Search: “framework”

AgenticAITA: A Proof-Of-Concept About Deliberative Multi-Agent Reasoning for Autonomous Trading Systems

arXiv:2605.12532v1 Announce Type: cross Abstract: Conventional algorithmic trading systems are grounded in deterministic heuristics or offline-trained statistical models that cannot adapt to the seman

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Amortized Neural Clustering of Time Series based on Statistical Features

DGX agent

arXiv:2605.13128v1 Announce Type: cross Abstract: This paper introduces an algorithm-agnostic approach to feature-based time series clustering via amortized neural inference. By training neural networ

researcharxiv-cs-lg

14 May 2026

Agents

CHAL: Council of Hierarchical Agentic Language

arXiv:2605.12718v1 Announce Type: new Abstract: Multi-agent debate has emerged as a promising approach for improving LLM reasoning on ground-truth tasks, yet current methodologies face certain structu

agentsarxiv-cs-ai

Convergent Differential Privacy Analysis for General Federated Learning

arXiv:2408.15621v3 Announce Type: replace Abstract: The powerful cooperation of federated learning (FL) and differential privacy~(DP) provides a promising paradigm for the large-scale private clients.

Tutorials

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces

arXiv:2605.12809v1 Announce Type: cross Abstract: A critical step for reliable large language models (LLMs) use in healthcare is to attribute predictions to their training data, akin to a medical case

tutorialsarxiv-cs-ai

Decision Support for Marketplace Policies under Incomplete Evidence: From Replay to Launch Readiness

arXiv:2605.12840v1 Announce Type: cross Abstract: Marketplace platforms routinely evaluate pricing and allocation policies using logged observational data, yet strong offline performance does not impl

DynoJEPP: Joint Estimation, Prediction and Planning in Dynamic Environments

arXiv:2605.12897v1 Announce Type: new Abstract: DynoJEPP is a factor-graph-based framework that jointly formulates and simultaneously optimizes estimation, prediction, and planning in dynamic environm

researcharxiv-cs-ro

EgoForce: Robust Online Egocentric Motion Reconstruction via Diffusion Forcing

arXiv:2605.13041v1 Announce Type: new Abstract: With recent advances in embodied agents and AR devices, egocentric observations are readily available as input for real-world interactive online applica

applicationsarxiv-cs-cv

Gradual Domain Adaptation for Graph Learning

arXiv:2501.17443v4 Announce Type: replace Abstract: Existing machine learning literature lacks graph-based domain adaptation techniques capable of handling large distribution shifts, primarily due to

Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration

arXiv:2605.12573v1 Announce Type: cross Abstract: Diffusion-based posterior sampling (PS) is a leading framework for imaging inverse problems, combining learned priors with measurement constraints. Ye

safetyarxiv-cs-ai

Lifelong Learning in Vision-Language Models: Enhanced EWC with Cross-Modal Knowledge Retention

model-releasesarxiv-cs-ro

arXiv:2605.12789v1 Announce Type: new Abstract: Large language-vision models (LVLMs) such as CLIP, Flamingo, and BLIP have revolutionized AI by enabling understanding across textual and visual modalit

NFR: Neural Feature-Guided Non-Rigid Shape Registration

arXiv:2505.22445v2 Announce Type: replace-cross Abstract: In this paper, we propose a novel learning-based framework for 3D shape registration, which overcomes the challenges of significant non-rigid

researcharxiv-cs-ai

Online Conformal Prediction: Enforcing monotonicity via Online Optimization

arXiv:2605.12668v1 Announce Type: cross Abstract: Conformal prediction provides a principled framework for uncertainty quantification with finite-sample coverage guarantees. While recent work has exte

applicationsarxiv-cs-lg

Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo

arXiv:2605.13807v1 Announce Type: cross Abstract: Neural-network quantum states have emerged as a powerful variational framework for quantum many-body systems, with recent progress often driven by mas

Quantifying LLM Safety Degradation Under Repeated Attacks Using Survival Analysis

arXiv:2605.12869v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in a wide range of applications, yet remain vulnerable to adversarial jailbreak attacks that ci

safetyarxiv-cs-ai

RS-Claw: Progressive Active Tool Exploration via Hierarchical Skill Trees for Remote Sensing Agents

model-releasesarxiv-cs-ai

arXiv:2605.13391v1 Announce Type: new Abstract: The rise of multi-modal large language models (MLLMs) is shifting remote sensing (RS) intelligence from 'see' to 'action', as OpenClaw-style frameworks

Strikingness-Aware Evaluation for Temporal Knowledge Graph Reasoning

arXiv:2605.13153v1 Announce Type: new Abstract: Temporal Knowledge Graph Reasoning (TKGR) aims at inferring missing (especially future) events from historical data. Current evaluation in TKGR uniforml

researcharxiv-cs-ai

Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis

arXiv:2605.13312v1 Announce Type: new Abstract: We present Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework for integrative brain network analysis that generalizes S

SynCABEL: Synthetic Contextualized Augmentation for Biomedical Entity Linking

model-releasesarxiv-cs-ai

arXiv:2601.19667v2 Announce Type: replace-cross Abstract: We present SynCABEL (Synthetic Contextualized Augmentation for Biomedical Entity Linking), a framework that addresses a central bottleneck in

TimelineReasoner: Advancing Timeline Summarization with Large Reasoning Models

arXiv:2605.12518v1 Announce Type: cross Abstract: The proliferation of online news poses a challenge to extracting structured timelines from unstructured content. While recent studies have shown that

researcharxiv-cs-ai

TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics

arXiv:2605.13748v1 Announce Type: new Abstract: Semidefinite programming (SDP) provides a principled framework for convex relaxations of nonconvex geometric constraints in motion planning, yet existin

researcharxiv-cs-ro

Tutorials

Visual Accommodation: Rethinking Image Scale as a Learnable Variable for Object Detection

arXiv:2412.06341v2 Announce Type: replace-cross Abstract: We propose Ciliary-DETR (previous name: Elastic-DETR), a framework for test-time resolution adjustment analogous to biological accommodation.

tutorialsarxiv-cs-ai

When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering

arXiv:2602.22474v2 Announce Type: replace-cross Abstract: Policy steering is an emerging way to adapt robot behaviors at deployment-time: a learned verifier analyzes low-level action samples proposed

Yield Curves Dynamics Using Variational Autoencoders Under No-arbitrage

arXiv:2605.12764v1 Announce Type: cross Abstract: This paper introduces a physics-informed generative framework that resolves the fundamental conflict between the statistical flexibility of deep learn

AlphaGRPO: Unlocking Self-Reflective Multimodal Generation in UMMs via Decompositional Verifiable Reward

arXiv:2605.12495v1 Announce Type: new Abstract: In this paper, we propose AlphaGRPO, a novel framework that applies Group Relative Policy Optimization (GRPO) to AR-Diffusion Unified Multimodal Models

safetyarxiv-cs-cv

Approximation Theory of Laplacian-Based Neural Operators for Reaction-Diffusion System

arXiv:2605.12025v1 Announce Type: new Abstract: Neural operators provide a framework for learning solution operators of partial differential equations (PDEs), enabling efficient surrogate modeling for

ASIP-Planner: Adaptive Planning for UAV Surface Inspection in Partially Known Indoor Environments

arXiv:2605.11119v1 Announce Type: new Abstract: Indoor infrastructure inspection, such as tunnels and industrial facilities, requires systematic surface coverage to ensure that all inspection targets

applicationsarxiv-cs-ro

Hardware

AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration -- Learning from Cheap, Optimizing Expensive

arXiv:2605.11518v1 Announce Type: cross Abstract: Effectively configuring scalable large language model (LLM) experiments, spanning architecture design, hyperparameter tuning, and beyond, is crucial f

hardwarearxiv-cs-cl

Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification

arXiv:2605.11460v1 Announce Type: new Abstract: System identification (SysID) is critical for modeling dynamical systems from experimental data, yet traditional approaches often fail to capture nonlin

Debiased Model-based Representations for Sample-efficient Continuous Control

arXiv:2605.11711v1 Announce Type: new Abstract: Model-based representations recently stand out as a promising framework that embeds latent dynamics information into the representations for downstream

Deep Probabilistic Unfolding for Quantized Compressive Sensing

arXiv:2605.11475v1 Announce Type: new Abstract: We propose a deep probabilistic unfolding model to address the classical quantized compressive sensing problem that leverages an unfolding framework to

Distributed Pose Graph Optimization via Continuous Riemannian Dynamics

model-releasesarxiv-cs-ro

arXiv:2605.11210v1 Announce Type: new Abstract: We present a framework for distributed Pose Graph Optimization (PGO) by formulating the problem as a second-order continuous-time dynamical system evolv

Evaluating the Pre-Consultation Ability of LLMs using Diagnostic Guidelines

model-releasesarxiv-cs-cl

arXiv:2601.03627v3 Announce Type: replace Abstract: We introduce EPAG, a benchmark dataset and framework designed for Evaluating the Pre-consultation Ability of LLMs using diagnostic Guidelines. LLMs

Agents

Forecast-aware Gaussian Splatting for Predictive 3D Representation in Language-Guided Pick-and-Place Manipulation

arXiv:2605.11144v1 Announce Type: new Abstract: We introduce Forecast-aware Gaussian Splatting (Forecast-GS), a predictive 3D representation framework for language-conditioned robotic manipulation. Wh

agentsarxiv-cs-ro

Fused Gromov-Wasserstein Distance with Feature Selection

arXiv:2605.12161v1 Announce Type: new Abstract: Fused Gromov-Wasserstein (FGW) distances provide a principled framework for comparing objects by jointly aligning structure and node features. However,

Holder Policy Optimisation

arXiv:2605.12058v1 Announce Type: new Abstract: Group Relative Policy Optimisation (GRPO) enhances large language models by estimating advantages across a group of sampled trajectories. However, mappi

Hypernetworks for Dynamic Feature Selection

arXiv:2605.12278v1 Announce Type: new Abstract: Dynamic feature selection (DFS) is a machine learning framework in which features are acquired sequentially for individual samples under budget constrai

Model-Level GNN Explanations via Rule-to-Graph Readout for Logit Reconstruction

arXiv:2503.09051v2 Announce Type: replace Abstract: We propose a novel model-level GNN explanation framework that shifts the explanation target from class-wise rule extraction to rule-based logit reco

applicationsarxiv-cs-lg

Monitoring access to piped water and sanitation infrastructure in Africa at disaggregated scales using satellite imagery and self-supervised learning

arXiv:2411.19093v4 Announce Type: replace Abstract: Access to drinking water and sanitation services is essential for health and well-being, yet large global disparities persist. Sustainable Developme

Optimizing 4D Wires for Sparse 3D Abstraction

arXiv:2605.11977v1 Announce Type: new Abstract: We present a unified framework for 3D geometric abstraction using a single continuous 4D wire, parameterized as a B-spline with spatial coordinates and

safetyarxiv-cs-cv

Partition Tree: Conditional Density Estimation over General Outcome Spaces

arXiv:2602.04042v2 Announce Type: replace Abstract: We propose Partition Tree, a novel tree-based framework for conditional density estimation over general outcome spaces that supports both continuous

Provably Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function

arXiv:2602.02406v2 Announce Type: replace-cross Abstract: Data-driven algorithm design automates hyperparameter tuning, but its statistical foundations remain limited because model performance can dep

Robustness Certificates for Neural Networks against Adversarial Attacks

arXiv:2512.20865v2 Announce Type: replace Abstract: The increasing use of machine learning in safety-critical domains amplifies the risk of adversarial threats, especially data poisoning attacks that

ScaleMoGen: Autoregressive Next-Scale Prediction for Human Motion Generation

arXiv:2605.11704v1 Announce Type: new Abstract: We present ScaleMoGen, a scale-wise autoregressive framework for text-driven human motion generation. Unlike conventional autoregressive approaches that

Semi-Supervised Bayesian GANs with Log-Signatures for Uncertainty-Aware Credit Card Fraud Detection

arXiv:2509.00931v3 Announce Type: replace-cross Abstract: We present a novel deep generative semi-supervised framework for credit card fraud detection, formulated as time series classification task. A

SRG: Score-based Relaxation-guided Generation for Mixed Integer Linear Programming

arXiv:2603.24033v2 Announce Type: replace Abstract: We propose Score-based Relaxation-guided Generation (SRG), a generative framework based on an approximate formulation of relaxation-guided stochasti

StoicLLM: Preference Optimization for Philosophical Alignment in Small Language Models

model-releasesarxiv-cs-cl

arXiv:2605.11483v1 Announce Type: new Abstract: While large language models excel at factual adaptation, their ability to internalize nuanced philosophical frameworks under severe data constraints rem

WorldComp2D: Spatio-semantic Representations of Object Identity and Location from Local Views

arXiv:2605.11743v1 Announce Type: new Abstract: Learning latent representations that capture both semantic and spatial information is central to efficient spatio-semantic reasoning. However, many exis