Search: “deployment”

FSFM: A Biologically-Inspired Framework for Selective Forgetting of Agent Memory

arXiv:2604.20300v1 Announce Type: new Abstract: For LLM agents, memory management critically impacts efficiency, quality, and security. While much research focuses on retention, selective forgetting--

safetyarxiv-cs-ai

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Hardware

Gaussians on a Diet: High-Quality Memory-Bounded 3D Gaussian Splatting Training

DGX agent

arXiv:2604.20046v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has revolutionized novel view synthesis with high-quality rendering through continuous aggregations of millions of 3D Gauss

hardwarearxiv-cs-cv

23 Apr 2026

Hardware

LaplacianFormer:Rethinking Linear Attention with Laplacian Kernel

arXiv:2604.20368v1 Announce Type: cross Abstract: The quadratic complexity of softmax attention presents a major obstacle for scaling Transformers to high-resolution vision tasks. Existing linear atte

hardwarearxiv-cs-ai

Learning to Solve the Quadratic Assignment Problem with Warm-Started MCMC Finetuning

arXiv:2604.20109v1 Announce Type: cross Abstract: The quadratic assignment problem (QAP) is a fundamental NP-hard task that poses significant challenges for both traditional heuristics and modern lear

Agents

Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems

arXiv:2604.20745v1 Announce Type: cross Abstract: Federated continual learning (FCL) allows distributed autonomous fleets to adapt collaboratively to evolving terrain types across extended mission lif

agentsarxiv-cs-cv

LLAMADRS: Evaluating Open-Source LLMs on Real Clinical Interviews--To Reason or Not to Reason?

model-releasesarxiv-cs-cl

arXiv:2501.03624v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel on many NLP benchmarks, but their behavior on real-world, semi-structured prediction remains underexplored.

LLMs Can Get 'Brain Rot': A Pilot Study on Twitter/X

arXiv:2510.13928v2 Announce Type: replace-cross Abstract: We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk web text induces lasting cognitive decline in large language mode

safetyarxiv-cs-ai

Local Ai

Locate-Then-Examine: Grounded Region Reasoning Improves Detection of AI-Generated Images

arXiv:2510.04225v2 Announce Type: replace-cross Abstract: The rapid growth of AI-generated imagery has blurred the boundary between real and synthetic content, raising practical concerns for digital i

local-aiarxiv-cs-ai

Maximum Likelihood Reconstruction for Multi-Look Digital Holography with Markov-Modeled Speckle Correlation

arXiv:2604.20154v1 Announce Type: cross Abstract: Multi-look acquisition is a widely used strategy for reducing speckle noise in coherent imaging systems such as digital holography. By acquiring multi

Measuring the Machine: Evaluating Generative AI as Pluralist Sociotechical Systems

arXiv:2604.20545v1 Announce Type: new Abstract: In measurement theory, instruments do not simply record reality; they help constitute what is observed. The same holds for generative AI evaluation: ben

MIRROR: A Hierarchical Benchmark for Metacognitive Calibration in Large Language Models

arXiv:2604.19809v1 Announce Type: new Abstract: We introduce MIRROR, a benchmark comprising eight experiments across four metacognitive levels that evaluates whether large language models can use self

Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards

arXiv:2506.16658v2 Announce Type: replace-cross Abstract: Multi-armed bandit (MAB) is a widely adopted framework for sequential decision-making under uncertainty. Traditional bandit algorithms rely so

safetyarxiv-cs-lg

Multi-Objective Reinforcement Learning for Generating Covalent Inhibitor Candidates

arXiv:2604.20019v1 Announce Type: new Abstract: Rational design of covalent inhibitors requires simultaneously optimizing multiple properties, such as binding affinity, target selectivity, or electrop

safetyarxiv-cs-lg

No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets

arXiv:2508.14098v2 Announce Type: replace-cross Abstract: Humanoids operating in real-world workspaces must frequently execute task-driven, short-range movements to SE(2) target poses. To be practical

On the Impact of Face Segmentation-Based Background Removal on Recognition and Morphing Attack Detection

arXiv:2604.20585v1 Announce Type: new Abstract: This study investigates the impact of face image background correction through segmentation on face recognition and morphing attack detection performanc

On the Quantization Robustness of Diffusion Language Models in Coding Benchmarks

arXiv:2604.20079v1 Announce Type: cross Abstract: Auto-regressive Large Language Models (LLMs) achieve strong performance on coding tasks, but incur high memory and inference costs. Diffusion-based la

researcharxiv-cs-cl

Onyx: Cost-Efficient Disk-Oblivious ANN Search

arXiv:2604.20401v1 Announce Type: cross Abstract: Approximate nearest neighbor (ANN) search in AI systems increasingly handles sensitive data on third-party infrastructure. Trusted execution environme

Opportunistic Bone-Loss Screening from Routine Knee Radiographs Using a Multi-Task Deep Learning Framework with Sensitivity-Constrained Threshold Optimization

arXiv:2604.20268v1 Announce Type: new Abstract: Background: Osteoporosis and osteopenia are often undiagnosed until fragility fractures occur. Dual-energy X-ray absorptiometry (DXA) is the reference s

Optimizing Data Augmentation for Real-Time Small UAV Detection: A Lightweight Context-Aware Approach

arXiv:2604.19999v1 Announce Type: new Abstract: Visual detection of Unmanned Aerial Vehicles (UAVs) is a critical task in surveillance systems due to their small physical size and environmental challe

OVPD: A Virtual-Physical Fusion Testing Dataset of OnSite Auton-omous Driving Challenge

model-releasesarxiv-cs-ro

arXiv:2604.20423v1 Announce Type: new Abstract: The rapid iteration of autonomous driving algorithms has created a growing demand for high-fidelity, replayable, and diagnosable testing data. However,

PokeVLA: Empowering Pocket-Sized Vision-Language-Action Model with Comprehensive World Knowledge Guidance

model-releasesarxiv-cs-ro

arXiv:2604.20834v1 Announce Type: new Abstract: Recent advances in Vision-Language-Action (VLA) models have opened new avenues for robot manipulation, yet existing methods exhibit limited efficiency a

Pre-Execution Query Slot-Time Prediction in Cloud Data Warehouses: A Feature-Scoped Machine Learning Approach

arXiv:2604.20145v1 Announce Type: cross Abstract: Cloud data warehouses bill compute based on slot-time consumed. In shared multi-tenant environments, query cost is highly variable and hard to estimat

Local Ai

QuadPiPS: A Perception-informed Footstep Planner for Quadrupeds With Semantic Affordance Prediction

arXiv:2501.00112v2 Announce Type: replace Abstract: This work proposes QuadPiPS, a perception-informed framework for quadrupedal foothold planning in the perception space. QuadPiPS employs a novel ego

local-aiarxiv-cs-ro

R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling

arXiv:2604.20316v1 Announce Type: new Abstract: Function calling empowers large language models (LLMs) to interface with external tools, yet existing RL-based approaches suffer from misalignment betwe

Rabies diagnosis in low-data settings: A comparative study on the impact of data augmentation and transfer learning

arXiv:2604.19823v1 Announce Type: cross Abstract: Rabies remains a major public health concern across many African and Asian countries, where accurate diagnosis is critical for effective epidemiologic

Rashomon Sets and Model Multiplicity in Federated Learning

model-releasesarxiv-cs-lg

arXiv:2602.09520v2 Announce Type: replace Abstract: The Rashomon set captures the collection of models that achieve near-identical empirical performance yet may differ substantially in their decision

retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers

model-releasesarxiv-cs-cv

arXiv:2602.08580v2 Announce Type: replace-cross Abstract: Automatic extraction of retinal vascular biomarkers from color fundus images (CFI) is crucial for large-scale studies of the retinal vasculatu

Saying More Than They Know: A Framework for Quantifying Epistemic-Rhetorical Miscalibration in Large Language Models

arXiv:2604.19768v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit systematic miscalibration with rhetorical intensity not proportionate to epistemic grounding. This study tests th

Supplement Generation Training for Enhancing Agentic Task Performance

arXiv:2604.20727v1 Announce Type: cross Abstract: Training large foundation models for agentic tasks is increasingly impractical due to the high computational costs, long iteration cycles, and rapid o

SwiftRepertoire: Few-Shot Immune-Signature Synthesis via Dynamic Kernel Codes

arXiv:2602.01051v4 Announce Type: replace Abstract: Repertoire-level analysis of T cell receptors offers a biologically grounded signal for disease detection and immune monitoring, yet practical deplo

VTouch++: A Multimodal Dataset with Vision-Based Tactile Enhancement for Bimanual Manipulation

arXiv:2604.20444v1 Announce Type: cross Abstract: Embodied intelligence has advanced rapidly in recent years; however, bimanual manipulation-especially in contact-rich tasks remains challenging. This

WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning

model-releasesarxiv-cs-cl

arXiv:2604.20398v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel at function-level code generation, project-level tasks such as generating functional and visually aesthetic mul

White-Basilisk: A Hybrid Model for Code Vulnerability Detection

arXiv:2507.08540v5 Announce Type: replace-cross Abstract: The proliferation of software vulnerabilities presents a significant challenge to cybersecurity, necessitating more effective detection method

WorkflowGen:an adaptive workflow generation mechanism driven by trajectory experience

arXiv:2604.19756v1 Announce Type: cross Abstract: Large language model (LLM) agents often suffer from high reasoning overhead, excessive token consumption, unstable execution, and inability to reuse p

Agents

X-Cache: Cross-Chunk Block Caching for Few-Step Autoregressive World Models Inference

arXiv:2604.20289v1 Announce Type: new Abstract: Real-time world simulation is becoming a key infrastructure for scalable evaluation and online reinforcement learning of autonomous driving systems. Rec

agentsarxiv-cs-cv

X-IONet: Cross-Platform Inertial Odometry Network for Pedestrian and Legged Robot

arXiv:2511.08277v2 Announce Type: replace-cross Abstract: Learning-based inertial odometry has achieved remarkable progress in pedestrian navigation. However, extending these methods to quadruped robo

Beyond Indistinguishability: Measuring Extraction Risk in LLM APIs

arXiv:2604.18697v1 Announce Type: cross Abstract: Indistinguishability properties such as differential privacy bounds or low empirically measured membership inference are widely treated as proxies to

researcharxiv-cs-cl

Bridging Semantics and Geometry: A Decoupled LVLM-SAM Framework for Reasoning Segmentation in Optical Remote Sensing

arXiv:2512.19302v2 Announce Type: replace Abstract: Large Vision--Language Models (LVLMs) hold great promise for advancing optical remote sensing (RS) analysis, yet existing reasoning segmentation fra

safetyarxiv-cs-cv

Bridging the High-Frequency Data Gap: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models

arXiv:2603.16497v2 Announce Type: replace-cross Abstract: Time series foundation models (TSFMs) require diverse, real-world datasets to adapt across varying domains and temporal frequencies. However,

Cross-Model Consistency of AI-Generated Exercise Prescriptions: A Repeated Generation Study Across Three Large Language Models

arXiv:2604.19598v1 Announce Type: cross Abstract: This study compared repeated generation consistency of exercise prescription outputs across three large language models (LLMs), specifically GPT-4.1,

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

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

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

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

Local Ai

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

local-aiarxiv-cs-ai

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

Agents

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 (

safetyarxiv-cs-ai