Search: “papers”

Teacher-Student Representational Alignment for Reinforcement Learning-Driven Imitation Learning

arXiv:2605.28372v1 Announce Type: new Abstract: Imitation learning (IL) from a state-based reinforcement learning (RL) policy is a common approach to overcome the curse of dimensionality in complex an

safetyarxiv-cs-lg

Research

The Computational Boundary of Inference: Capability Internalization, Training, and the Turing Jump

DGX agent

arXiv:2605.27381v1 Announce Type: cross Abstract: Claims about recursive self-improvement in AI often slide from repeated internal revision to the possibility of qualitatively stronger capability with

researcharxiv-cs-ai

28 May 2026

The Decision to Verify: How Warmth and User Characteristics Shape Reliance on Conversational Agents for Information Search

arXiv:2605.28498v1 Announce Type: cross Abstract: Conversational artificial intelligence (AI) provides an efficient and convenient gateway to information access. However, it can cause overreliance whe

TinyDejaVu: Smaller RAM and Faster Inference with Neural Networks on MCUs for Sensor Data Streams

arXiv:2512.09786v2 Announce Type: replace Abstract: Examples of embedded intelligence include a wide variety of tiny neural networks used on-board wireless sensors and actuators, which are expected to

Token Optimization Strategies for LLM-Based Oracle-to-PostgreSQL Migration

arXiv:2605.28557v1 Announce Type: cross Abstract: LLMs are increasingly used for software modernization, code translation, and database migration. However, LLM-based Oracle2PostgreSQL migration remain

Too many business leaders believe that AI says what it means. And it’s odd because we naturally attribute a high number of human traits to A…

model-releasesallie-k--miller--x

Too many business leaders believe that AI says what it means. And it’s odd because we naturally attribute a high number of human traits to AI, and yet we refuse to believe it can have hidden intent? 3

Turning Video Models into Generalist Robot Policies

arXiv:2605.27817v1 Announce Type: cross Abstract: Video generative models have emerged as a promising robotics backbone, capable of generating videos that depict the completion of complex tasks across

Agents

Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Study

arXiv:2506.08311v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) agents leverage Large Language Models (LLMs) to autonomously diagnose and fix software bugs through reasoning,

agentsarxiv-cs-ai

Hardware

Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation

arXiv:2605.27582v1 Announce Type: cross Abstract: Embodied navigation requires an agent to map language and visual observations to a stream of spatial actions that drive a real robot through environme

hardwarearxiv-cs-cv

Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding

arXiv:2602.13075v2 Announce Type: replace Abstract: Graph pre-training has achieved remarkable success in recent years, delivering transferable representations for downstream adaptation. However, most

applicationsarxiv-cs-lg

Unified Synthesis of Compositional Speech and Sound from Free-Form Text Prompts

arXiv:2605.28063v1 Announce Type: cross Abstract: Audio generation has made significant progress, yet synthesizing unified audio where speech and sounds are naturally composited remains a challenge. C

Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models

arXiv:2605.27376v1 Announce Type: cross Abstract: While prompt-based text-to-speech (TTS) models enable natural language-driven speaking style control, they often provide limited fine-grained control

Using Zero-Shot LLM-Generated Survey Data for Geographically Explicit Population Synthesis

arXiv:2605.27401v1 Announce Type: cross Abstract: There is a growing interest in utilizing synthetic populations for a diverse range of applications. At the same time, we are witnessing a tremendous g

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

When prompt perturbations break your A/B test: A valid statistical test for generative surveying

arXiv:2605.27463v1 Announce Type: cross Abstract: Generative surveying -- where collections of LLM-based personas provide feedback on messages -- has emerged as a cheap and scalable alternative to tra

When Think-with-Image Meets Safety: What Determines Multimodal Jailbreak Robustness?

arXiv:2605.27932v1 Announce Type: cross Abstract: Think-with-image reasoning is emerging as a new inference paradigm for large vision-language models, but its safety implications remain poorly underst

Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs

arXiv:2605.27913v1 Announce Type: new Abstract: Node classification on graphs often requires labeled nodes, yet obtaining labels at graph scale is expensive. When node attributes contain semantic cont

Which Pretraining Paradigm Better Serves Spatial Intelligence? An Empirical Comparison of Vision-Language and Video Generation Models

arXiv:2605.28132v1 Announce Type: new Abstract: Spatial intelligence requires visual representations that capture both semantic objects and geometric structure in the physical world. To support this,

tutorialsarxiv-cs-cv

XTransfer: Modality-Agnostic Few-Shot Model Transfer for Human Sensing at the Edge

model-releasesarxiv-cs-cv

arXiv:2506.22726v4 Announce Type: replace Abstract: Deep learning for human sensing on edge systems presents significant potential for smart applications. However, its training and development are hin

You Live More Than Once: Towards Hierarchical Skill Meta-Evolving

arXiv:2605.28390v1 Announce Type: new Abstract: Test-time skill evolving is regarded as a new paradigm for enhancing deployed agentic systems. Existing works mainly focus on hard-coded skill evolving

Zipping the Thought: When and How Compressed Reasoning Data Works in LLM Post-Training

arXiv:2605.28008v1 Announce Type: new Abstract: Large language models (LLMs) can now solve complex problems through long chain-of-thought (CoT) reasoning, but the trade-off between performance and tok

A Dataset of Robot-Patient and Doctor-Patient Medical Dialogues for Spoken Language Processing Tasks

arXiv:2605.26747v1 Announce Type: new Abstract: Large Language Models (LLMs) have brought huge improvements to Artificial Intelligence (AI), which can be applied to general-purpose tasks. However, the

A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection

arXiv:2605.26533v1 Announce Type: cross Abstract: Automated industrial inspection requires both precise defect localization and structured maintenance report generation; in current practice these task

A Multivariate Bernoulli-Based Sampling Method for Multi-Label Data with Application to Meta-Research

arXiv:2512.08371v4 Announce Type: replace Abstract: Datasets may contain observations with multiple labels. If the labels are not mutually exclusive, and if the labels vary greatly in frequency, obtai

A Physics-Informed Hierarchical Neural Network for Microwave Scattering Analysis of 3D PEC Targets

arXiv:2508.03774v5 Announce Type: replace-cross Abstract: Accurate modeling of scattering from three-dimensional (3D) perfectly electrically conducting (PEC) targets at microwave frequencies constitut

Adaptive Multi-prompt Contrastive Network for Few-shot Out-of-distribution Detection

arXiv:2506.17633v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection attempts to distinguish outlier samples to prevent models trained on the in-distribution (ID) dataset from

applicationsarxiv-cs-ai

AdaSD: Adaptive Speculative Decoding for Efficient Language Model Inference

model-releasesarxiv-cs-cl

arXiv:2512.11280v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable performance across a wide range of tasks, but their increasing parameter sizes significantly s

Adversarial Water-Filling: Theory, Algorithms and Foundation Model

arXiv:2605.26163v1 Announce Type: cross Abstract: Competitive resource allocation problems over frequency and space can be formulated as minimax interaction between transmit power and worst-case inter

tutorialsarxiv-cs-lg

AI-T2I: Aggregating-and-Isolating Cross-Attention to Diffusion Models for Text-to-Image Synthesis

arXiv:2605.25763v2 Announce Type: replace Abstract: Text-to-image synthesis has made significant progress, benefiting from the strong generative capabilities of diffusion models. However, these models

safetyarxiv-cs-cv

Amortized Factor Inference Networks for Posterior Inference

arXiv:2605.26419v1 Announce Type: new Abstract: Amortized inference promises fast test-time Bayesian inference, but existing methods are inherently tied to fixed models. Extending amortization to unse

An End-to-End Learning Approach for Solving Capacitated Location-Routing Problems

arXiv:2511.02525v2 Announce Type: replace-cross Abstract: The capacitated location-routing problems (CLRPs) are classical problems in combinatorial optimization, which require simultaneously making lo

An investigation of AI integration in sound designer workflows and experiences

arXiv:2605.27174v1 Announce Type: cross Abstract: Artificial intelligence is increasingly being integrated into professional audio production workflows, yet a gap persists between the tools developers

tutorialsarxiv-cs-ai

Augment Engineering: A Methodology for Multi-Tool AI Orchestration Across Professional Domains

arXiv:2605.26146v1 Announce Type: cross Abstract: Organizations increasingly deploy separate purpose-built AI tools across professional domains, often hiring domain specialists for each, recreating th

applicationsarxiv-cs-ai

Beyond Binary: Turning Partial Success into Dense Verifiable Rewards for Reinforcement Learning in Code Generation

arXiv:2601.03525v3 Announce Type: replace-cross Abstract: Effective reward design is a central challenge in Reinforcement Learning (RL) for code generation. Mainstream test-suite-level outcome rewards

Beyond Questions: Evaluating What Large Language Models (Actually) Know

arXiv:2605.26937v1 Announce Type: cross Abstract: Parametric knowledge in large language models (LLMs) is a cornerstone of their success, yet remains poorly understood. Existing knowledge benchmarks t

Agents

Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems

arXiv:2502.14321v3 Announce Type: replace-cross Abstract: Large language model-based multi-agent systems have recently gained significant attention due to their potential for complex, collaborative, a

agentsarxiv-cs-cl

Beyond the Data Mesh Illusion: Designing Modern AI-augmented Lakehouses to Bridge the Gap Between Theory and Practice

arXiv:2605.27131v1 Announce Type: cross Abstract: Enterprise data platforms face an enduring tension between domain self-service and holistic governance. The data mesh paradigm proposed decentralized

Black-box Membership Inference Attacks on the Pre-training Data of Image-generation Models

arXiv:2605.27020v1 Announce Type: cross Abstract: The rapid advancement of diffusion-based image generation models has raised serious concerns regarding potential copyright and privacy infringements i

Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling

arXiv:2605.27023v1 Announce Type: new Abstract: Knowledge graphs (KGs) have become the core backbone of numerous downstream tasks such as question answering and recommender systems. However, despite a

Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection

arXiv:2605.26193v1 Announce Type: cross Abstract: Time series anomaly detection (TSAD) has long been a hot research topic in data mining due to its various applications. Recent studies challenge the e

Local Ai

CART Random Forests as Sequential Allocation over Random Opportunity Sets: A Stochastic-Control Theory of Ensemble Risk

arXiv:2605.26675v1 Announce Type: cross Abstract: CART random forests are among the most widely used modern predictive methods, with well-documented empirical success. Yet, at the mechanistic level, t

local-aiarxiv-cs-lg

Chain Of Thought Compression: A Theoretical Analysis

arXiv:2601.21576v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) has unlocked advanced reasoning abilities of Large Language Models (LLMs) with intermediate steps, yet incurs prohibitive com

Chaos-SSL: An Attention-Based Self-Supervised Learning Framework with Chaotic Transformation for Medical Image Classification

arXiv:2605.27146v1 Announce Type: new Abstract: Self-Supervised Learning (SSL) has emerged as a powerful paradigm to mitigate the reliance on large, annotated datasets, a common bottleneck in medical

tutorialsarxiv-cs-cv

CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learning

model-releasesarxiv-cs-lg

arXiv:2502.03946v5 Announce Type: replace Abstract: Data preprocessing is often paid little attention in machine learning, despite its potentially significant impact on model performance. While automa

CNNs, Transformers, Hybrid, and Vision Language Models for Skin Cancer Detection

model-releasesarxiv-cs-cv

arXiv:2605.26294v1 Announce Type: new Abstract: Skin cancer is a common and fast rising malignancy worldwide. Early detection is critical for improving outcomes. Deep learning models trained on dermos

Conceptual Schema Inference for Tabular Datasets using Large Language Models

arXiv:2509.04632v2 Announce Type: replace-cross Abstract: Large collections of tabular data from data lakes, web tables and open data portals often originate from heterogeneous sources, leading to rep

applicationsarxiv-cs-ai

Confounder Detection via Treatment Intent: A New Observational Study Design

arXiv:2605.26413v1 Announce Type: cross Abstract: Understanding the effects of interventions is central to scientific progress, with randomized controlled trials (RCTs) regarded as the gold standard f

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