arXiv AI

Adversarial Instance Generation and Robust Training for Neural Combinatorial Optimization with Multiple Objectives

arXiv:2601. 01665v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has shown great promise in addressing multi-objective combinatorial optimization problems (MOCOPs).

arXiv AI

Jun 8

Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach

arXiv:2510. 09041v3 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has demonstrated remarkable success in developing autonomous driving policies.

By Junchao Fan, Qi Wei, Ruichen Zhang, Yang Lu, Jianhua Wang, Xiaolin Chang, Bo Ai

agents reinforcement-learning robotics benchmarks safety

arXiv AI

Jul 14

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning

arXiv:2607. 10630v1 Announce Type: cross Abstract: Robust motion planning in dense traffic requires autonomous vehicles to interact in rare and safety-critical scenarios that are underrepresented in naturalistic driving data.

By Tong Nie, Yuewen Mei, Junlin He, Yihong Tang, Jian Sun, Wei Ma

agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 3

ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization

arXiv:2501. 17377v4 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) has emerged as a promising approach for solving Combinatorial Optimization (CO) problems, such as the 3D Bin Packing Problem (3D-BPP), Traveling Salesman Problem (TSP), or Vehicle Routing Problem (VRP), but these neural solvers often exhibit brittleness when facing distribution shifts.

By Han Fang, Paul Weng, Yutong Ban

reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 1

RAISE: LLM-based Automated Heuristic Design with Robust Adversary Instance Search

arXiv:2606. 31801v1 Announce Type: new Abstract: Automated Heuristic Design (AHD) with Large Language Models (LLMs) has shown remarkable progress in discovering high-quality heuristics.

By Fei Liu, Alessio Figalli, Patrick Owen, Nicola Serra

llms safety

arXiv AI

Aug 6

Stabilizing Multi-Attack Adversarial Training via Bandit Optimization

arXiv:2511. 12265v2 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) remain vulnerable to diverse adversarial perturbations, motivating multi-attack adversarial training (AT) for improved robustness.

By Rui Wang, Zeming Wei, Xiyue Zhang, Meng Sun

reinforcement-learning safety

arXiv AI

Jun 3

EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents

arXiv:2606. 03678v1 Announce Type: new Abstract: Generating safety-critical scenarios is essential for validating and improving autonomous driving systems, yet it inherently requires maximizing adversariality to expose failures while preserving realism.

By Tong Nie, Yuewen Mei, Yihong Tang, Junlin He, Jie Deng, Jian Sun, Wei Ma

llms agents robotics benchmarks safety

arXiv AI

Aug 10

Evolving Parallel Algorithm Portfolios via Potential-Aware Instance Generation with LLMs

arXiv:2608. 06808v1 Announce Type: new Abstract: The Automatic Construction of Portfolios via Large Language Models (LLM-ACP) suffers from poor generalization in practical few-shot scenarios when solving complex combinatorial optimization problems.

By Shaofeng Zhang, Shengcai Liu, Zhiyuan Wang, Ke Tang

llms benchmarks

arXiv AI

Jun 10

Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey

arXiv:2403. 00420v3 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex environments.

By Lucas Schott, Josephine Delas, Hatem Hajri, Elies Gherbi, Reda Yaich, Nora Boulahia-Cuppens, Frederic Cuppens, Sylvain Lamprier

agents reinforcement-learning safety

arXiv AI

Aug 11

ACEvo: Adversarial Co-Evolution of Problem Distributions and Solvers for Combinatorial Optimization

arXiv:2506. 02594v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to synthesize heuristic programs, yet most existing pipelines optimize solvers against fixed benchmark distributions.

By Ruibo Duan, Yuxin Liu, Haoran Ye, Xinyao Dong, Zhiqiang Xu, Chenglin Fan

llms benchmarks safety

arXiv AI

Jul 13

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization

arXiv:2602. 07764v2 Announce Type: replace-cross Abstract: Multi-objective reinforcement learning (MORL) seeks to train agents capable of balancing conflicting objectives.

By Tanmay Ambadkar, Sourav Panda, Shreyash Kale, Jonathan Dodge, Abhinav Verma

agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 25

Learning Structural Hardness for Combinatorial Auctions: Instance-Dependent Algorithm Selection via Graph Neural Networks

arXiv:2602. 14772v2 Announce Type: replace Abstract: The Winner Determination Problem (WDP) in combinatorial auctions is NP-hard, and no existing method reliably predicts which instances will defeat fast greedy heuristics.

By Sungwoo Kang

benchmarks safety

arXiv Machine Learning

Jul 7

Training Verifiably Robust Agents Using Set-Based Reinforcement Learning

arXiv:2408. 09112v2 Announce Type: replace Abstract: Reinforcement learning policies parametrized by deep neural networks have achieved strong performance for continuous control, yet even small input perturbations may lead to unpredictable behavior.

By Manuel Wendl, Lukas Koller, Tobias Ladner, Matthias Althoff

agents reinforcement-learning benchmarks safety

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