Search: “applications”

Additive Atomic Forests for Symbolic Function and Antiderivative Discovery

arXiv:2605.08130v1 Announce Type: new Abstract: We present a framework for the simultaneous symbolic recovery of a function and its antiderivative from data. The framework rests on three ideas. First,

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Agents

Agent-First Tool API: A Semantic Interface Paradigm for Enterprise AI Agent Systems

DGX agent

arXiv:2605.10555v1 Announce Type: new Abstract: As AI agents transition from research prototypes to enterprise production systems, the tool interfaces they consume remain rooted in human-oriented CRUD

agentsarxiv-cs-ai

12 May 2026

Agents

Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery

arXiv:2605.08956v1 Announce Type: new Abstract: A growing body of work pursues AI scientists capable of end-to-end autonomous scientific discovery. This position paper argues that although they alread

agentsarxiv-cs-ai

Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks

arXiv:2605.10590v1 Announce Type: cross Abstract: Causal sensitivity analysis aims to provide bounds for causal effect estimates in the presence of unobserved confounding. However, existing methods fo

Arcane: An Assertion Reduction Framework through Semantic Clustering and MCTS-Guided Rule Exploring

arXiv:2605.10107v1 Announce Type: new Abstract: Assertion-based Verification (ABV) is essential for ensuring that hardware designs conform to their intended specifications. However, existing automated

ASACK : Adaptive Safe Active Continual Koopman Learning for Uncertain Systems with Contractive Guarantees

arXiv:2605.09659v1 Announce Type: new Abstract: Koopman operator theory provides a powerful framework for representing nonlinear dynamics through a linear operator acting on lifted observables, enabli

safetyarxiv-cs-ro

Tutorials

Attention-based graph neural networks: a survey

arXiv:2605.08679v1 Announce Type: cross Abstract: Graph neural networks (GNNs) aim to learn well-trained representations in a lower-dimension space for downstream tasks while preserving the topologica

tutorialsarxiv-cs-ai

Attention Itself Could Retrieve.RetrieveVGGT: Training-Free Long Context Streaming 3D Reconstruction via Query-Key Similarity Retrieval

arXiv:2605.09644v1 Announce Type: new Abstract: Visual Geometry Grounded Transformer (VGGT) advances 3D reconstruction via scalable Transformer architecture, but the quadratic complexity of global att

researcharxiv-cs-cv

BabelDOC: Better Layout-Preserving PDF Translation via Intermediate Representation

arXiv:2605.10845v1 Announce Type: cross Abstract: As global cross-lingual communication intensifies, language barriers in visually rich documents such as PDFs remain a practical bottleneck. Existing d

BaLoRA: Bayesian Low-Rank Adaptation of Large Scale Models

arXiv:2605.08110v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has become the standard for fine-tuning large pre-trained models at reduced computational cost. However, its low-rank point

Beta Sampling is All You Need: Efficient Image Generation Strategy for Diffusion Models using Stepwise Spectral Analysis

arXiv:2407.12173v2 Announce Type: replace-cross Abstract: Generative diffusion models have emerged as a powerful tool for high-quality image synthesis, yet their iterative nature demands significant c

Breaking Contextual Inertia: Reinforcement Learning with Single-Turn Anchors for Stable Multi-Turn Interaction

arXiv:2603.04783v2 Announce Type: replace Abstract: While LLMs demonstrate strong reasoning capabilities when provided with full information in a single turn, they exhibit substantial vulnerability in

CAGS: Color-Adaptive Volumetric Video Streaming with Dynamic 3D Gaussian Splatting

arXiv:2605.09279v1 Announce Type: cross Abstract: Volumetric video (VV) streaming enables real-time, immersive access to remote 3D environments, powering telepresence, ecological monitoring, and robot

researcharxiv-cs-cv

Can Language Models Identify Side Effects of Breast Cancer Radiation Treatments?

arXiv:2605.08439v1 Announce Type: new Abstract: Accurately communicating the side effects of cancer treatments to cancer survivors is critical, particularly in settings such as informed consent, where

researcharxiv-cs-cl

Character-Level Transformer for Tajik-Persian Transliteration with a Parallel Lexical Corpus

arXiv:2605.09092v1 Announce Type: new Abstract: This study addresses automatic transliteration from Tajik (Cyrillic script) to Persian (Perso-Arabic script). We present a curated, lexicographically ve

ChatbotManip: A Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour

arXiv:2506.12090v2 Announce Type: replace Abstract: This paper introduces ChatbotManip, a novel dataset for studying manipulation in Chatbots. It contains simulated generated conversations between a c

CNSocialDepress: A Chinese Social Media Dataset for Depression Risk Detection and Structured Analysis

arXiv:2510.11233v3 Announce Type: replace Abstract: Depression is a pressing global public health issue, yet publicly available Chinese-language resources for depression risk detection remain scarce a

Coarsening Linear Non-Gaussian Causal Models with Cycles

arXiv:2605.10163v1 Announce Type: cross Abstract: Recent work on causal abstraction, in particular graphical approaches focusing on causal structure between clusters of variables, aims to summarize a

Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning

arXiv:2605.08885v1 Announce Type: new Abstract: SO(3) equivariant graph neural networks have become the dominant paradigm for atomistic foundation models, achieving high accuracy and data efficiency b

Comparing Two Proxy Methods for Causal Identification

arXiv:2512.00175v3 Announce Type: replace-cross Abstract: Identifying causal effects in the presence of unmeasured variables is a fundamental challenge in causal inference, for which proxy variable me

Computer Use at the Edge of the Statistical Precipice

arXiv:2605.08261v1 Announce Type: cross Abstract: Evaluating Computer Use Agents (CUAs) on interactive environments is fraught with methodological pitfalls that the field has yet to systematically add

Concise Geometric Description as a Bridge: Unleashing the Potential of LLM for Plane Geometry Problem Solving

arXiv:2601.21164v3 Announce Type: replace Abstract: Plane Geometry Problem Solving (PGPS) is a multimodal reasoning task that aims to solve a plane geometric problem based on a geometric diagram and p

ConfoundingSHAP: Quantifying confounding strength in causal inference

arXiv:2605.10533v1 Announce Type: new Abstract: In causal inference, confounders are variables that influence both treatment decisions and outcomes. However, unlike as in randomized clinical trials, t

Consensus Sampling for Safer Generative AI

arXiv:2511.09493v2 Announce Type: replace Abstract: Motivated by undetectable risks in generative AI, we study a general robust aggregation problem: how to aggregate several probability distributions

Consolidation-Expansion Operator Mechanics:A Unified Framework for Adaptive Learning

arXiv:2605.09968v1 Announce Type: new Abstract: Every adaptive learning system must alternate between two operations: consolidating what it already knows and expanding into new evidence. We propose Co

Core-Halo Decomposition: Decentralizing Large-Scale Fixed-Point Problems

arXiv:2605.08681v1 Announce Type: cross Abstract: We study solving large-scale fixed-point equation (x^star=ar F(x^star)) with decomposition. Standard strict decomposition assigns each agent a disjoin

Local Ai

Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys

arXiv:2605.08103v1 Announce Type: cross Abstract: High-entropy alloys (HEAs) have attracted growing attention for their exceptional mechanical and thermal properties arising from complex atomic config

local-aiarxiv-cs-ai

cuRegOT: A GPU-Accelerated Solver for Entropic-Regularized Optimal Transport

arXiv:2605.08793v1 Announce Type: cross Abstract: Optimal transport (OT) has emerged as a fundamental tool in modern machine learning, yet its computational cost remains a significant bottleneck for l

Deep set based operator learning with uncertainty quantification

arXiv:2509.25646v2 Announce Type: replace Abstract: Learning operators from data is central to scientific machine learning. While DeepONets are widely used for their ability to handle complex domains,

Agents

Delivering Science as a Service: Sci-Orchestra's Cloud-Native Approach to HPC

arXiv:2605.08396v1 Announce Type: new Abstract: The increasing complexity of modern computational environments often burdens researchers with infrastructure management, authentication protocols, and c

agentsarxiv-cs-cv

dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models

arXiv:2605.09291v1 Announce Type: new Abstract: Discrete flow models (DFMs) are a class of flexible generative models for generating discrete data, and diffusion large language models (dLLMs) can be v

safetyarxiv-cs-lg

Differentially Private Sampling from Distributions via Wasserstein Projection

arXiv:2605.10015v1 Announce Type: cross Abstract: In this paper, we study the problem of sampling from a distribution under the constraint of differential privacy (DP). Prior works measure the utility

Domain-Adaptive Arrhythmia Classification Using a Hybrid Transformer on Wearable Heart Signals

arXiv:2605.08199v1 Announce Type: cross Abstract: Cardiovascular disease remains the leading cause of death globally, underscoring the need for effective, accessible monitoring solutions, particularly

Local Ai

Dystruct: Dynamically Structured Diffusion Language Model Decoding via Bayesian Inference

arXiv:2605.09820v1 Announce Type: new Abstract: Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive models, primarily due to their ability to enable par

local-aiarxiv-cs-lg

Local Ai

Efficient LLM Collaboration via Planning

arXiv:2506.11578v4 Announce Type: replace Abstract: Recently, large language models (LLMs) have demonstrated strong performance, ranging from simple to complex tasks. However, while large models achie

local-aiarxiv-cs-ai

Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians

arXiv:2410.01656v2 Announce Type: replace-cross Abstract: We study the estimation of distributional parameters when samples are shown only if they fall in some unknown set S subseteq R^d. Kontonis, Tz

Explanation-Aware Learning for Enhanced Interpretability in Biomedical Imaging

arXiv:2605.10054v1 Announce Type: new Abstract: Deep neural networks for medical image diagnosis often achieve high predictive accuracy while relying on spurious or clinically irrelevant visual cues,

safetyarxiv-cs-cv

Exploring 6D Object Pose Estimation with Deformation

arXiv:2604.06720v2 Announce Type: replace Abstract: We present DeSOPE, a large-scale dataset for 6DoF deformed objects. Most 6D object pose methods assume rigid or articulated objects, an assumption t

researcharxiv-cs-cv

FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings

arXiv:2605.08198v1 Announce Type: cross Abstract: We present FairHealth, an open-source Python library that provides a unified, modular framework for trustworthy machine learning in healthcare applica

Fashion130K: An E-commerce Fashion Dataset for Outfit Generation with Unified Multi-modal Condition

model-releasesarxiv-cs-cv

arXiv:2605.10127v1 Announce Type: new Abstract: Recent research work on fashion outfit generation focuses on promoting visual consistency of garments by leveraging key information from reference image

Tools

Fast mode for Opus 4.7 available on AI Gateway

Vercel has introduced a fast mode option for Claude Opus 4.7 on its AI Gateway service, enabling faster response times for users integrating the model through their platform. This feature likely provi

toolsvercel-blog

Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability

arXiv:2605.09214v1 Announce Type: cross Abstract: Kullback-Leibler (KL) regularization is ubiquitous in reinforcement learning algorithms in the form of reverse or forward KL. Recent studies have demo

Few-Click-Driven Interactive 3D Segmentation with Semantic Embedding

arXiv:2605.08925v1 Announce Type: new Abstract: Interactive segmentation allows efficient label generation by leveraging user-provided clicks to progressively refine predictions, which is critical whe

safetyarxiv-cs-cv

FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting

model-releasesarxiv-cs-lg

arXiv:2502.18834v2 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can

Fixed-Point Neural Optimal Transport without Implicit Differentiation

arXiv:2605.10792v1 Announce Type: cross Abstract: We propose an implicit neural formulation of optimal transport that eliminates adversarial min--max optimization and multi-network architectures commo

From Traditional Taggers to LLMs: A Comparative Study of POS Tagging for Medieval Romance Languages

arXiv:2605.09147v1 Announce Type: cross Abstract: Part-of-speech (POS) tagging for Medieval Romance languages remains challenging due to orthographic variation, morphological complexity, and limited a

Generalized Category Discovery in Federated Graph Learning

arXiv:2605.08178v1 Announce Type: cross Abstract: Federated Graph Learning (FGL) enables collaborative learning over distributed graph data, yet existing approaches largely rely on a closed-world assu

GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Tasks

model-releasesarxiv-cs-cv

arXiv:2605.10645v1 Announce Type: new Abstract: Data-driven medical AI is traditionally formulated as a discriminative mapping from input X to output Y via a learned function f, which does not general