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The Amazing Stability of Flow Matching

arXiv:2604.16079v1 Announce Type: new Abstract: The success of deep generative models in generating high-quality and diverse samples is often attributed to particular architectures and large training

Applications

The Crutch or the Ceiling? How Different Generations of LLMs Shape EFL Student Writings

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Human DGX agent

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DGX agent

arXiv:2604.15460v1 Announce Type: cross Abstract: The rapid evolution of Large Language Models (LLMs) has made them powerful tools for enhancing student writing. This study explores the extent and lim

applicationsarxiv-cs-ai

20 Apr 2026

The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback

arXiv:2604.16087v1 Announce Type: new Abstract: We study the problem of learning in zero-sum matrix games with repeated play and bandit feedback. Specifically, we focus on developing uncoupled algorit

The Illusion of Equivalence: Systematic FP16 Divergence in KV-Cached Autoregressive Inference

arXiv:2604.15409v1 Announce Type: cross Abstract: KV caching is a ubiquitous optimization in autoregressive transformer inference, long presumed to be numerically equivalent to cache-free computation.

The Informational Cost of Agency: A Bounded Measure of Interaction Efficiency for Deployed Reinforcement Learning

arXiv:2603.01283v2 Announce Type: replace Abstract: Deployed RL agents operate in closed-loop systems where reliable performance depends on maintaining coherent coupling between observations, actions,

safetyarxiv-cs-ai

The Machine Learning Approach to Moment Closure Relations for Plasma: A Review

arXiv:2511.22486v2 Announce Type: replace-cross Abstract: The requirement for large-scale global simulations of plasma is an ongoing challenge in both space and laboratory plasma physics. Any simulati

researcharxiv-cs-lg

The Metacognitive Monitoring Battery: A Cross-Domain Benchmark for LLM Self-Monitoring

model-releasesarxiv-cs-cl

arXiv:2604.15702v1 Announce Type: new Abstract: We introduce a cross-domain behavioural assay of monitoring-control coupling in LLMs, grounded in the Nelson and Narens (1990) metacognitive framework a

The Price of Paranoia: Robust Risk-Sensitive Cooperation in Non-Stationary Multi-Agent Reinforcement Learning

arXiv:2604.15695v1 Announce Type: cross Abstract: Cooperative equilibria are fragile. When agents learn alongside each other rather than in a fixed environment, the process of learning destabilizes th

safetyarxiv-cs-ai

The Reasoning Trap: How Enhancing LLM Reasoning Amplifies Tool Hallucination

arXiv:2510.22977v2 Announce Type: replace-cross Abstract: Enhancing the reasoning capabilities of Large Language Models (LLMs) is a key strategy for building Agents that 'think then act.' However, rec

The Relic Condition: When Published Scholarship Becomes Material for Its Own Replacement

arXiv:2604.16116v1 Announce Type: cross Abstract: We extracted the scholarly reasoning systems of two internationally prominent humanities and social science scholars from their published corpora alon

Agents

The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE

arXiv:2604.15468v1 Announce Type: cross Abstract: AI-based systems, currently driven largely by LLMs and tool-using agentic harnesses, are increasingly discussed as a possible threat to software engin

agentsarxiv-cs-ai

The Spectral Geometry of Thought: Phase Transitions, Instruction Reversal, Token-Level Dynamics, and Perfect Correctness Prediction in How Transformers Reason

arXiv:2604.15350v1 Announce Type: new Abstract: We discover that large language models exhibit spectral phase transitions in their hidden activation spaces when engaging in reasoning versus factual re

The Synthetic Media Shift: Tracking the Rise, Virality, and Detectability of AI-Generated Multimodal Misinformation

arXiv:2604.15372v1 Announce Type: cross Abstract: As generative AI advances, the distinction between authentic and synthetic media is increasingly blurred, challenging the integrity of online informat

Hardware

The threat of analytic flexibility in using large language models to simulate human data

arXiv:2509.13397v3 Announce Type: replace-cross Abstract: Social scientists are now using large language models to create 'silicon samples': synthetic datasets intended to stand in for human responden

hardwarearxiv-cs-ai

Agents

The World Leaks the Future: Harness Evolution for Future Prediction Agents

arXiv:2604.15719v1 Announce Type: new Abstract: Many consequential decisions must be made before the relevant outcome is known. Such problems are commonly framed as future prediction, where an LLM age

agentsarxiv-cs-ai

Think Multilingual, Not Harder: A Data-Efficient Framework for Teaching Reasoning Models to Code-Switch

arXiv:2604.15490v1 Announce Type: new Abstract: Recent developments in reasoning capabilities have enabled large language models to solve increasingly complex mathematical, symbolic, and logical tasks

researcharxiv-cs-cl

Time-optimal Convexified Reeds-Shepp Paths on a Sphere

arXiv:2504.00966v2 Announce Type: replace Abstract: This article studies the time-optimal path planning problem for a convexified Reeds-Shepp (CRS) vehicle on a unit sphere, capable of both forward an

researcharxiv-cs-ro

To LLM, or Not to LLM: How Designers and Developers Navigate LLMs as Tools or Teammates

arXiv:2604.15344v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into design and development workflows, yet decisions about their use are rarely binary or pur

Tutorials

TopFeaRe: Locating Critical State of Adversarial Resilience for Graphs Regarding Topology-Feature Entanglement

arXiv:2604.15370v1 Announce Type: cross Abstract: Graph adversarial attacks are usually produced from the two perspectives of topology/structure and node feature, both of them represent the paramount

tutorialsarxiv-cs-lg

Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Dataset

arXiv:2604.15964v1 Announce Type: cross Abstract: Accurate automatic brain tumor segmentation in Low and Middle-Income (LMIC) countries is challenging due to the lack of defined national imaging proto

Towards In-Context Tone Style Transfer with A Large-Scale Triplet Dataset

arXiv:2604.16114v1 Announce Type: new Abstract: Tone style transfer for photo retouching aims to adapt the stylistic tone of the reference image to a given content image. However, the lack of high-qua

Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures

arXiv:2604.16042v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have achieved strong performance across many NLP tasks, their opaque internal mechanisms hinder trustworthiness and

safetyarxiv-cs-ai

Towards Realistic Open-Vocabulary Remote Sensing Segmentation: Benchmark and Baseline

model-releasesarxiv-cs-cv

arXiv:2604.15652v1 Announce Type: new Abstract: Open-vocabulary remote sensing image segmentation (OVRSIS) remains underexplored due to fragmented datasets, limited training diversity, and the lack of

Towards Rigorous Explainability by Feature Attribution

arXiv:2604.15898v1 Announce Type: new Abstract: For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models. Unfortunately, such metho

Towards Robust Endogenous Reasoning: Unifying Drift Adaptation in Non-Stationary Tuning

arXiv:2604.15705v1 Announce Type: new Abstract: Reinforcement Fine-Tuning (RFT) has established itself as a critical paradigm for the alignment of Multi-modal Large Language Models (MLLMs) with comple

Hardware

Towards Understanding, Analyzing, and Optimizing Agentic AI Execution: A CPU-Centric Perspective

arXiv:2511.00739v3 Announce Type: replace Abstract: Agentic AI serving converts monolithic LLM-based inference to autonomous problem-solvers that can plan, call tools, perform reasoning, and adapt on

hardwarearxiv-cs-ai

Towards Universal Convergence of Backward Error in Linear System Solvers

arXiv:2604.16075v1 Announce Type: cross Abstract: The quest for an algorithm that solves an nimes n linear system in O(n^2) time complexity, or O(n^2 ext{poly}(1/epsilon)) when solving up to epsilon r

TPA: Next Token Probability Attribution for Detecting Hallucinations in RAG

arXiv:2512.07515v4 Announce Type: replace-cross Abstract: Detecting hallucinations in Retrieval-Augmented Generation remains a challenge. Prior approaches attribute hallucinations to a binary conflict

Training Flow Matching: The Role of Weighting and Parameterization

arXiv:2603.06454v2 Announce Type: replace Abstract: We study the training objectives of denoising-based generative models, with a particular focus on loss weighting and output parameterization, includ

Training Time Prediction for Mixed Precision-based Distributed Training

arXiv:2604.16145v1 Announce Type: cross Abstract: Accurate prediction of training time in distributed deep learning is crucial for resource allocation, cost estimation, and job scheduling. We observe

Trajectory Planning for Safe Dual Control with Active Exploration

arXiv:2604.15507v1 Announce Type: new Abstract: Planning safe trajectories under model uncertainty is a fundamental challenge. Robust planning ensures safety by considering worst-case realizations, ye

safetyarxiv-cs-ro

Transfer Learning from Foundational Optimization Embeddings to Unsupervised SAT Representations

arXiv:2604.15448v1 Announce Type: cross Abstract: Foundational optimization embeddings have recently emerged as powerful pre-trained representations for mixed-integer programming (MIP) problems. These

Transformer Neural Processes - Kernel Regression

arXiv:2411.12502v4 Announce Type: replace-cross Abstract: Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic p

Applications

TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings

arXiv:2508.16860v2 Announce Type: replace-cross Abstract: Pretrained Language Models or PLMs are transformer-based architectures that can be used in bug triaging tasks. PLMs can better capture token s

applicationsarxiv-cs-ai

TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data Synthesis

model-releasesarxiv-cs-cl

arXiv:2505.24672v2 Announce Type: replace Abstract: Large Language Models (LLMs) excel in various natural language processing tasks but remain vulnerable to generating harmful content or being exploit

Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions

arXiv:2510.04237v5 Announce Type: replace Abstract: In this paper, we propose a novel kernel stochastic gradient descent (SGD) algorithm for large-scale supervised learning with general losses. Compar

TTL: Test-time Textual Learning for OOD Detection with Pretrained Vision-Language Models

arXiv:2604.15756v1 Announce Type: new Abstract: Vision-language models (VLMs) such as CLIP exhibit strong Out-of-distribution (OOD) detection capabilities by aligning visual and textual representation

researcharxiv-cs-cl

TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation

arXiv:2604.15950v1 Announce Type: new Abstract: Pancreatic ductal adenocarcinoma (PDAC) segmentation on contrast-enhanced CT is inherently ambiguous: inter-rater disagreement among experts reflects ge

Two-Dimensional Deep ReLU CNN Approximation for Korobov Functions: A Constructive Approach

arXiv:2503.07976v2 Announce Type: replace-cross Abstract: This paper investigates approximation capabilities of two-dimensional (2D) deep convolutional neural networks (CNNs), with Korobov functions s

Local Ai

Two-Stage Framework for Efficient UAV-Based Wildfire Video Analysis with Adaptive Compression and Fire Source Detection

arXiv:2508.16739v2 Announce Type: replace Abstract: Unmanned Aerial Vehicles (UAVs) have become increasingly important in disaster emergency response by facilitating aerial video analysis. Due to the

local-aiarxiv-cs-cv

TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models

model-releasesarxiv-cs-cv

arXiv:2604.15967v1 Announce Type: cross Abstract: Despite the remarkable synthesis capabilities of text-to-image (T2I) models, safeguarding them against content violations remains a persistent challen

UA-Net: Uncertainty-Aware Network for TRISO Image Semantic Segmentation

arXiv:2604.15542v1 Announce Type: new Abstract: Tristructural isotropic (TRISO)-coated particle fuels undergo dimensional changes and chemical reactions during high-temperature neutron irradiation. Po

Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters

arXiv:2604.15339v1 Announce Type: cross Abstract: Uncertainty, vagueness, and ambiguity are closely related and often confused concepts in human-robot interaction (HRI). In earlier studies, these conc

Understanding New-Knowledge-Induced Factual Hallucinations in LLMs: Analysis and Interpretation

arXiv:2511.02626v3 Announce Type: replace Abstract: Prior works have shown that fine-tuning on new knowledge can induce factual hallucinations in large language models (LLMs), leading to incorrect out

researcharxiv-cs-cl

UniEditBench: A Unified and Cost-Effective Benchmark for Image and Video Editing via Distilled MLLMs

arXiv:2604.15871v1 Announce Type: cross Abstract: The evaluation of visual editing models remains fragmented across methods and modalities. Existing benchmarks are often tailored to specific paradigms

Univariate Channel Fusion for Multivariate Time Series Classification

arXiv:2604.16119v1 Announce Type: new Abstract: Multivariate time series classification (MTSC) plays a crucial role in various domains, including biomedical signal analysis and motion monitoring. Howe

researcharxiv-cs-lg

Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy

arXiv:2603.05719v2 Announce Type: replace Abstract: Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applicatio

Applications

Unveiling Stochasticity: Universal Multi-modal Probabilistic Modeling for Traffic Forecasting

arXiv:2604.16084v1 Announce Type: cross Abstract: Traffic forecasting is a challenging spatio-temporal modeling task and a critical component of urban transportation management. Current studies mainly

applicationsarxiv-cs-ai