Search: “framework”

Early Detection of Water Stress by Plant Electrophysiology: Machine Learning for Irrigation Management

arXiv:2604.28038v1 Announce Type: new Abstract: Purpose: Fast detection of plant stress is key to plant phenotyping, precision agriculture, and automated crop management. In particular, efficient irri

agentsarxiv-cs-lg

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Hardware

EdgeFM: Efficient Edge Inference for Vision-Language Models

DGX agent

arXiv:2604.27476v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated strong applicability in edge industrial applications, yet their deployment remains severely constrained

hardwarearxiv-cs-cv

1 May 2026

Exploring Vision Neural Network Pruning via Screening Methodology

arXiv:2502.07189v2 Announce Type: replace Abstract: The remarkable performance of modern deep neural networks (DNNs) is largely driven by their massive scale, often comprising tens to hundreds of mill

applicationsarxiv-cs-lg

From Competition to Collaboration: Designing Sustainable Mechanisms Between LLMs and Online Forums

arXiv:2602.04572v2 Announce Type: replace Abstract: While Generative AI (GenAI) systems draw users away from (Q&A) forums, they also depend on the very data those forums produce to improve their perfo

researcharxiv-cs-ai

In-Context Prompting Obsoletes Agent Orchestration for Procedural Tasks

arXiv:2604.27891v1 Announce Type: new Abstract: Agent orchestration frameworks -- LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others -- place an external orchestrator above the LLM, tracking

agentsarxiv-cs-ai

Learning-to-Explain through 20Q Gaming: An Explainable Recommender for Cybersecurity Education

arXiv:2604.26964v1 Announce Type: cross Abstract: The growing sophistication of contemporary cyber threats necessitates a more effective and adaptive approach to cybersecurity training. Intuitive and

safetyarxiv-cs-ai

Multi-Level Narrative Evaluation Outperforms Lexical Features for Mental Health

arXiv:2604.27846v1 Announce Type: new Abstract: How people narrate their experiences offers a window into how the mind organizes them. Computational approaches to therapeutic writing have evolved from

local-aiarxiv-cs-cl

On the Expressive Power of GNNs to Solve Linear SDPs

arXiv:2604.27786v1 Announce Type: new Abstract: Semidefinite programs (SDPs) are a powerful framework for convex optimization and for constructing strong relaxations of hard combinatorial problems. Ho

Optimization before Evaluation: Evaluation with Unoptimised Prompts Can be Misleading

arXiv:2604.27637v1 Announce Type: new Abstract: Current Large Language Model (LLM) evaluation frameworks utilize the same static prompt template across all models under evaluation. This differs from t

researcharxiv-cs-ai

Real-World Doctor Agent with Proactive Consultation through Multi-Agent Reinforcement Learning

arXiv:2505.19630v4 Announce Type: replace Abstract: Large language models (LLMs) struggle in real-world clinical consultations. Single-turn consultation systems require patients to describe all sympto

agentsarxiv-cs-cl

Sparse-View 3D Gaussian Splatting in the Wild

arXiv:2604.27422v1 Announce Type: new Abstract: We propose a 3D novel sparse-view synthesis framework for unconstrained real-world scenarios that contain distractors. Unlike existing methods that prim

applicationsarxiv-cs-cv

TranSplat: Instant Object Relighting in Gaussian Splatting via Spherical Harmonic Radiance Transfer

arXiv:2503.22676v5 Announce Type: replace Abstract: We present TranSplat, a method for instant, accurate object relighting within the Gaussian Splatting (GS) framework. Rather than relying on costly i

applicationsarxiv-cs-cv

Why Self-Supervised Encoders Want to Be Normal

arXiv:2604.27743v1 Announce Type: cross Abstract: We develop a geometric and information-theoretic framework for encoder-decoder learning built on the Information Bottleneck (IB) principle. Recasting

researcharxiv-cs-ai

Hardware

AdaFRUGAL: Adaptive Memory-Efficient Training with Dynamic Control

arXiv:2601.11568v2 Announce Type: replace-cross Abstract: Training Large Language Models (LLMs) is highly memory-intensive due to optimizer state overhead. The FRUGAL framework mitigates this with gra

hardwarearxiv-cs-ai

Alter-Art: Exploring Embodied Artistic Creation through a Robot Avatar

arXiv:2604.26473v1 Announce Type: new Abstract: As with every emerging technology, new tools in the hands of artists reshape the nature of artwork creation. Current frameworks for robotics in arts dep

agentsarxiv-cs-ro

Auditing Marketing Budget Allocation with Hindsight Regret

model-releasesarxiv-cs-ai

arXiv:2604.25977v1 Announce Type: cross Abstract: Organizations routinely make strategic budget allocations under operational constraints, but often lack a principled way to assess whether realized al

DreamProver: Evolving Transferable Lemma Libraries via a Wake-Sleep Theorem-Proving Agent

arXiv:2604.26311v1 Announce Type: new Abstract: We introduce DreamProver, an agentic framework that leverages a 'wake-sleep' program induction paradigm to discover reusable lemmas for formal theorem p

agentsarxiv-cs-ai

DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis

model-releasesarxiv-cs-ai

arXiv:2604.26328v1 Announce Type: cross Abstract: The rapid advancement of large language models (LLMs) presents new security challenges, particularly in detecting machine-generated text used for misi

Emergent Coordination in Multi-Agent Language Models

arXiv:2510.05174v4 Announce Type: replace-cross Abstract: When are multi-agent LLM systems merely a collection of individual agents versus an integrated collective with higher-order structure? We intr

local-aiarxiv-cs-ai

Explainable Representation of Finite-Memory Policies for POMDPs using Decision Trees

arXiv:2411.13365v2 Announce Type: replace Abstract: Partially Observable Markov Decision Processes (POMDPs) are a fundamental framework for decision-making under uncertainty and partial observability.

tutorialsarxiv-cs-ai

Featurising Pixels from Dynamic 3D Scenes with Linear In-Context Learners

arXiv:2604.26488v1 Announce Type: new Abstract: One of the most exciting applications of vision models involve pixel-level reasoning. Despite the abundance of vision foundation models, we still lack r

LIT-RAGBench: Benchmarking Generator Capabilities of Large Language Models in Retrieval-Augmented Generation

model-releasesarxiv-cs-cl

arXiv:2603.06198v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a framework in which a Generator, such as a Large Language Model (LLM), produces answers by retrieving docum

Multiple Additive Neural Networks for Structured and Unstructured Data

arXiv:2604.26888v1 Announce Type: new Abstract: This paper extends and explains the Multiple Additive Neural Networks (MANN) methodology, an enhancement to the traditional Gradient Boosting framework,

Probabilistic data quality assessment for structural monitoring data via outlier-resistant conditional diffusion model

arXiv:2604.26366v1 Announce Type: cross Abstract: Data quality assessment is an essential step that ensures the reliability of the subsequent structural health monitoring (SHM) tasks. This study propo

applicationsarxiv-cs-lg

Quantamination: Dynamic Quantization Leaks Your Data Across the Batch

arXiv:2604.26505v1 Announce Type: cross Abstract: Dynamic quantization emerged as a practical approach to increase the utilization and efficiency of the machine learning serving flow. Unlike static qu

Quantum Feature Selection with Higher-Order Binary Optimization on Trapped-Ion Hardware

model-releasesarxiv-cs-lg

arXiv:2604.26834v1 Announce Type: cross Abstract: We present a quantum feature-selection framework based on a higher-order unconstrained binary optimization (HUBO) formulation that explicitly incorpor

SD2AIL: Adversarial Imitation Learning from Synthetic Demonstrations via Diffusion Models

arXiv:2512.18583v2 Announce Type: replace Abstract: Adversarial Imitation Learning (AIL) is a dominant framework in imitation learning that infers rewards from expert demonstrations to guide policy op

safetyarxiv-cs-lg

SecMate: Multi-Agent Adaptive Cybersecurity Troubleshooting with Tri-Context Personalization

arXiv:2604.26394v1 Announce Type: cross Abstract: Recent advances in large language models and agentic frameworks have enabled virtual customer assistants (VCAs) for complex support. We present SecMat

local-aiarxiv-cs-ai

STAR-Filter: Efficient Convex Free-Space Approximation via Starshaped Set Filtering in Noisy Environments

arXiv:2604.26626v1 Announce Type: new Abstract: Approximating collision-free space is fundamental to robot planning in complex environments. Convex geometric representations, such as polytopes and ell

applicationsarxiv-cs-ro

Unsupervised Graph Modeling for Anomaly Detection in Accounting Subject Relationships

arXiv:2604.26216v1 Announce Type: new Abstract: This paper addresses the problem of anomaly detection in accounting subject association structures, proposing a structured modeling and unsupervised dis

Beyond Accuracy: Benchmarking Cross-Task Consistency in Unified Multimodal Models

arXiv:2604.25072v1 Announce Type: new Abstract: Unified Multimodal Models (uMMs) aim to support both visual understanding and visual generation within a shared representation. However, existing evalua

safetyarxiv-cs-cv

Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning

model-releasesarxiv-cs-lg

arXiv:2604.24912v1 Announce Type: cross Abstract: We introduce HAML (Hamiltonian Adaptation via Meta-Learning), a framework for fast online adaptation of effective Hamiltonian models of superconductin

DDA-Thinker: Decoupled Dual-Atomic Reinforcement Learning for Reasoning-Driven Image Editing

arXiv:2604.25477v1 Announce Type: new Abstract: Recent image editing models have achieved strong visual fidelity but often struggle with tasks requiring complex reasoning. To investigate and enhance t

tutorialsarxiv-cs-cv

Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity

arXiv:2603.20645v2 Announce Type: replace Abstract: Diffusion models have become a leading framework in generative modeling, yet their theoretical understanding -- especially for high-dimensional data

tutorialsarxiv-cs-lg

Fractionally Supervised Classification with Maxima Nominated Samples

arXiv:2604.25145v1 Announce Type: cross Abstract: Fractionally supervised classification (FSC) offers a flexible framework for combining labeled and unlabeled data in model-based classification, but e

From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems

arXiv:2507.07847v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has emerged as a crucial framework in natural language processing (NLP), improving factual consistency and redu

researcharxiv-cs-cl

GeoSearch: Augmenting Worldwide Geolocalization with Web-Scale Reverse Image Search and Image Matching

arXiv:2604.25390v1 Announce Type: cross Abstract: Worldwide image geolocalization, which aims to predict the GPS coordinates of any image on Earth, remains challenging due to global visual diversity.

How Can Reinforcement Learning Achieve Expert-level Placement?

arXiv:2604.25191v1 Announce Type: cross Abstract: Chip placement is a critical step in physical design. While reinforcement learning (RL)-based methods have recently emerged, their training primarily

tutorialsarxiv-cs-lg

Instantaneous Planning, Control and Safety for Navigation in Unknown Underwater Spaces

arXiv:2604.05310v2 Announce Type: replace Abstract: Navigating autonomous underwater vehicles (AUVs) in unknown environments is significantly challenging due to poor visibility, weak signal transmissi

safetyarxiv-cs-ro

Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models

arXiv:2512.20677v4 Announce Type: replace-cross Abstract: The increasing deployment of large language models (LLMs) in safety-critical applications raises fundamental challenges in systematically eval

safetyarxiv-cs-cl

Measuring the stability and plasticity of recommender systems

arXiv:2508.03941v3 Announce Type: replace-cross Abstract: The typical offline protocol to evaluate recommendation algorithms is to collect a dataset of user-item interactions and then use a part of th

Mutual Forcing: Dual-Mode Self-Evolution for Fast Autoregressive Audio-Video Character Generation

arXiv:2604.25819v1 Announce Type: new Abstract: In this work, we propose Mutual Forcing, a framework for fast autoregressive audio-video generation with long-horizon audio-video synchronization. Our a

NUBO: A Transparent Python Package for Bayesian Optimization

model-releasesarxiv-cs-lg

arXiv:2305.06709v4 Announce Type: replace Abstract: NUBO, short for Newcastle University Bayesian Optimisation, is a Bayesian optimization framework for the optimization of expensive-to-evaluate black

PSI-Bench: Towards Clinically Grounded and Interpretable Evaluation of Depression Patient Simulators

model-releasesarxiv-cs-cl

arXiv:2604.25840v1 Announce Type: new Abstract: Patient simulators are gaining traction in mental health training by providing scalable exposure to complex and sensitive patient interactions. Simulati

Rethinking Efficiency in Neural Combinatorial Optimization: Batched Preference Optimization with Mamba

arXiv:2602.20730v2 Announce Type: replace Abstract: We study efficiency as a first-class objective in Neural Combinatorial Optimization (NCO) and present ECO, an efficient learning framework that comb

local-aiarxiv-cs-lg

Safe-Support Q-Learning: Learning without Unsafe Exploration

arXiv:2604.25379v1 Announce Type: new Abstract: Ensuring safety during reinforcement learning (RL) training is critical in real-world applications where unsafe exploration can lead to devastating outc

safetyarxiv-cs-lg

SaliencyDecor: Enhancing Neural Network Interpretability through Feature Decorrelation

arXiv:2604.25315v1 Announce Type: new Abstract: Gradient-based saliency methods are widely used to interpret deep neural networks, yet they often produce noisy and unstable explanations that poorly al

SciDER: Scientific Data-centric End-to-end Researcher

arXiv:2603.01421v2 Announce Type: replace-cross Abstract: Automated scientific discovery with large language models is transforming the research lifecycle from ideation to experimentation, yet existin

agentsarxiv-cs-cl