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arXiv:2605.27523v1 Announce Type: cross Abstract: Deep generative models offer powerful tools for multivariate data analysis, but their black-box architectures are often unidentified and difficult to

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Mag-VLA: Vision-Language-Action Model for Bimanual Magnetically Actuated Microrobot Manipulation

DGX agent

arXiv:2605.28486v1 Announce Type: new Abstract: Magnetically actuated microrobots have been used as wireless, non-contact manipulation tools at microscales, making them promising for minimally invasiv

safetyarxiv-cs-ro

28 May 2026

MRMMIA: Membership Inference Attacks on Memory in Chat Agents

arXiv:2605.27825v1 Announce Type: cross Abstract: Membership inference attacks (MIAs) test whether a target data record belongs to a system's private data, and have become a standard tool to measure p

agentsarxiv-cs-lg

Off-Policy Learning to Reason Works Because It Is More Pessimistic Than You Think

arXiv:2605.28150v1 Announce Type: new Abstract: Large scale reinforcement learning has become a central tool for improving reasoning in large language models. At this scale, generation is often lagged

safetyarxiv-cs-lg

Applications

Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization

arXiv:2605.28164v1 Announce Type: cross Abstract: Evolutionary computation offers a variety of tools to solve complex real-world optimization problems. However, research often focuses on smaller, simp

applicationsarxiv-cs-ai

ProvMind: Provenance-grounded reasoning for materials synthesis

arXiv:2605.28487v1 Announce Type: new Abstract: Materials process optimization requires reasoning over routes, conditions, tools and causal dependencies, yet most computational formulations flatten sy

Semantic Optimal Transport for Sparse Autoencoder Feature Matching and Circuit Compression

arXiv:2605.28567v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have become a central tool for interpreting language models. However, two key SAE analyses that remain difficult to scale a

researcharxiv-cs-ai

STR Robot: Design of an Autonomous Mobile Robot from Simulation to Reality

model-releasesarxiv-cs-ro

arXiv:2605.28110v1 Announce Type: new Abstract: With the rapid development of simulation tools, the development and validation of autonomous robotic systems have become more efficient before real-worl

Local Ai

The Energy Blind Spot: NVIDIA's Flagship Edge AI Hardware Cannot Support Process-Level Energy Attribution

arXiv:2605.27599v1 Announce Type: cross Abstract: Agentic AI workloads - where a single user goal triggers multi-step orchestration, tool calls, retries, and failure recovery - are being targeted for

local-aiarxiv-cs-ai

TRACES: Proactive Safety Auditing for Multi-Turn LLM Agents via Trajectory-State Modeling

arXiv:2605.27690v1 Announce Type: new Abstract: LLM agents increasingly operate through multi-turn tool use and environment interaction, where safety risks often emerge from intermediate steps long be

safetyarxiv-cs-cl

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,

FinHarness: An Inline Lifecycle Safety Harness for Finance LLM Agents

arXiv:2605.27333v1 Announce Type: new Abstract: Finance LLM agents must simultaneously block prompt-induced unauthorized actions and approve legitimate multi-step business workflows. However, boundary

safetyarxiv-cs-cl

Generating Robust Portfolios of Optimization Models using Large Language Models

arXiv:2605.27013v1 Announce Type: new Abstract: Mathematical optimization is a powerful tool for structured decision-making across domains such as resource allocation and planning. Formulating optimiz

researcharxiv-cs-ai

'Give Me BF16 or Give Me Death'? Accuracy-Performance Trade-Offs in LLM Quantization

arXiv:2411.02355v4 Announce Type: replace-cross Abstract: Quantization is a powerful tool for accelerating large language model (LLM) inference, but the accuracy-performance trade-offs across differen

Knowledge Graphs as the Missing Data Layer for LLM-Based Industrial Asset Operations

arXiv:2605.26874v1 Announce Type: cross Abstract: LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores. AssetOpsBench (KDD 2026) establishes

LLMs versus the Halting Problem: Characterizing Program Termination Reasoning

arXiv:2601.18987v5 Announce Type: replace-cross Abstract: Determining whether a program terminates is a central problem in computer science. Turing's Halting Problem established termination as undecid

Modeling Agentic Technical Debt and Stochastic Tax: A Standalone Framework for Measurement, Simulation, and Dashboarding

arXiv:2605.27320v1 Announce Type: new Abstract: Agentic AI systems combine probabilistic reasoning with delegated action through tools, context, memory, orchestration, and external workflow integratio

PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations

arXiv:2505.02974v3 Announce Type: replace Abstract: Machine learning-based surrogate models have emerged as a powerful tool to accelerate simulation-driven scientific workflows, but their adoption is

researcharxiv-cs-lg

Probabilistic Smoothing with Ratio-Monotone Transforms for Global Optimization

arXiv:2605.27316v1 Announce Type: new Abstract: Probabilistic smoothing is a standard tool for global optimization, but existing methods rely on Gaussian kernels and specific transforms, often resulti

researcharxiv-cs-lg

Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline

arXiv:2605.26132v1 Announce Type: new Abstract: Can post-trained large language models (LLMs) further improve themselves using only unlabeled prompts, without external teachers or feedback from tools?

The Strongest Teacher Is Not Always the Best Teacher: Student-Centric Answer Selection

arXiv:2605.26872v1 Announce Type: cross Abstract: LLM training increasingly relies on teacher-generated supervision, from synthetic responses to reasoning traces and tool-use demonstrations. Current p

Towards Controllable Image Generation through Representation-Conditioned Diffusion Models

arXiv:2605.27343v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for high-quality image generation and editing, but guiding these models to produce specific outputs rema

researcharxiv-cs-cv

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

XGrammar-2: Efficient Dynamic Structured Generation Engine for Agentic LLMs

arXiv:2601.04426v3 Announce Type: replace Abstract: Modern LLM agents increasingly rely on dynamic structured generation, such as tool calling and response protocols. Unlike traditional structured gen

Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks

arXiv:2505.24876v2 Announce Type: replace-cross Abstract: Deep reasoning is fundamental for solving complex tasks, especially in vision-centric scenarios that demand sequential, multimodal understandi

AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning

arXiv:2605.24486v1 Announce Type: new Abstract: Recent progress on long-horizon agentic tasks has been driven largely by scaling up individual agents through stronger models, better tools, and more ef

AION: Next-Generation Tasks and Practical Harness for Time Series

arXiv:2605.25045v1 Announce Type: new Abstract: Time series research is moving beyond fixed forecasting benchmarks toward realistic tasks that combine prediction, contextual reasoning, tool use, and s

Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agents

arXiv:2605.25971v1 Announce Type: new Abstract: While AI agents demonstrate remarkable capabilities in reasoning and tool use, they remain fundamentally reactive: they compute responses only after exp

Beyond Final Answers: Auditing Trajectory-Level Hallucinations in Multi-Agent Industrial Workflows

arXiv:2605.24219v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents that reason, use tools, and act over multiple steps. Yet most hallucination

CausalFlow: Causal Attribution and Counterfactual Repair for LLM Agent Failures

arXiv:2605.25338v1 Announce Type: cross Abstract: Large language model (LLM) agents frequently fail on multi-step tasks involving reasoning, tool use, and environment interaction. While such failures

Code2UML: Agentic LLMs with context engineering for scalable software visualization

arXiv:2605.24453v1 Announce Type: cross Abstract: Large Language Model (LLM)-based code analysis tools are adopted to automate software documentation tasks. However, the scalability of these approache

Contractual Skills: A GovernSpec Design Framework for Enterprise AI Agents

arXiv:2605.22634v2 Announce Type: replace-cross Abstract: Skills have become a practical packaging mechanism for agent instructions, workflows, scripts, and reference materials. In enterprise settings

safetyarxiv-cs-ai

CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents

arXiv:2605.25624v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its exte

IterInject: Indirect Prompt Injection Against LLM Agents via Feedback-Guided Iterative Optimization

model-releasesarxiv-cs-lg

arXiv:2605.24659v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed for complex tasks requiring planning, tool use, and interaction with external services. Their reliance on unt

Applications

Message-Passing GNNs Fail to Approximate Sparse Triangular Factorizations

arXiv:2502.01397v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have been proposed as a tool for learning sparse matrix preconditioners, which are key components in accelerating

applicationsarxiv-cs-ai

Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model

arXiv:2605.23934v1 Announce Type: new Abstract: Quantum computing devices are recognized as powerful tools for solving NP-complete problems. However, the intricacy of their modeling presents notable b

SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent

arXiv:2605.24468v1 Announce Type: new Abstract: Long-horizon agentic reasoning requires large language models to act over long interaction histories containing thoughts, tool calls, observations, and

SEAL: Synergistic Co-Evolution of Agents and Learning Environments

arXiv:2605.24426v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly improved through interaction, yet most self-evolution methods adapt either the policy or the learning

safetyarxiv-cs-cl

STORM: Internalized Modeling for Spatial-Temporal Reasoning in Video-Language Models

arXiv:2605.26014v1 Announce Type: cross Abstract: Many video reasoning tasks require tracking motion, temporal order, and evolving visual states across frames. Existing methods built on large vision-l

agentsarxiv-cs-cl

SURGE: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors

arXiv:2502.11167v5 Announce Type: replace-cross Abstract: Neural surrogate models are powerful and efficient tools in data mining. Meanwhile, large language models (LLMs) have demonstrated remarkable

Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security

arXiv:2605.23989v1 Announce Type: new Abstract: Agentic AI systems -- Large Language Models (LLMs) augmented with planning, tool use, memory, and long-horizon interactions -- can execute complex tasks

safetyarxiv-cs-ai

VineLM: Trie-Based Fine-Grained Control for Agentic Workflows

arXiv:2605.23914v1 Announce Type: cross Abstract: Agentic workflows interleave configurable LLM stages with tool stages and often include retries or refinement loops. Existing workflow managers profil

Archimedean Copula Inference via Taylor-Mode AD

model-releasesarxiv-cs-lg

arXiv:2605.23134v1 Announce Type: new Abstract: No existing nested Archimedean copula tool handles all three of (a) arbitrary per-variable (right-)censoring in survival analysis, (b) arbitrary nesting

ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport

arXiv:2602.07235v2 Announce Type: replace-cross Abstract: Watermarking is an important tool for promoting the responsible use of large language models (LLMs). Existing watermarks insert a signal into

researcharxiv-cs-ai

BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems

arXiv:2605.22866v1 Announce Type: new Abstract: Compound AI systems route tasks through hierarchies of specialised components. Attribution is dominated by Shapley-based methods (SHAP), which decompose

Local Ai

CultivAgents: Cultivating Relationship-Centered Multi-Agent Systems for Personalized Gardening

arXiv:2605.23193v1 Announce Type: cross Abstract: Gardening is critical to support well-being, cultural continuity, and food autonomy, yet existing digital tools often provide generic advice that over

local-aiarxiv-cs-cl

DepthAgent: Towards Better Universal Depth Estimation via Sample-wise Expert Selection

model-releasesarxiv-cs-cv

arXiv:2605.23281v1 Announce Type: new Abstract: Monocular metric depth estimation has achieved strong progress with large-scale training and universal-camera modeling, yet robust deployment across div

Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems

arXiv:2605.22883v1 Announce Type: new Abstract: Current AI energy benchmarks measure consumption at the granularity of a single model invocation or training run. For classical single-turn workloads th

safetyarxiv-cs-ai