arXiv AI
Jul 22
Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States
arXiv:2607. 18589v1 Announce Type: cross Abstract: Reinforcement learning is conventionally divided into model-based and model-free methods.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jul 22
Graph-Based Agentic AI with LangGraph: Workflow Pathways for Long-Running Stateful Business Processes
arXiv:2607. 19297v1 Announce Type: new Abstract: This paper is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in business processes.
arXiv AI
Jul 22
MAGE: Human-Like Macro Placement via Agentic Multimodal Reasoning
arXiv:2607. 18536v1 Announce Type: new Abstract: Macro placement still requires substantial manual refinement in industrial physical design flows.
arXiv AI
Jul 22
SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring
arXiv:2607. 18665v1 Announce Type: new Abstract: Large language models (LLMs) increasingly support science, but they can also convert hazardous scientific knowledge into actionable misuse guidance.
arXiv AI
Jul 22
Supra Cognitive Modes: A Routed Architecture for Agent Memory
arXiv:2607. 19096v1 Announce Type: new Abstract: Agent-memory workloads mix direct factual lookup, relation-chain and current-state reasoning, and broad synthesis over long histories.
arXiv AI
Jul 22
ABot-World-0: Infinite Interactive World Rollout on a Single Desktop GPU
arXiv:2607. 19191v1 Announce Type: cross Abstract: We present ABot-World-0, an action-conditioned video world model for real-time, long-horizon closed-loop interaction, supported by a multi-source data infrastructure spanning AAA games, simulation engines, and internet videos to learn controllable world dynamics.
arXiv AI
Jul 22
Bridging the Last Mile of Circuit Design: PostEDA-Bench, a Hierarchical Benchmark for PPA Convergence and DRC Fixing
arXiv:2605. 06936v3 Announce Type: replace-cross Abstract: LLM-based agents are increasingly applied to the "last mile" of Electronic Design Automation (EDA): repairing residual sign-off Design Rule Check (DRC) violations and converging Power-Performance-Area (PPA) targets after tool runs.
arXiv AI
Jul 22
Toward Learning POMDPs Beyond Full-Rank Actions and State Observability
arXiv:2601. 18930v4 Announce Type: replace-cross Abstract: We are interested in enabling autonomous agents to learn and reason about systems with hidden states, such as locking mechanisms.
arXiv AI
Jul 22
Semantic Cooperative Games for Contribution Attribution in LLM-Based Multi-Agent Systems
arXiv:2607. 18255v1 Announce Type: new Abstract: Contribution attribution has become a central problem in LLM-based multi-agent systems, where final outputs are produced through multiple agents, message exchanges, and ordered workflow dependencies.
arXiv Machine Learning
Jul 22
Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures
arXiv:2607. 18359v1 Announce Type: cross Abstract: Critical infrastructures are increasingly distributed, interdependent, and exposed to evolving disruptions, making resilience a central requirement for their operation and control.
arXiv AI
Jul 22
Do AI-Native Biotechs Need Departments? Benchmarking Company World Models for AI-Driven Drug Development
arXiv:2607. 18696v1 Announce Type: new Abstract: AI-native biotechnology companies are often designed by copying human biotech org charts into agent roles.
arXiv AI
Jul 22
SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategy Refinement in E-Commerce Recommendation
arXiv:2607. 17719v2 Announce Type: replace Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS).
arXiv Machine Learning
Jul 22
A Reinforcement-Learning-Augmented Liquid-Fueled Reactor Network Model for Predicting Lean Blowout in Gas Turbine Combustors
arXiv:2607. 19281v1 Announce Type: new Abstract: This study introduces a reinforcement learning (RL) framework for generating optimal liquid-fueled reactors to improve lean blowout (LBO) predictions in gas turbine combustors.
arXiv Machine Learning
Jul 22
End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation
arXiv:2607. 18637v1 Announce Type: cross Abstract: Generating closed-loop traffic scenarios that are both realistic and controllable is crucial for evaluating autonomous driving systems, especially under rare safety-critical interactions.
arXiv AI
Jul 22
Computing on the Fly: Navigating a Vision for the Future of Drone Computing
arXiv:2607. 19213v1 Announce Type: cross Abstract: The report envisions a decade in which drones move goods, medical supplies, and information at a scale comparable to national infrastructure investments like highways and the electric grid.
arXiv AI
Jul 22
ResearchArena: Evaluating Sabotage and Monitoring in Automated AI R&D
arXiv:2607. 19321v1 Announce Type: new Abstract: As AI agents begin to automate AI R&D, we need ways to assess whether their outputs are safe to deploy, even when the agents themselves may be untrusted.
arXiv AI
Jul 22
Decode-Time Grammars: Constrained LLM Generation over a Refinement Order of Grammar Fragments
arXiv:2607. 18357v1 Announce Type: cross Abstract: Large language models now write a growing share of the world's code, increasingly inside agents and serving systems that compile, execute, or dispatch generated code without line-by-line review.
arXiv AI
Jul 22
Data Leakage Prevention in Agentic Applications via Preemptive Hardening
arXiv:2607. 18847v1 Announce Type: cross Abstract: Agentic systems integrate LLM driven planning with interfaces to external tools, making data leakage and tool misuse feasible via instruction/data boundary failures and prompt injection attacks.
arXiv AI
Jul 22
SENTINEL: A Multi-Level Formal Framework for Safety Evaluation of Foundation Model-based Embodied Agents
arXiv:2510. 12985v3 Announce Type: replace Abstract: We present SENTINEL, a framework for formally evaluating the physical safety of foundation model (FM)-based embodied agents.
arXiv AI
Jul 22
NaviAIS: A Scenario-Level Vessel Trajectory Prediction Dataset withVectorized Lane Priors and the NaviLane Forecasting Framework
arXiv:2607. 18887v1 Announce Type: new Abstract: Vessel trajectory prediction in complex maritime environments is essential for traffic management, collision warning, route planning, and autonomous navigation.