Search: “framework”

ARMS: Automatic Reward Shaping for Sparse-Reward Multi-Agent Reinforcement Learning

arXiv:2605.23562v1 Announce Type: cross Abstract: Sparse rewards are a major bottleneck in multi-agent reinforcement learning (MARL), where simultaneous learning induces non-stationarity and makes rew

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Safety

Bridging AI and Clinical Reasoning: Abductive Explanations for Alignment on Critical Symptoms

DGX agent

arXiv:2602.13985v2 Announce Type: replace Abstract: Artificial intelligence (AI) has demonstrated strong potential in clinical diagnostics, often achieving accuracy comparable to or exceeding that of

safetyarxiv-cs-ai

25 May 2026

CBANet: A Compact Attention-Based CNN-BiLSTM Network for Aggressive Driving Event Detection

arXiv:2605.23471v1 Announce Type: cross Abstract: Aggressive driving is a major cause of traffic accidents and poses a serious threat to road safety. Although deep learning methods have shown promisin

Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance

arXiv:2605.23453v1 Announce Type: new Abstract: We conducted a reproducibility-oriented re-evaluation of prior migraine classification studies, correcting for data leakage and metric bias. We then int

Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning

arXiv:2605.23138v1 Announce Type: cross Abstract: Variational Quantum Algorithms (VQAs) potentially offer a pathway to practical quantum advantage, but their optimization is heavily hindered by barren

Commutator-Induced Uncertainty in VAEs

arXiv:2605.23449v1 Announce Type: cross Abstract: Variational autoencoders (VAEs) often struggle to represent non-commutative structure in learned latent spaces. Symmetry-aware VAEs commonly address t

researcharxiv-cs-cv

Cost-Effective Model Evaluation with Meta-Learning

arXiv:2605.23595v1 Announce Type: cross Abstract: The rapid growth of machine learning has produced an ever-expanding ecosystem of models, making it increasingly challenging to verify the reliability

Decomposition-Based Modular Conformal Prediction for Two-Stage Modeling

arXiv:2510.04406v2 Announce Type: replace-cross Abstract: Conformal prediction offers finite-sample coverage guarantees under minimal assumptions. However, existing methods treat the entire modeling p

Disentangling Interaction and Bias Effects in Opinion Dynamics of Large Language Models

arXiv:2509.06858v2 Announce Type: replace-cross Abstract: Large Language Models are increasingly used to simulate human opinion dynamics, yet the effect of genuine interaction is often obscured by sys

DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks

arXiv:2605.23056v1 Announce Type: cross Abstract: Virtual Reality (VR) services delivered over 6G networks demand ultra-low latency and high bandwidth to ensure seamless user experiences. This paper p

researcharxiv-cs-ai

Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning

arXiv:2509.26383v5 Announce Type: replace-cross Abstract: Knowledge-graph retrieval-augmented generation (KG-RAG) couples large language models (LLMs) with structured, verifiable knowledge graphs (KGs

Evaluating Large Language Models in a Complex Hidden Role Game

arXiv:2605.22826v1 Announce Type: cross Abstract: Quantifying the deceptive potential of Large Language Models (LLMs) is critical for AI safety, yet difficult to achieve in uncontrolled environments.

Agents

EVE-Agent: Evidence-Verifiable Self-Evolving Agents

arXiv:2605.22905v1 Announce Type: new Abstract: Self-evolving agents should not train on examples they cannot justify. Data-free self-evolving search agents offer a scalable route to systems that gene

agentsarxiv-cs-ai

Agents

ExpOS: Explainable Open-Surgery Skills Assessment Using 3D Hand Reconstruction

arXiv:2605.23653v1 Announce Type: new Abstract: Timely and transparent feedback is essential for effective surgical training, yet current assessment remains dependent on expert observation, limiting s

agentsarxiv-cs-cv

FAST-ME: Foundation-aware Adaptive Stopping for Motion Estimation for Efficient IoT Video Analysis

model-releasesarxiv-cs-cv

arXiv:2605.23428v1 Announce Type: new Abstract: In modern multimedia systems, efficient video processing is critical, especially in resource-constrained environments such as IoT-based camera networks,

FastKernels: Benchmarking GPU Kernel Generation in Production

arXiv:2605.23215v1 Announce Type: cross Abstract: LLM-based agents for GPU kernel generation are advancing rapidly, yet their progress is fundamentally constrained by the benchmarks they optimize agai

Local Ai

From Activation to Causality: Discovery of Causal Visual Representations in the Human Brain

arXiv:2605.23895v1 Announce Type: new Abstract: Identifying which brain regions represent a visual concept in the human brain is a central challenge in neuroscience. Existing approaches have localized

local-aiarxiv-cs-cv

From Simulation to Discovery: AI Enabled Probabilistic Emulation of Mechanistic Crop Systems

arXiv:2605.22848v1 Announce Type: cross Abstract: Global food security depends on predicting crop responses to climate variability, yet process based crop models remain too computationally expensive f

GAF: Gaussian Action Field as a 4D Representation for Dynamic World Modeling in Robotic Manipulation

arXiv:2506.14135v5 Announce Type: replace-cross Abstract: Accurate scene perception is critical for vision-based robotic manipulation. Existing approaches typically follow either a Vision-to-Action (V

tutorialsarxiv-cs-cv

Local Ai

HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation

arXiv:2605.23043v1 Announce Type: new Abstract: Agentic text-simulation systems write in sequence, with each item becoming possible context for later steps. That makes uncertainty path-dependent: an e

local-aiarxiv-cs-cl

How Far Are We from Generating Missing Modalities with Foundation Models?

model-releasesarxiv-cs-cl

arXiv:2506.03530v3 Announce Type: replace-cross Abstract: Multimodal foundation models have demonstrated impressive capabilities across diverse tasks. However, their potential as plug-and-play solutio

Local Ai

How Far Will They Go? Red-Teaming Online Influence with Large Language Models

arXiv:2605.22880v1 Announce Type: cross Abstract: As large language model (LLM)-based agents increasingly participate in online discourse, red-teaming their capacity to support political influence cam

local-aiarxiv-cs-ai

Applications

Knowledge Distillation for Low-Resource Open-source Text-to-SQL Model

arXiv:2605.22843v1 Announce Type: new Abstract: Text-to-SQL converts natural language questions into executable SQL queries, enabling non-technical users to access relational databases for analytics a

applicationsarxiv-cs-cl

Learning a Particle Dynamics Model with Real-world Videos

arXiv:2605.23845v1 Announce Type: new Abstract: Data-driven learning approaches for physics simulation, sometimes referred to as world models, have emerged as promising alternatives to traditional phy

tutorialsarxiv-cs-cv

Learning-Augmented Online Scheduling with Parsimonious Preemption

arXiv:2605.23255v1 Announce Type: new Abstract: Learning-augmented algorithms have emerged as a powerful paradigm to surpass traditional worst-case lower bounds by integrating potentially noisy predic

Less Effort, Shorter Proofs: Reinforcement Learning for Security Protocol Analysis in Tamarin

arXiv:2605.23643v1 Announce Type: cross Abstract: Tools like Tamarin and ProVerif have achieved notable success in analyzing and verifying complex real-world protocols such as EMV, 5G, and WPA2, even

tutorialsarxiv-cs-lg

Leveraging Foundation Models for Causal Generative Modeling

arXiv:2605.23861v1 Announce Type: cross Abstract: Causal generative modeling is essential for developing reliable and transparent AI systems capable of counterfactual reasoning. While existing approac

researcharxiv-cs-ai

LFRAG: Layout-oriented Fine-grained Retrieval-Augmented Generation on Multimodal Document Understanding

arXiv:2605.22829v1 Announce Type: cross Abstract: Multimodal Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for enhancing Large Language Models (LLMs) with external knowledg

Low-Cost Hard-Label Adversarial Attack with Theoretical Foundations

arXiv:2601.14300v3 Announce Type: replace Abstract: Hard-label black-box attacks, relying solely on top-1 predictions, represent one of the most challenging yet practically threat models. Despite rece

MedExpMem: Adapting Experience Memory for Differential Diagnosis

arXiv:2605.22872v1 Announce Type: cross Abstract: Experienced physicians develop diagnostic expertise through clinical practice, acquiring not only disease knowledge but also the ability to differenti

Agents

MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection

arXiv:2605.23723v1 Announce Type: new Abstract: Large language model agents increasingly rely on persistent memory to store past interactions, retrieve relevant demonstrations, and improve long-horizo

agentsarxiv-cs-ai

Non-normal spectral signatures of instability in neural network training dynamics

arXiv:2605.23476v1 Announce Type: new Abstract: Training instabilities in deep networks - loss spikes, oscillatory convergence, and gradient pathologies - are empirically prevalent but lack a rigorous

Nonlinear Transformations Against Unlearnable Datasets

arXiv:2406.02883v2 Announce Type: replace Abstract: Automated scraping stands out as a common method for collecting data in deep learning models without the authorization of data owners. Recent studie

tutorialsarxiv-cs-lg

Occlusion-Aware Physics-Semantic Keyframe Selection for Robust Video Editing

arXiv:2605.23192v1 Announce Type: new Abstract: Video editing has recently achieved remarkable progress with diffusion-based generative models, enabling diverse object-level manipulations from natural

researcharxiv-cs-cv

PACE: Two-Timescale Self-Evolution for Small Language Model Agents

arXiv:2605.23019v1 Announce Type: new Abstract: Deploying language-model agents in production often requires substantial compute and human effort to tune prompts, parsers, validators, and other compon

Philosophical Dispositions as Behavioral Constraints for AI-Assisted Code Review: An Empirical Study

arXiv:2605.23108v1 Announce Type: cross Abstract: AI-assisted code review tools typically operate as generic 'expert reviewer' agents, producing homogeneous findings regardless of the analysis type ne

ReCoVer: Resilient LLM Pre-Training System via Fault-Tolerant Collective and Versatile Workload

arXiv:2605.11215v2 Announce Type: replace-cross Abstract: Pre-training large language models on massive GPU clusters has made hardware faults routine rather than rare, driving the need for resilient t

Sparse Compositional Flow Matching by geometric assembly from motion primitives

arXiv:2605.23341v1 Announce Type: cross Abstract: Embodied trajectories, such as the executable motion sequences of robotic manipulators, underwater vehicles, and mobile robots, are a fundamental outp

researcharxiv-cs-ai

Transcoders Trace Visual Grounding and Hallucinations in Vision-Language Models

arXiv:2605.22902v1 Announce Type: cross Abstract: Generative Vision-Language Models (VLMs) perform well on multimodal reasoning, but how visual inputs are transformed to text remains poorly understood

Transform-Invariant Generative Ray Path Sampling for Efficient Radio Propagation Modeling

arXiv:2603.01655v2 Announce Type: replace Abstract: Ray tracing has become a standard for accurate radio propagation modeling, but suffers from exponential computational complexity, as the number of c

U-CESE: Unified Clip-based Event Search Engine for AI Challenge HCMC 2025

arXiv:2605.23274v1 Announce Type: new Abstract: Retrieving events from large-scale video datasets is challenging due to complex temporal, spatial, and multimodal information. This paper presents U-CES

researcharxiv-cs-cv

USIM and U0: A Vision-Language-Action Dataset and Model for General Underwater Robots

arXiv:2510.07869v4 Announce Type: replace Abstract: Underwater environments pose unique challenges for robotic navigation and manipulation. While existing research has primarily focused on task-specif

researcharxiv-cs-ro

When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization

arXiv:2605.23272v1 Announce Type: cross Abstract: Symbolic Regression (SR) plays a central role in scientific knowledge discovery by distilling mathematical equations from observational data. Most exi

Adaptive RBF-KAN: A Comparative Evaluation of Dynamic Shape Parameters in Kolmogorov-Arnold Networks

arXiv:2605.21534v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) approximate multivariate functions using learnable univariate edge functions, typically parameterized by B-spline ba

AgForce Enables Antigen-conditioned Generative Antibody Design

arXiv:2605.21610v1 Announce Type: new Abstract: Antibody design methods condition on antigen structure to generate complementarity-determining regions (CDR), yet a systematic evaluation of baseline me

ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models

arXiv:2605.22222v1 Announce Type: new Abstract: Partial differential equation (PDE) foundation models are pretrained networks that forecast how physical fields like velocity and pressure evolve from a

Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration

arXiv:2510.16590v2 Announce Type: replace Abstract: Applications of machine learning in chemistry are often limited by the scarcity and expense of labeled data, restricting traditional supervised meth

Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models

arXiv:2603.02938v2 Announce Type: replace Abstract: Graph-based tasks in the zero-shot setting remain a significant challenge due to data scarcity and the inability of traditional Graph Neural Network