23 citations · 36 across the 21 of their papers we have counts for
21 papers
LLM-Driven Algorithm Design for Quantum Circuit Synthesis based on Binary Decision Diagrams
Yoonju Sim, Federico Berto, Chuanbo Hua +2
Quantum circuits are central to implementing quantum algorithms on quantum devices, where quantum gates must be reversible. Many quantum algorithms rely on Boolean functions, which…
Rethinking Positional Encoding for Neural Vehicle Routing
Chuanbo Hua, Federico Berto, Andre Hottung +8
Transformer-based models have become the dominant paradigm for neural combinatorial optimization (NCO) of vehicle routing problems (VRPs), yet the role of positional encoding (PE)…
EvoNav: Evolutionary Reward Function Design for Robot Navigation with Large Language Models
Zhikai Zhao, Chuanbo Hua, Federico Berto +4
Robot navigation is a crucial task with applications to social robots in dynamic human environments. While Reinforcement Learning (RL) has shown great promise for this problem, the…
JudgeFlow: Agentic Workflow Optimization via Block Judge
Zihan Ma, Zhikai Zhao, Chuanbo Hua +2
Optimizing LLM-based agentic workflows is challenging for scaling AI capabilities. Current methods rely on coarse, end-to-end evaluation signals and lack fine-grained signals on wh…
VRPAgent: LLM-Driven Discovery of Heuristic Operators for Vehicle Routing Problems
André Hottung, Federico Berto, Chuanbo Hua +9
Designing high-performing heuristics for vehicle routing problems (VRPs) is a complex task that requires both intuition and deep domain knowledge. Large language model (LLM)-based…
TrajEvo: Trajectory Prediction Heuristics Design via LLM-driven Evolution
Zhikai Zhao, Chuanbo Hua, Federico Berto +4
Trajectory prediction is a critical task in modeling human behavior, especially in safety-critical domains such as social robotics and autonomous vehicle navigation. Traditional he…
TrajEvo: Designing Trajectory Prediction Heuristics via LLM-driven Evolution
Zhikai Zhao, Chuanbo Hua, Federico Berto +4
Trajectory prediction is a crucial task in modeling human behavior, especially in fields as social robotics and autonomous vehicle navigation. Traditional heuristics based on handc…
USPR: Learning a Unified Solver for Profiled Routing
Chuanbo Hua, Federico Berto, Zhikai Zhao +3
The Profiled Vehicle Routing Problem (PVRP) extends the classical VRP by incorporating vehicle-client-specific preferences and constraints, reflecting real-world requirements such…
RRNCO: Towards Real-World Routing with Neural Combinatorial Optimization
Jiwoo Son, Zhikai Zhao, Federico Berto +4
The practical deployment of Neural Combinatorial Optimization (NCO) for Vehicle Routing Problems (VRPs) is hindered by a critical sim-to-real gap. This gap stems not only from trai…
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems
Chuanbo Hua, Federico Berto, Jiwoo Son +3
The profiled vehicle routing problem (PVRP) is a generalization of the heterogeneous capacitated vehicle routing problem (HCVRP) in which the objective is to optimize the routes of…
PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization
Federico Berto, Chuanbo Hua, Laurin Luttmann +6
Combinatorial optimization problems involving multiple agents are notoriously challenging due to their NP-hard nature and the necessity for effective agent coordination. Despite ad…
RouteFinder: Towards Foundation Models for Vehicle Routing Problems
Federico Berto, Chuanbo Hua, Nayeli Gast Zepeda +6
This paper introduces RouteFinder, a comprehensive foundation model framework to tackle different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model f…
Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
Huijie Tang, Federico Berto, Jinkyoo Park
Multi-Agent Reinforcement Learning (MARL) based Multi-Agent Path Finding (MAPF) has recently gained attention due to its efficiency and scalability. Several MARL-MAPF methods choos…
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
Huijie Tang, Federico Berto, Zihan Ma +3
Large-scale multi-agent pathfinding (MAPF) presents significant challenges in several areas. As systems grow in complexity with a multitude of autonomous agents operating simultane…
Learning Efficient Surrogate Dynamic Models with Graph Spline Networks
Chuanbo Hua, Federico Berto, Michael Poli +2
While complex simulations of physical systems have been widely used in engineering and scientific computing, lowering their often prohibitive computational requirements has only re…
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Federico Berto, Chuanbo Hua, Junyoung Park +30
Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement lear…
Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences
Minsu Kim, Federico Berto, Sungsoo Ahn +1
We study the problem of optimizing biological sequences, e.g., proteins, DNA, and RNA, to maximize a black-box score function that is only evaluated in an offline dataset. We propo…
Transform Once: Efficient Operator Learning in Frequency Domain
Michael Poli, Stefano Massaroli, Federico Berto +4
Spectral analysis provides one of the most effective paradigms for information-preserving dimensionality reduction, as simple descriptions of naturally occurring signals are often…
Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction
Junyoung Park, Federico Berto, Arec Jamgochian +2
In this paper, we propose Meta-SysId, a meta-learning approach to model sets of systems that have behavior governed by common but unknown laws and that differentiate themselves by…
DevFormer: A Symmetric Transformer for Context-Aware Device Placement
Haeyeon Kim, Minsu Kim, Federico Berto +2
In this paper, we present DevFormer, a novel transformer-based architecture for addressing the complex and computationally demanding problem of hardware design optimization. Despit…
Neural Solvers for Fast and Accurate Numerical Optimal Control
Federico Berto, Stefano Massaroli, Michael Poli +1
Synthesizing optimal controllers for dynamical systems often involves solving optimization problems with hard real-time constraints. These constraints determine the class of numeri…