Search: “framework”

Complete-muE: Optimal Hyperparameter Transfer and Scaling for MoE Models

arXiv:2605.23893v1 Announce Type: new Abstract: We propose Complete-muE, a framework which targets hyperparameter transfer across dense FFN and any Mixture-of-Experts (MoE) setups in transformer block

General Hazard Detection

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

DGX agent

arXiv:2605.23304v1 Announce Type: new Abstract: Hazard, as an abstract concept, is typically defined through cognitive-level logical reasoning rather than concrete examples. In contrast, existing haza

safetyarxiv-cs-cv

25 May 2026

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning

arXiv:2510.04567v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise

researcharxiv-cs-ai

Hidden Human-Like Nature of Machine-Generated Texts: Theory and Detection Enhancement

arXiv:2605.23190v1 Announce Type: new Abstract: Machine-generated texts (MGTs) produced by large language models (LLMs) are increasingly prevalent across various applications, while their potential mi

researcharxiv-cs-cl

LangFlash: Feed-forward 3D Language Gaussian Splatting from Sparse Unposed Images

arXiv:2605.23287v1 Announce Type: new Abstract: We present LangFlash, a feed-forward framework for 3D Language Gaussian Splatting that reconstructs 3D scenes parameterized by Gaussian primitives enric

Naturalistic measure of social norms alignment

arXiv:2605.23420v1 Announce Type: new Abstract: Social norms reflect shared expectations on acceptable behavior. Measuring social norms alignment remains challenging, with existing approaches typicall

safetyarxiv-cs-cl

Operator-Based Generalization Bound for Deep Learning: Insights on Multi-Task Learning

arXiv:2512.19184v2 Announce Type: replace-cross Abstract: This paper presents novel generalization bounds for vector-valued neural networks and deep kernel methods, focusing on multi-task learning thr

researcharxiv-cs-ai

Orbax: Distributed Checkpointing with JAX

arXiv:2605.23066v1 Announce Type: cross Abstract: In a landscape of high-performance distributed ML systems, JAX has emerged as a framework of choice. However, JAX's modular design philosophy leaves i

Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Melt Pool Dynamics in Laser Powder Bed Fusion

arXiv:2506.20537v3 Announce Type: replace Abstract: Efficient simulation of Laser Powder Bed Fusion (LPBF) is crucial for process prediction due to the lasting issue of high computational cost associa

PLACE: Prompt Learning for Attributed Community Search in Large Graphs

arXiv:2507.05311v2 Announce Type: replace-cross Abstract: In this paper, we propose PLACE (Prompt Learning for Attributed Community Search), an innovative graph prompt learning framework for ACS. Enli

applicationsarxiv-cs-ai

Spatio-Temporal Forecasting of Retaining Wall Deformation: Mitigating Error Accumulation via Multi-Resolution ConvLSTM Stacking Ensemble

arXiv:2603.10453v2 Announce Type: replace Abstract: This study proposes a multi-resolution Convolutional Long Short-Term Memory (ConvLSTM) ensemble framework that leverages diverse temporal input reso

Tutorials

Steered Generation via Gradient-Based Optimization on Sparse Query Features

arXiv:2605.23040v1 Announce Type: new Abstract: Latent steering exploits internal representations of Large Language Models (LLMs) to guide generation, yet interventions on dense states can entangle di

tutorialsarxiv-cs-lg

Training-Free Rate-Distortion-Perception Traversal With Diffusion

arXiv:2603.04005v2 Announce Type: replace-cross Abstract: The rate-distortion-perception (RDP) tradeoff characterizes the fundamental limits of lossy compression by jointly considering bitrate, recons

A Tale of Two Cities: Pessimism and Opportunism in Offline Dynamic Pricing

arXiv:2411.08126v2 Announce Type: replace-cross Abstract: We study offline dynamic pricing when historical data provide incomplete coverage of the price space such that some candidate prices, includin

Can Breath Biomarkers Causally Influence Blood Glucose? Investigating VOC-Mediated Modulation in Diabetes

arXiv:2605.22075v1 Announce Type: new Abstract: Diabetes is a global health burden, and early detection is critical for timely intervention. This study explores a non-invasive, data-driven framework t

Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost

arXiv:2605.22502v1 Announce Type: cross Abstract: Agent orchestration frameworks have proliferated, collectively exceeding 290,000 GitHub stars across LangGraph, CrewAI, Google ADK, OpenAI Agents SDK,

DualOptim+: Bridging Shared and Decoupled Optimizer States for Better Machine Unlearning in Large Language Models

arXiv:2605.21539v1 Announce Type: new Abstract: We propose DualOptim+, a novel optimization framework for improving machine unlearning in large language models. It introduces a base state to capture c

Dynamic Mixture of Latent Memories for Self-Evolving Agents

arXiv:2605.21951v1 Announce Type: new Abstract: Achieving self-evolution in intelligent agents requires the continual accumulation of new knowledge across changing task sequences without forgetting pr

Equilibrium Propagation and Hamiltonian Inference in the Diffusive Fitzhugh-Nagumo Model

arXiv:2605.21568v1 Announce Type: new Abstract: In this work, we extend the Equilibrium Propagation framework to skew-gradient systems and show an equivalence between deep Energy-Based Models and Hami

Hardware

From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal k-Sparse GLMs

arXiv:2605.22188v1 Announce Type: new Abstract: GPUs have significantly accelerated first-order methods for large-scale optimization, especially in continuous optimization. However, this success has n

hardwarearxiv-cs-lg

Generative Modeling by Value-Driven Transport

arXiv:2605.22507v1 Announce Type: new Abstract: We propose a new framework for generative modeling based on a discrete-time stochastic control formulation of measure transport. Adapting classic result

I-SAFE: Wasserstein Coherence Metrics for Structural Auditing of Scientific AI Models

arXiv:2605.21731v1 Announce Type: new Abstract: Deep learning models are increasingly used in scientific prediction tasks where strong benchmark performance is often interpreted as evidence of scienti

Noise Schedule Design for Diffusion Models: An Optimal Control Perspective

arXiv:2605.21911v1 Announce Type: new Abstract: We develop a principled framework for analyzing and designing noise schedules in diffusion models. We show that one can recast this design problem as an

Self-Supervised ConvLSTM for Fermi Large Area Telescope Transient Detection

arXiv:2605.22112v1 Announce Type: cross Abstract: We present a framework for detecting transient gamma-ray phenomena in a controlled environment by combining end-to-end simulations of the Fermi-LAT sk

Temporal Contrastive Transformer for Financial Crime Detection: Self-Supervised Sequence Embeddings via Predictive Contrastive Coding

arXiv:2605.21490v1 Announce Type: new Abstract: We introduce the Temporal Contrastive Transformer (TCT), a representation learning framework designed to capture contextual temporal dynamics in sequenc

applicationsarxiv-cs-lg

Toward Understanding Adversarial Distillation: Why Robust Teachers Fail

arXiv:2605.21999v1 Announce Type: new Abstract: Adversarial Distillation aims to enhance student robustness by guiding the student with a robust teacher's soft labels within the min-max adversarial tr

Zero-shot adaptation to order book dynamics

arXiv:2605.21707v1 Announce Type: cross Abstract: We describe an adaptive market-making architecture that preserves the analytical structure of the Avellaneda--Stoikov framework while introducing a su

Beyond Benchmark Islands: Toward Representative Trustworthiness Evaluation for Agentic AI

arXiv:2603.14987v2 Announce Type: replace Abstract: Agentic AI systems increasingly act through tool-augmented, multi-step workflows whose failures (unsafe tool use, unauthorised actions, social harm)

CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation

model-releasesarxiv-cs-ro

arXiv:2605.22082v1 Announce Type: new Abstract: We present CoRMA(Contrastive Robotic Motor Adaptation), a context-based meta-adaptation framework that modifies RMA for force-dominant assembly. CoRMA r

Discovering Implicit Large Language Model Alignment Objectives

arXiv:2602.15338v2 Announce Type: replace-cross Abstract: Large language model (LLM) alignment relies on complex reward signals that often obscure the specific behaviors being incentivized, creating c

safetyarxiv-cs-cl

Local Ai

Look-Closer-Then-Diagnose: Confidence-Aware Ultrasound VQA via Active Zooming

arXiv:2605.21652v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have significantly advanced medical visual question answering, yet their performance in ultrasound remains suboptimal. In

local-aiarxiv-cs-cv

Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles

arXiv:2605.22177v1 Announce Type: cross Abstract: The proliferation of large language models (LLMs) and modular skills has endowed autonomous agents with increasingly powerful capabilities. Existing f

Modeling Pathology-Like Behavioral Patterns in Language Models Through Behavioral Fine-Tuning

arXiv:2605.22356v1 Announce Type: new Abstract: Large language models are increasingly used as computational tools for modeling human-like behavior. We introduce a behavioral induction framework that

safetyarxiv-cs-cl

PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models

arXiv:2605.22311v1 Announce Type: new Abstract: Identity-conditioned diffusion models enable high-quality and identity-consistent face generation, but they also raise severe privacy concerns, as model

PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions

arXiv:2605.20206v1 Announce Type: cross Abstract: NIST's Privacy Risk Assessment Methodology (PRAM) provides a structured framework for privacy experts to assess privacy risks. However, its complexity

applicationsarxiv-cs-ai

Temporal Coding as a Substrate for Sensorimotor Object Inference: A Spiking Reinterpretation of Thousand Brains Architecture

model-releasesarxiv-cs-ro

arXiv:2605.22206v1 Announce Type: cross Abstract: The Thousand Brains Theory (TBT) and its open-source Monty framework model object recognition through sensorimotor inference -- identifying objects by

Translating Signals to Languages for sEMG-Based Activity Recognition

arXiv:2605.22403v1 Announce Type: new Abstract: Surface electromyography (sEMG) signal-based activity recognition has attracted increasing research attention in recent years. To develop accurate sEMG

Advantage Collapse in Group Relative Policy Optimization: Diagnosis and Mitigation

arXiv:2605.21125v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO), a prominent algorithm within the Reinforcement Learning from Verifiable Rewards (RLVR) framework, has achieve

ChunkFT: Byte-Streamed Optimization for Memory-Efficient Full Fine-Tuning

arXiv:2605.21177v1 Announce Type: cross Abstract: This work presents extsc{ChunkFT}, a memory-efficient fine-tuning framework that reformulates full-parameter fine-tuning around a dynamically activate

Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves

arXiv:2605.06395v2 Announce Type: replace Abstract: Modern deep learning architectures increasingly contend with sophisticated signals that are natively infinite-dimensional, such as time series, prob

applicationsarxiv-cs-lg

ELEMENT: Multi-Modal Retinal Vessel Segmentation Based on a Coupled Region Growing and Machine Learning Approach

arXiv:2605.20458v1 Announce Type: new Abstract: Vascular structures in the retina contain important information for the detection and analysis of ocular diseases, including age-related macular degener

Local Ai

Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models

arXiv:2602.16608v2 Announce Type: replace Abstract: Transformer models achieve state-of-the-art performance across domains and tasks, yet their deeply layered representations make their predictions di

local-aiarxiv-cs-cl

Gaze into the Details: Locality-Sensitive Enhancement for OCTA Retinal Vessel Segmentation

arXiv:2605.20651v1 Announce Type: new Abstract: Existing deep learning frameworks for Optical Coherence Tomography Angiography (OCTA) vessel segmentation are largely derived from the U-Net architectur

GraphCSVAE: Graph Categorical Structured Variational Autoencoder for Spatiotemporal Auditing of Physical Vulnerability Towards Sustainable Post-Disaster Risk Reduction

arXiv:2509.10308v2 Announce Type: replace Abstract: In the aftermath of disasters, many institutions worldwide face challenges in monitoring changes in disaster risk, limiting assessment of progress t

Humanoid Whole-Body Manipulation via Active Spatial Brain and Generalizable Action Cerebellum

model-releasesarxiv-cs-ro

arXiv:2605.21133v1 Announce Type: new Abstract: In this paper, we explore spatial-aware humanoid whole-body manipulation task. Compared with tabletop settings, this task poses two key challenges: 1) S

Local Ai

Interaction Locality in Hierarchical Recursive Reasoning

arXiv:2605.20784v1 Announce Type: cross Abstract: Spatial reasoning requires both location-bound computation and location-invariant structure: agents must make local moves while preserving route, obje

local-aiarxiv-cs-lg

Lean Refactor: Multi-Objective Controllable Proof Optimization via Agentic Strategy Search

arXiv:2605.20244v1 Announce Type: cross Abstract: We present Lean Refactor, a plug-and-play retrieval-augmented agentic framework for multi-objective, controllable, and version-robust refactoring of L

Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inference

arXiv:2511.21223v2 Announce Type: replace-cross Abstract: Variational inference (VI) is a cornerstone of modern Bayesian learning, enabling approximate inference in complex models. However, its formul