1. Learning for routing: A guided review of recent developments and future directions

   http://arxiv.org/abs/2507.00218v1 · arxiv

   This paper reviews the current progress in applying machine learning (ML) tools to solve NP-hard combinatorial optimization problems, with a focus on routing problems such as the traveling salesman problem (TSP) and the vehicle routing problem (VRP). Due to the inherent complexity of these problems,

2. Ultracold atomic lattice systems for simulating topological phases: A review

   http://arxiv.org/abs/2606.16598v2 · arxiv

   Owing to rapid recent progress, ultracold atomic lattice systems for simulating topological phases are now at a pivotal stage, evolving from established paradigms into increasingly versatile and programmable quantum simulators. In this review, we survey recent experimental advances across four major

3. AutoRestTest at the SBFT 2026 Tool Competition

   http://arxiv.org/abs/2607.01063v1 · arxiv

   Large input spaces and complex inter-operation dependencies make black-box REST API testing challenging. AutoRestTest combines a Semantic Property Dependency Graph, multi-agent reinforcement learning, and large language models to intelligently explore large API input spaces. In the SBFT 2026 REST Le

4. Monte Carlo Tree Search: A Review of Recent Modifications and Applications

   http://arxiv.org/abs/2103.04931v4 · arxiv

   Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of act

5. Proceedings of HLPP 2026: 19th International Symposium on High-Level Parallel Programming and Applications

   http://arxiv.org/abs/2607.12917v1 · arxiv

   This volume contains the ten peer-reviewed papers presented at HLPP 2026, the 19th International Symposium on High-Level Parallel Programming and Applications, held on 9-10 July 2026 at the Institut Henri Poincare in Paris, France. The symposium covers high-level approaches to parallel programming:

6. The Quantum Revolution in Philosophy (Book Review)

   http://arxiv.org/abs/2110.12102v1 · arxiv

   In this thought-provoking book, Richard Healey proposes a new interpretation of quantum theory inspired by pragmatist philosophy. Healey puts forward the interpretation as an alternative to realist quantum theories on the one hand such as Bohmian mechanics, spontaneous collapse theories, and many-wo

7. Second MOASEI Competition at AAMAS'2026: A Technical Report

   http://arxiv.org/abs/2607.03399v1 · arxiv

   We describe the 2026 Methods for Open Agent Systems Evaluation Initiative (MOASEI) Competition, a benchmark event for evaluating multi-agent decision-making under open-system conditions. Building on the inaugural 2025 competition, the 2026 edition retained wildfire fighting, cybersecurity, and ride-

8. Review of Entangled Coherent States

   http://arxiv.org/abs/1112.1778v1 · arxiv

   We review entangled coherent state research since its first implicit use in 1967 to the present. Entangled coherent states are important to quantum superselection principles, quantum information processing, quantum optics, and mathematical physics. Despite their inherent fragility they have produced

9. Review Arcade: On the Human Alignment and Gameability of LLM Reviews

   http://arxiv.org/abs/2605.28897v1 · arxiv

   LLM-generated reviews for scientific papers are gaining considerable traction and are even being officially piloted by major conferences. We have to assume that not only reviewers are using LLM-assistance, but also that authors use LLMs to revise their papers before submitting. In this work, we perf

10. When AI Reviews Train AI Reviewers: Scientific-Judgment Collapse and Mitigation

    http://arxiv.org/abs/2609.20942v1 · arxiv

    Large language models (LLMs) increasingly participate in scientific evaluation, both as automated reviewers and as assistants to human reviewers. As model-generated reviews enter public data and future training corpora, AI peer review can become recursive: later reviewers learn from judgments produc

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