalability

model-releasesarxiv-cs-cv

arXiv:2604.06665v2 Announce Type: replace Abstract: Video depth estimation is essential for providing 3D scene structure in applications ranging from autonomous driving to mixed reality. Current end-t

VISTA: Verification In Sequential Turn-based Assessment

arXiv:2510.27052v5 Announce Type: replace Abstract: Hallucination--defined here as generating statements unsupported or contradicted by available evidence or conversational context--remains a major ob

researcharxiv-cs-cl

Watch the Weights: Unsupervised monitoring and control of fine-tuned LLMs

model-releasesarxiv-cs-cl

arXiv:2508.00161v3 Announce Type: replace-cross Abstract: The releases of powerful open-weight large language models (LLMs) are often not accompanied by access to their full training data. Existing in