Search: “framework”

Cost-Sensitive Evaluation for Binary Classifiers

model-releasesarxiv-cs-lg

arXiv:2510.22016v2 Announce Type: replace Abstract: Selecting an appropriate evaluation metric for classifiers is crucial for model comparison, parameter optimization, and deployment decisions, yet th

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Decoupling Skeleton and Flesh: Efficient Multimodal Table Reasoning with Disentangled Alignment and Structure-aware Guidance

DGX agent

arXiv:2602.03491v2 Announce Type: replace-cross Abstract: Reasoning over table images remains challenging for Large Vision-Language Models (LVLMs) due to complex layouts and tightly coupled structure-

local-aiarxiv-cs-cl

28 May 2026

Disentangling Adversarial Prompts: A Semantic-Graph Defense for Robust LLM Security

arXiv:2605.27823v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly vulnerable to adversarial prompts that exploit semantic ambiguities to bypass safety mechanisms, resulti

safetyarxiv-cs-ai

Evaluating the Generation Capabilities of Large Chinese Language Models

arXiv:2308.04823v5 Announce Type: replace Abstract: This paper unveils CG-Eval, the first-ever comprehensive and automated evaluation framework designed for assessing the generative capabilities of la

researcharxiv-cs-cl

From Learning Resources to Competencies: LLM-Based Tagging with Evidence and Graph Constraints

arXiv:2605.28483v1 Announce Type: new Abstract: Linking learning resources to a structured competency framework is key to enabling competency-based search and curriculum analytics in Learning Manageme

safetyarxiv-cs-ai

Gradient Transformer: Learning to Generate Updates for LLMs

model-releasesarxiv-cs-lg

arXiv:2605.27591v1 Announce Type: new Abstract: Many organizations lack computational resources to fine-tune large language models (LLMs) on private (unshareable) data for better utility, while fine-t

Hermes Agent v0.15.0 - “The Velocity Release” Changelog below

Hermes Agent v0.15.0, released by Nous Research as 'The Velocity Release,' represents an update to their Hermes Agent framework that likely focuses on performance improvements and speed optimizations.

agentsnous-research--x

High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention

arXiv:2605.27758v1 Announce Type: cross Abstract: Automotive crashworthiness optimization remains a safety-critical challenge, requiring the management of large-scale nonlinear structural deformations

safetyarxiv-cs-ai

Tutorials

How to secure your vibecoded app in 4 steps 🔒 Speed without security is a liability. Here's how to ship without leaving the back door open …

This post from Replit outlines a 4-step security framework for protecting vibecoded applications in production environments. It emphasizes that application speed must not come at the expense of securi

tutorialsreplit--x

Imitating and Finetuning Model Predictive Control for Robust and Symmetric Quadrupedal Locomotion

arXiv:2311.02304v3 Announce Type: replace Abstract: Control of legged robots is a challenging problem that has been investigated by different approaches, such as model-based control and learning algor

safetyarxiv-cs-ro

Inpainting-Style Conditional Diffusion for Multivariable Time Series Forecasting

model-releasesarxiv-cs-cv

arXiv:2605.28324v1 Announce Type: new Abstract: In this paper, we propose a novel conditional diffusion-based framework for multivariable time-series solar power forecasting. The proposed method refor

Tutorials

Learning Correlated Reward Models: Statistical Barriers and Opportunities

arXiv:2510.15839v2 Announce Type: replace Abstract: Random Utility Models (RUMs) are a classical framework for modeling user preferences and play a key role in reward modeling for Reinforcement Learni

tutorialsarxiv-cs-lg

Learning Tangent Bundles and Characteristic Classes with Autoencoder Atlases

arXiv:2602.22873v2 Announce Type: replace-cross Abstract: We introduce a theoretical framework that connects multi-chart autoencoders in manifold learning with the classical theory of vector bundles a

On the Learnability of Test-Time Adaptation: A Recovery Complexity Perspective

arXiv:2605.28057v1 Announce Type: cross Abstract: Test-time adaptation (TTA) aims to adapt models to maintain reliable performance on non-stationary test streams without requiring labeled data. Despit

PEAM: Parametric Embodied Agent Memory through Contrastive Internalization of Experience in Minecraft

arXiv:2605.27762v1 Announce Type: new Abstract: We present PEAM, a Parametric Embodied Agent Memory framework in Minecraft that transforms agent memory from inference-time retrieval into parameter-res

PetroBench: A Benchmark for Large Language Models in Petroleum Engineering

arXiv:2605.28032v1 Announce Type: new Abstract: Large Language Models are increasingly applied in the petroleum industry, highlighting the need for a domain-specific evaluation framework. This study d

Semi-Supervised Hypothesis Testing by Betting on Predictions

arXiv:2605.28533v1 Announce Type: new Abstract: We introduce a testing-by-betting framework that leverages predictions on unlabeled data to enhance the power of sequential hypothesis testing. Given li

SNARE: Adaptive Scenario Synthesis for Eliciting Overeager Behavior in Coding Agents

arXiv:2605.28122v1 Announce Type: cross Abstract: A coding agent executes a benign task as a sequence of shell, file, and network actions, any of which can quietly exceed the authorized scope while th

Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey

arXiv:2605.27431v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) presents a naturally compatible and scalable framework for multimodal learning, demonstrating strong adaptability across dive

Triangular-Reference Schrodinger Bridges for Time Series Generation

arXiv:2605.27478v1 Announce Type: cross Abstract: We introduce Triangular-Reference Schrodinger Bridges for Time Series (TR-SBTS), a conservative extension of the SBTS framework in which the Brownian

Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation

arXiv:2605.28364v1 Announce Type: cross Abstract: Reinforcement learning with multinomial logistic (MNL) function approximation has become an important framework due to its flexibility and broad appli

VideoCanvas: Unified Video Completion from Arbitrary Spatiotemporal Patches via In-Context Conditioning

model-releasesarxiv-cs-cv

arXiv:2510.08555v2 Announce Type: replace Abstract: Existing controllable video generation methods are typically designed for rigid, task-specific settings, such as first-frame image-to-video, inpaint

AI Agent for Reverse-Engineering Legacy Finite-Difference Code and Translating to Devito

arXiv:2601.18381v2 Announce Type: replace Abstract: To facilitate the transformation of legacy finite difference implementations into the Devito environment, this study develops an integrated AI agent

agentsarxiv-cs-ai

BAIT: Boundary-Guided Disclosure Escalation via Self-Conditioned Reasoning

arXiv:2605.27110v1 Announce Type: cross Abstract: In this work, we propose BAIT (Boundary-Aware Iterative Trap), a three-step jailbreak framework that approaches malicious goals through internal discl

safetyarxiv-cs-cl

Applications

Closing the Loop in Teleoperation: Episode-Level Data Quality Assessment and Feedback for High-Quality Demonstration Collection

arXiv:2605.26349v1 Announce Type: new Abstract: Industrial automation is at a pivotal moment, as Physical AI is driving a transition from rigid, hand-engineered automation systems toward more flexible

applicationsarxiv-cs-ro

Constrained Bayesian Experimental Design via Online Planning

arXiv:2605.26990v1 Announce Type: cross Abstract: Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments. However, existing BED methods are un

safetyarxiv-cs-lg

DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendation

arXiv:2602.18907v2 Announce Type: replace-cross Abstract: We introduce DeepInterestGR, a novel framework that integrates deep interest mining into the generative recommendation pipeline. This addresse

researcharxiv-cs-cv

Applications

Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks

arXiv:2605.26290v1 Announce Type: new Abstract: Temporal signed networks (TSNs) model the time evolution of cooperative and adversarial relationships that arise in applications such as social media an

applicationsarxiv-cs-lg

Excited to dive into this - an open source agent designed with memory/continual learning in mind

This post likely announces or discusses an open-source AI agent framework that incorporates memory and continual learning capabilities, enabling the system to retain information across interactions an

agentsharrison-chase--x

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Experiments in Agentic AI for Science

arXiv:2605.26305v1 Announce Type: new Abstract: This paper details two novel frameworks for developing autonomous, agentic AI in scientific workflows. Both systems leverage a hybrid Local Body, Remote

local-aiarxiv-cs-ai

Exploring the robustness of TractOracle methods in RL-based tractography

arXiv:2507.11486v2 Announce Type: replace Abstract: Tractography algorithms leverage diffusion MRI to reconstruct the fibrous architecture of the brain's white matter. Among machine learning approache

Information Theoretic Perspective on Representation Learning

arXiv:2601.11334v2 Announce Type: replace-cross Abstract: An information-theoretic framework is introduced to analyze last-layer embedding, focusing on learned representations for regression tasks. We

Introducing Google AI Threat Defense to help you outpace the adversary

model-releasesgoogle-cloud-ai

aside_block <ListValue: [StructValue([('title', 'Summary of today’s news'), ('body', <wagtail.rich_text.RichText object at 0x7fb0f516f910>), ('btn_text', ''), ('href', ''), ('image', None)])]> AI-powe

Kan Extension Transformers: A Categorical Unification of Attention, Diffusion, and Predict-Detach Self-Conditioning

arXiv:2605.27259v1 Announce Type: new Abstract: We propose Kan Extension Transformers (KETs) as a unifying categorical framework for a diverse group of Transformer implementations. The core claim is t

L-Learning : A Lyapunov-Based Approach Leveraging Lagrangian Mechanics for Efficient and Stable Robot Tracking

arXiv:2605.26648v1 Announce Type: new Abstract: This paper presents L-Learning, a novel data-driven control framework for robotics that integrates Lyapunov stability theory with Lagrangian mechanics t

researcharxiv-cs-ro

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection

model-releasesarxiv-cs-lg

arXiv:2505.21285v4 Announce Type: replace Abstract: This work proposes a framework LGKDE that learns kernel density estimation for graphs. The key challenge in graph density estimation lies in effecti

Learning Reference-Guided Exposure Correction with Hybrid Illumination Characteristics

arXiv:2605.26729v1 Announce Type: new Abstract: We present HICNet, a reference-guided exposure correction framework. A lightweight, content-agnostic encoder distills each image into a compact illumina

researcharxiv-cs-cv

Multi-Modal Building Inspection via Perceiver IO Fusion of Satellite and Street-Level Imagery

arXiv:2605.26381v1 Announce Type: new Abstract: We present a multi-modal classification framework that fuses satellite and street-level imagery through a Perceiver IO architecture operating on spatial

researcharxiv-cs-cv

Neural Bayesian Sequential Routing

arXiv:2605.26147v1 Announce Type: new Abstract: Human decision-making is sequential and uncertainty-aware, yet standard neural networks often rely on static, dense forward computation with limited vis

agentsarxiv-cs-lg

OMD-GraphRAG: Enhancing GraphRAG with Ontology-Guided Extraction, Multi-Dimensional Clustering and Dual-Channel Fusion

arXiv:2603.25152v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems face significant challenges in complex reasoning, multi-hop queries, and domain-specific QA. While exis

Prospective evaluation of multimodal respiratory failure prediction: Do chest X-rays improve performance beyond EHR signals?

arXiv:2605.26255v1 Announce Type: cross Abstract: Early prediction of respiratory failure is critical for timely clinical intervention in intensive care units. Existing electronic health record (EHR)-

Hardware

SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning

arXiv:2605.26509v1 Announce Type: new Abstract: Gaussian processes (GPs) provide a principled Bayesian framework for uncertainty estimation, but their computational complexity severely limits scalabil

hardwarearxiv-cs-lg

Stateful Inference for Low-Latency Multi-Agent Tool Calling

arXiv:2605.26289v1 Announce Type: new Abstract: Multi-agent tool calling is becoming the dominant interaction pattern for LLM-based systems, yet existing inference frameworks treat each tool call as a

agentsarxiv-cs-lg

Stochastic Decision Horizons for Constrained Reinforcement Learning

arXiv:2602.04599v2 Announce Type: replace Abstract: We propose stochastic decision horizons (SDH), a theoretically grounded framework for solving constrained RL problems with every-step constraint sat

safetyarxiv-cs-lg

Tutorials

Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM

arXiv:2605.26405v1 Announce Type: new Abstract: Educational interventions are effective tools for enhancing student learning. While Large Language Models (LLMs) allow for generating adaptive feedback

tutorialsarxiv-cs-cl

Two Speeds of Learning: A Representation-Readout Decomposition of Grokking and Double Descent

arXiv:2605.27078v1 Announce Type: cross Abstract: Training loss and accuracy are the standard signals used to monitor generalization during deep neural network training. Two well-documented phenomena

Accelerating Bayesian inverse design in computational fluid dynamics using neural operators

arXiv:2605.26059v1 Announce Type: cross Abstract: Bayesian inverse design provides a principled framework for inferring aerodynamic geometries from sparse flow observations while quantifying uncertain

26 May 2026

Autoregression-Free Neural Operators for Time-Dependent PDEs

arXiv:2605.25413v1 Announce Type: cross Abstract: Neural operators learn mappings from function-dependent inputs to solutions, providing an effective framework for solving partial differential equatio