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Reformulating Neural Operators in d+1 Dimensions for Embedding Evolution

arXiv:2505.11766v4 Announce Type: replace-cross Abstract: Neural Operators (NOs) are powerful architectures for learning mappings between function spaces. While most advances focus on refining kernel

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Regret Minimization with Adaptive Opponents in Repeated Games

DGX agent

arXiv:2606.06486v1 Announce Type: cross Abstract: In this paper, we study regret minimization in repeated games with adaptive opponents who can respond based on histories of play. The standard metric

safetyarxiv-cs-ai

6 Jun 2026

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

arXiv:2606.05555v1 Announce Type: cross Abstract: Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge. While recent advances in model-based RL achieve strong

Residual Modeling for High-Fidelity Learned Compression of Scientific Data

arXiv:2606.05389v1 Announce Type: new Abstract: Lossy compression is essential for massive spatiotemporal data from scientific simulations. Learned compressors can achieve high compression ratios at m

Applications

Rethinking Infrastructure Inspection as Image Difference Classification: A Traffic Sign Case Study

arXiv:2606.06375v1 Announce Type: new Abstract: Digital twins (DTs) allow the digitalization of road infrastructure inspection, though this is hindered by limited annotated data. This work exploits th

applicationsarxiv-cs-ai

Retry Policy Gradients in Continuous Action Spaces

arXiv:2606.05888v1 Announce Type: new Abstract: Retry-based objectives such as pass@K and max@K optimize the best return obtained from multiple sampled trajectories, and recent work has shown that the

Reward-Decomposed Reinforcement Learning for Immersive Video Role-Playing

arXiv:2605.04733v2 Announce Type: replace Abstract: Text-based role-playing models can imitate character styles, but often fail to capture scene atmosphere and evolving tension, which are crucial for

Reward Learning through Ranking Mean Squared Error

arXiv:2601.09236v3 Announce Type: replace-cross Abstract: Reward design remains a significant bottleneck in applying reinforcement learning (RL) to real-world problems. A popular alternative is reward

Risk Assessment of Autonomous Driving: Integrating Technical Failures, Ethical Dilemmas, and Policy Frameworks

arXiv:2606.06396v1 Announce Type: new Abstract: Autonomous driving technology has the potential to reduce the large number of road traffic accidents caused by human error each year, but it also brings

RREDCoT: Segment-Level Reward Redistribution for Reasoning Models

arXiv:2606.06475v1 Announce Type: cross Abstract: Recent advancements in reasoning language models have been driven by Reinforcement Learning (RL) fine-tuning. Most often, these rely on the Group Rela

Safety Paradox: How Enhanced Safety Awareness Leaves LLMs Vulnerable to Posterior Attack

arXiv:2606.05614v1 Announce Type: new Abstract: Large language models (LLMs) are rigorously aligned to refuse harmful requests, a process that inherently cultivates a latent capacity to evaluate and r

SAGE: Scalable AI Governance & Evaluation

arXiv:2602.07840v3 Announce Type: replace-cross Abstract: Evaluating relevance in large-scale search systems is fundamentally constrained by the governance gap between nuanced, resource-constrained hu

SagnacAssisted Enhanced OTDR for Distributed Acoustic Sensing: A Standardized Benchmark and Engineering Evaluation Framework

arXiv:2606.05754v1 Announce Type: cross Abstract: Phase-sensitive optical time-domain reflectometry (phi-OTDR) is widely used in large-scale distributed acoustic sensing (DAS) because it provides dist

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime

arXiv:2509.24882v2 Announce Type: replace-cross Abstract: Neural scaling laws underlie many of the recent advances in deep learning, yet their theoretical understanding remains largely confined to lin

SciVisAgentSkills: Design and Evaluation of Agent Skills for Scientific Data Analysis and Visualization

arXiv:2606.05525v1 Announce Type: new Abstract: Recent advances in agentic visualization have enabled the translation of natural language into executable scientific visualization (SciVis) workflows. W

Search-Time Contamination in Deep Research Agents: Measuring Performance Inflation in Public Benchmark Evaluation

arXiv:2606.05241v1 Announce Type: cross Abstract: Public benchmarks enable fair and reproducible evaluation of LLM reasoning, but they become fragile for deep research agents that actively search the

Selective-Advantage Entropy-Adaptive Horizon GRPO: Asymmetric Token-Level Discounting for Efficient Reinforcement Learning of Language Models

arXiv:2606.05434v1 Announce Type: cross Abstract: Group Relative Policy Optimisation (GRPO) has emerged as an effective reinforcement-learning algorithm for aligning language models on reasoning tasks

Self-Commitment Latency: A Reward-Free Probe for Prompted Implicit Hacking

arXiv:2606.05625v1 Announce Type: new Abstract: Implicit reward hacking is hard to audit when a language model's chain of thought appears benign: a final answer may be anchored by a prompt shortcut wh

Semantic Partial Grounding via LLMs

arXiv:2602.22067v2 Announce Type: replace Abstract: Grounding is a critical step in classical planning, yet it often becomes a computational bottleneck due to the exponential growth in grounded action

SentinelBench: A Benchmark for Long-Running Monitoring Agents

arXiv:2606.05342v1 Announce Type: new Abstract: AI agents are increasingly asked to carry out work that spans minutes, hours, or longer. Yet the default model of agent behavior is continuous action: i

Separation Power of Equivariant Neural Networks

arXiv:2406.08966v3 Announce Type: replace-cross Abstract: The separation power of a machine learning model refers to its ability to distinguish between different inputs and is often used as a proxy fo

Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation

arXiv:2606.05510v1 Announce Type: new Abstract: Telehealth systems have become increasingly important for delivering accessible and timely medical information. Existing large language models often str

SlotGCG: Exploiting the Positional Vulnerability in LLMs for Jailbreak Attacks

arXiv:2606.05609v1 Announce Type: cross Abstract: As large language models (LLMs) are widely deployed, identifying their vulnerability through jailbreak attacks becomes increasingly critical. Optimiza

Soft Sequence Policy Optimization

arXiv:2602.19327v3 Announce Type: replace-cross Abstract: A significant portion of recent research on Large Language Model (LLM) alignment focuses on developing new policy optimization methods based o

Stable Deep Reinforcement Learning via Isotropic Gaussian Representations

arXiv:2602.19373v3 Announce Type: replace-cross Abstract: Deep reinforcement learning systems often suffer from unstable training dynamics due to non-stationarity, where learning objectives and data d

Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting

arXiv:2606.06102v1 Announce Type: new Abstract: Ultra-short-term solar irradiance prediction is critical for photovoltaic system dispatch and power grid stability. Existing approaches suffer from thre

Step-by-Step Optimization-like Reasoning in LLMs over Expanding Search Spaces

arXiv:2606.05464v1 Announce Type: new Abstract: Verifiable reward training has improved mathematical and coding reasoning, but these domains capture only part of step-by-step decision making. Many rea

Subspace-Aware Sparse Autoencoders for Effective Mechanistic Interpretability

arXiv:2606.06333v1 Announce Type: cross Abstract: Sparse Autoencoders (SAEs) are widely used for mechanistic interpretability in large language models, yet their formulation assigns each latent featur

Synapse: Federated Tool Routing via Typed Compendium Artifacts

arXiv:2602.00911v2 Announce Type: replace Abstract: The unit of collaboration in federated learning determines what guarantees are even expressible. Flat units like weights, prompts, raw examples, car

Synthetic Contrastive Reasoning for Multi-Table Q&A

arXiv:2606.05382v1 Announce Type: new Abstract: Multi-table question answering requires models to retrieve relevant evidence, link schemas, and perform compositional reasoning across relational tables

TAPO: Tool-Aware Policy Optimization via Credit Transfer for Multimodal Search Agents

arXiv:2606.05784v1 Announce Type: new Abstract: We identify and formally characterize credit misassignment as a systematic failure mode of GRPO in tool-augmented multimodal search agents: its uniform

The End of Software Engineering: How AI Agents Are Fundamentally Restructuring the Software Paradigm

arXiv:2606.05608v1 Announce Type: cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into s

Tutorials

The Role of Instructional Guidance in Generative AI-Assisted Learning: Empirical Evidence from Construction Engineering Education

arXiv:2606.05509v1 Announce Type: cross Abstract: Generative artificial intelligence (AI) is increasingly used to support self-directed learning, yet student interaction with such systems often remain

tutorialsarxiv-cs-ai

The Score Hamiltonian: Mapping Diffusion Models to Adiabatic Transport

arXiv:2606.05217v1 Announce Type: cross Abstract: We exhibit an exact correspondence between sampling with score-based diffusion models and adiabatic transport of ground states for a family of Schrodi

Agents

The Virtual Roundtable: Multi-Agent Personas Simulating the Dynamics of Human Brainstorming

arXiv:2606.05178v1 Announce Type: cross Abstract: As AI-driven product development accelerates, the bottleneck is shifting from how we build to what we build. Traditional human brainstorming faces cha

agentsarxiv-cs-ai

Agents

TinyML-Driven Cybersecurity for Autonomous Spacecraft: Latency-Accuracy Analysis for SPARTA RF and Cyber Threat Detection

arXiv:2606.05779v1 Announce Type: cross Abstract: Autonomous spacecraft require rapid, lightweight, and reliable onboard detection of cyber-RF threats. Using the SPARTA attack model, we analyze the la

agentsarxiv-cs-ai

TLA-Prover: Verifiable TLA+ Specification Synthesis via Preference-Optimized Low-Rank Adaptation

arXiv:2606.06133v1 Announce Type: cross Abstract: TLA+ is a formal specification language for verifying distributed systems and safety-critical protocols. Large language models (LLMs) frequently produ

TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management

arXiv:2606.06337v1 Announce Type: new Abstract: Large language model (LLM) deployments for long-horizon tasks face a fundamental constraint: context windows are finite while productive work sessions a

Local Ai

TOKI: A Bitemporal Operator Algebra for Contradiction Resolution in LLM-Agent Persistent Memory

arXiv:2606.06240v1 Announce Type: cross Abstract: Persistent memory for an LLM agent is a write-heavy substrate: every belief update is a versioned write, and a new claim may contradict a stored one.

local-aiarxiv-cs-ai

ToolChoiceConfusion: Causal Minimal Tool Filtering for Reliable LLM Agents

arXiv:2606.06284v1 Announce Type: new Abstract: Large language model agents increasingly rely on external tools, but larger tool menus can reduce reliability and efficiency by increasing wrong-tool ca

Towards AI epidemiology: a measurement standardisation framework for prospective risk detection

arXiv:2512.15783v3 Announce Type: replace Abstract: This paper proposes a measurement standardisation framework that compresses expert-AI interactions into structured, comparable fields for prospectiv

Towards Healthy Evolution: Exploring the Role and Mechanisms of Human-Agent Interaction in Self-Evolving Systems

arXiv:2606.06114v1 Announce Type: new Abstract: Self-evolving agents improve through continual self-play and self-generated learning signals, but autonomous evolution can also cause capability degrada

Towards the Readability of LLM-Generated Codes through Multitask Representation Engineering

arXiv:2606.06214v1 Announce Type: cross Abstract: Correctness and readability are key measures of code quality, respectively ensuring functional fidelity and ease of comprehension. While most existing

Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models

arXiv:2606.05481v1 Announce Type: cross Abstract: Data-driven Prognostics and Health Management (PHM) uses time-varying condition-monitoring data to diagnose system states and estimate remaining usefu

Towards World Models in Biomedical Research

arXiv:2606.05925v1 Announce Type: new Abstract: A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbatio

Tutorials

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models

arXiv:2606.06285v1 Announce Type: new Abstract: Time series foundation models (TS-FMs) aim to learn generalizable temporal representations that can be adapted to a wide range of downstream tasks. In r

tutorialsarxiv-cs-ai

Trust, but Don't Verify: Epistemic Blind Spots in LLM Source Evaluation

arXiv:2606.05403v1 Announce Type: cross Abstract: Language models increasingly act as epistemic proxies, synthesizing evidence from multiple sources to inform decisions. Whether they evaluate the qual

Applications

Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory

arXiv:2606.05334v1 Announce Type: new Abstract: Returned products in circular factories re-enter production with heterogeneous degradation states, usage histories, and remaining capability. Reuse cann

applicationsarxiv-cs-ai