Search: “framework”

arXiv:2604.19518v1 Announce Type: new Abstract: Decentralized optimization enables multiple devices to learn a global machine learning model while each individual device only has access to its local d

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Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models

DGX agent

arXiv:2604.18612v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, while recent prompting strategies such as Chain-of-Thou

model-releasesarxiv-cs-ai

22 Apr 2026

AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulations

arXiv:2504.09662v3 Announce Type: replace-cross Abstract: Multi-agent large language model simulations have the potential to model complex human behaviors and interactions. If the mechanics are set up

An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QA

arXiv:2604.19685v1 Announce Type: new Abstract: Answering open-ended questions remains challenging for AI systems because it requires synthesis, judgment, and exploration beyond factual retrieval, and

researcharxiv-cs-cl

Applications

AutoAdapt: Automated domain adaptation for large language models

applicationsmicrosoft-research

Deploying large language models (LLMs) in real-world, high-stakes settings is harder than it should be. In high-stakes settings like law, medicine, and cloud incident response, performance and reliabi

Autogenesis: A Self-Evolving Agent Protocol

arXiv:2604.15034v2 Announce Type: replace Abstract: Recent advances in LLM based agent systems have shown promise in tackling complex, long horizon tasks. However, existing agent protocols (e.g., A2A

benchmarking agents by their ability to play video games 🎮

benchmarking agents by their ability to play video games 🎮 We are excited to launch VideoGameBench on Antim Labs, created by @a1zhang, Thomas L. Griffiths (@cocosci_lab), @karthik_r_n, and @OfirPress

agentsyohei-nakajima--x

Beyond Bellman: High-Order Generator Regression for Continuous-Time Policy Evaluation

arXiv:2604.18972v1 Announce Type: cross Abstract: We study finite-horizon continuous-time policy evaluation from discrete closed-loop trajectories under time-inhomogeneous dynamics. The target value s

safetyarxiv-cs-lg

Applications

Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models

arXiv:2604.18751v1 Announce Type: cross Abstract: Nonlinear machine-learning models are increasingly used to discover causal relationships in time-series data, yet the interpretation of their outputs

applicationsarxiv-cs-ai

Beyond the 'Diff': Addressing Agentic Entropy in Agentic Software Development

arXiv:2604.16323v2 Announce Type: replace-cross Abstract: As autonomous coding agents become deeply embedded in software development workflows, their high operational velocity introduces a critical ov

Chat2Workflow: A Benchmark for Generating Executable Visual Workflows with Natural Language

arXiv:2604.19667v1 Announce Type: cross Abstract: At present, executable visual workflows have emerged as a mainstream paradigm in real-world industrial deployments, offering strong reliability and co

Construction of Knowledge Graph based on Language Model

arXiv:2604.19137v1 Announce Type: new Abstract: Knowledge Graph (KG) can effectively integrate valuable information from massive data, and thus has been rapidly developed and widely used in many field

researcharxiv-cs-cl

ConvVitMamba: Efficient Multiscale Convolution, Transformer, and Mamba-Based Sequence modelling for Hyperspectral Image Classification

model-releasesarxiv-cs-cv

arXiv:2604.18856v1 Announce Type: new Abstract: Hyperspectral image (HSI) classification remains challenging due to high spectral dimensionality, redundancy, and limited labeled data. Although convolu

Curvature-Aware PCA with Geodesic Tangent Space Aggregation for Semi-Supervised Learning

arXiv:2604.18816v1 Announce Type: cross Abstract: Principal Component Analysis (PCA) is a fundamental tool for representation learning, but its global linear formulation fails to capture the structure

Detecting Data Contamination in Large Language Models

arXiv:2604.19561v1 Announce Type: new Abstract: Large Language Models (LLMs) utilize large amounts of data for their training, some of which may come from copyrighted sources. Membership Inference Att

DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs

arXiv:2604.19118v1 Announce Type: cross Abstract: Modern distributed systems generate massive volumes of log data that are critical for detecting anomalies and cyber threats. However, in real world se

Enhancing Construction Worker Safety in Extreme Heat: A Machine Learning Approach Utilizing Wearable Technology for Predictive Health Analytics

arXiv:2604.19559v1 Announce Type: new Abstract: Construction workers are highly vulnerable to heat stress, yet tools that translate real-time physiological data into actionable safety intelligence rem

Ensembling Pruned Attention Heads For Uncertainty-Aware Efficient Transformers

arXiv:2510.18358v2 Announce Type: replace-cross Abstract: Uncertainty quantification (UQ) is essential for deploying deep neural networks in safety-critical settings. Although methods like Deep Ensemb

safetyarxiv-cs-cv

Explore Like Humans: Autonomous Exploration with Online SG-Memo Construction for Embodied Agents

arXiv:2604.19034v1 Announce Type: new Abstract: Constructing structured spatial memory is essential for enabling long-horizon reasoning in complex embodied navigation tasks. Current memory constructio

agentsarxiv-cs-cv

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion

arXiv:2604.19015v1 Announce Type: cross Abstract: Federated fine-tuning of Large Language Models (LLMs) is obstructed by a trilemma of challenges: protecting LLMs intellectual property (IP), ensuring

From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing

arXiv:2604.18918v1 Announce Type: cross Abstract: Simulation-based testing of autonomous driving systems (ADS) must uncover realistic and diverse failures in dense, heterogeneous traffic. However, exi

safetyarxiv-cs-lg

GenerativeMPC: VLM-RAG-guided Whole-Body MPC with Virtual Impedance for Bimanual Mobile Manipulation

model-releasesarxiv-cs-ro

arXiv:2604.19522v1 Announce Type: new Abstract: Bimanual mobile manipulation requires a seamless integration between high-level semantic reasoning and safe, compliant physical interaction - a challeng

Guiding Distribution Matching Distillation with Gradient-Based Reinforcement Learning

arXiv:2604.19009v1 Announce Type: cross Abstract: Diffusion distillation, exemplified by Distribution Matching Distillation (DMD), has shown great promise in few-step generation but often sacrifices q

safetyarxiv-cs-cv

Hybrid Task and Motion Planning with Reactive Collision Handling for Multi-Robot Disassembly of Complex Products: Application to EV Batteries

arXiv:2509.21020v2 Announce Type: replace Abstract: This paper addresses the problem of multi-robot coordination for complex manipulation task sequences. We present a vision-driven task-and-motion pla

safetyarxiv-cs-ro

I predicted in January that 'harness' would be the AI buzzword of H12026 - seems accurate so far. Everyone is talking about harnesses but I'…

I predicted in January that 'harness' would be the AI buzzword of H12026 - seems accurate so far. Everyone is talking about harnesses but I'm not confident that the majority of people (including me!)

agentsharrison-chase--x

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

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

Tutorials

IR-Flow: Bridging Discriminative and Generative Image Restoration via Rectified Flow

arXiv:2604.19680v1 Announce Type: new Abstract: In image restoration, single-step discriminative mappings often lack fine details via expectation learning, whereas generative paradigms suffer from ine

tutorialsarxiv-cs-cv

KG-ViP: Bridging Knowledge Grounding and Visual Perception in Multi-modal LLMs for Visual Question Answering

arXiv:2601.11632v2 Announce Type: replace Abstract: Multi-modal Large Language Models (MLLMs) for Visual Question Answering (VQA) often suffer from dual limitations: knowledge hallucination and insuff

researcharxiv-cs-cv

Hardware

LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation

arXiv:2604.19167v1 Announce Type: cross Abstract: Deploying large language models (LLMs) in resource-constrained environments is hindered by heavy computational and memory requirements. We present LBL

hardwarearxiv-cs-ai

Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty

arXiv:2604.19677v1 Announce Type: cross Abstract: Reinforcement learning-based control policies have been frequently demonstrated to be more effective than analytical techniques for many manipulation

LiveVLN: Breaking the Stop-and-Go Loop in Vision-Language Navigation

model-releasesarxiv-cs-ro

arXiv:2604.19536v1 Announce Type: new Abstract: Recent navigation systems achieve strong benchmark results, yet real-world deployment often remains visibly stop-and-go. This bottleneck arises because

M^{2}GRPO: Mamba-based Multi-Agent Group Relative Policy Optimization for Biomimetic Underwater Robots Pursuit

arXiv:2604.19404v1 Announce Type: cross Abstract: Traditional policy learning methods in cooperative pursuit face fundamental challenges in biomimetic underwater robots, where long-horizon decision ma

MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mapping

arXiv:2603.22650v2 Announce Type: replace Abstract: Active mapping aims to determine how an agent should move to efficiently reconstruct unknown environments. Most existing approaches rely on greedy n

agentsarxiv-cs-cv

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

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

Industry

Microsoft issues emergency update for macOS and Linux ASP.NET threat

Microsoft issued an emergency update for ASP.NET Core

industryars-technica

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

Tutorials

Modelling and Analysing Behaviours and Emotions via Complex User Interactions

arXiv:1902.07683v1 Announce Type: cross Abstract: Over the past 15 years, the volume, richness and quality of data collected from the combined social networking platforms has increased beyond all expe

tutorialsarxiv-cs-ai

Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge

arXiv:2603.11665v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-a-Judges due to their strong alignment with human judgment across vario

safetyarxiv-cs-cl

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

One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization

arXiv:2601.18572v2 Announce Type: replace Abstract: Personalization of LLMs by sociodemographic subgroup often improves user experience, but can also introduce or amplify biases and unfair outcomes ac

safetyarxiv-cs-cl

Local Ai

PolarQuant: Optimal Gaussian Weight Quantization via Hadamard Rotation for LLM Compression

arXiv:2603.29078v2 Announce Type: replace Abstract: We present PolarQuant, a post-training weight quantization method for large language models (LLMs) that exploits the distributional structure of neu

local-aiarxiv-cs-cl

PuzzleWorld: A Benchmark for Multimodal, Open-Ended Reasoning in Puzzlehunts

arXiv:2506.06211v2 Announce Type: replace-cross Abstract: Puzzlehunts are a genre of complex, multi-step puzzles lacking well-defined problem definitions. In contrast to conventional reasoning benchma

Reducing the Offline-Streaming Gap for Unified ASR Transducer with Consistency Regularization

arXiv:2604.19079v1 Announce Type: cross Abstract: Unification of automatic speech recognition (ASR) systems reduces development and maintenance costs, but training a single model to perform well in bo

Reinforcement Learning Enabled Adaptive Multi-Task Control for Bipedal Soccer Robots

arXiv:2604.19104v1 Announce Type: cross Abstract: Developing bipedal football robots in dynamiccombat environments presents challenges related to motionstability and deep coupling of multiple tasks, a

Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients

arXiv:2505.06335v2 Announce Type: replace-cross Abstract: Federated Learning (FL) has the potential for simultaneous global learning amongst a large number of parallel agents, enabling emerging AI suc

Local Ai

Self-Improving Tabular Language Models via Iterative Group Alignment

arXiv:2604.18966v1 Announce Type: cross Abstract: While language models have been adapted for tabular data generation, two fundamental limitations remain: (1) static fine-tuning produces models that c

local-aiarxiv-cs-ai