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TRACE: Tackling Real-World Resource Assignment Problems via Agentic Heuristic Design
Oct 1, 2026Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-PerezAutomated Heuristic Design Repair-Assignment Problem
NEC Laboratories Europe · i2CAT Foundation and ICREA
When Is Deletion Ordering Tractable? From Update Dynamics to Permutation Structure
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HALO: Heterogeneous Allocation Via Localized Observations for the Vehicle Routing Problem
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Department of Intelligent Systems Engineering, Indiana University Bloomington · Department of Computer Science, Indiana University Bloomington
Every Cache Entry Earns Its Place: Global Allocation of Resolution and Coverage for KV Cache Compression
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Tsinghua University Beijing, China
RASP-QAOA: Resource-Aware Per-Instance Selection for Exact QAOA Simulation
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Institute of Smart Industry and Green Energy, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Model or Harness? An Interaction-Centric Taxonomy for Localizing Agent Failures
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Scale AI
Exploring Budgeted Image Classification with Content-Sensitive Resource Allocation
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Tandon School of Engineering, New York University
Incremental Optimal Assignment for Real-Time Crowd Tracking
Jul 23, 2026Ismail H. TorosluRepair-Assignment Problem Multi-Object Tracking
Dept. of Computer Eng., METU, Ankara, Türkiye
Attribution Markets: A Fisher-Market Formulation for Fractional Credit Assignment Between Planned Tasks and Performed Actions
Jul 22, 2026Salavat IshbulatovMatching Markets Repair-Assignment Problem
Independent researcher
WASABI: Whole-graph Assignment-based Stabilizer for lAne topology By Inter-frame tracking
Jul 22, 2026Tetsuhiro Uchida, Myu Sasaki, Kensho Nakajima +2Autonomous Driving Perception Autonomous Driving
Sony Honda Mobility Inc.
Varying Bundle Size Reactive Multi-Task Assignment using Selective Cost Estimation for Multi-Agent Systems
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Luleå University of Technology, Luleå, SE-971 87 Sweden
Optimality-Preserving Decomposition for Scalable QAOA in Natural-Language-Guided Multi-Drone Assignment
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Department of Smart Convergence Korea University Republic of Korea · School of Electrical Engineering Korea University Republic of Korea
Learning Unbiased Permutations via Flow Matching
May 16, 2026Yimeng Min, Carla P. GomesPermutation Conditional Flow Matching
Department of Computer Science Cornell University Ithaca, NY, USA
Multi-Agent Cooperative Transportation: Optimal and Efficient Task Allocation and Path Finding
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University of Bristol
Identifiable Token Correspondence for World Models
May 15, 2026Youngin Kim, Ray Sun, Inho Kim +2World Models World
Interdisciplinary Program in Artificial Intelligence, Seoul National University · Department of Computer Science and Engineering, Seoul National University · KRAFTON
QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning
May 12, 2026Kien X. Nguyen, Ankit Kulshrestha, Ilya Safro +1Quantum Circuits Qubit
Department of Computer and Information Sciences, University of Delaware, USA · Quantum Lab, Fujitsu Research of America, USA
Learning-Augmented Scalable Linear Assignment Problem Optimization via Neural Dual Warm-Starts
May 10, 2026Ilay Yavlovich, Jad Agbaria, Muhamed Mhamed +2Repair-Assignment Problem Combinatorial Optimization
Department of Electrical and Computer Engineering, Technion – Israel Institute of Technology, Haifa, Israel.
Many-to-Many Multi-Agent Pickup and Delivery
May 8, 2026Ethan Schneider, Jingkai Chen, Tianyi Gu +3Warehouse Automation Multi-Robot Systems
Institute of Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA · Symbotic Inc., Wilmington, MA 01887, USA · Northeastern University, Boston, MA 02115, USA
Alternating Target-Path Planning for Scalable Multi-Agent Coordination
May 8, 2026Yu Kumagai, Keisuke OkumuraMulti-Agent Path Finding Multi-Agent Coordination
1Hitotsubashi University, Japan · 2National Institute of Advanced Industrial Science and Technology (AIST), Japan
ARMATA: Auto-Regressive Multi-Agent Task Assignment
May 5, 2026Yazan Youssef, Aboelmagd Noureldin, Sidney GivigiRepair-Assignment Problem Autoregressive Model
Department of Electrical and Computer Engineering, Queen’s University, Kingston, ON, Canada · Department of Electrical and Computer Engineering at the Royal Military College of Canada, and the Department of Electrical and Computer Engineering at Queen’s University, both in Kingston, Canada · School of Computing, Queen’s University, Kingston, ON, Canada
Active multiple matrix completion with adaptive confidence sets
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OvGU Magdeburg · SequeL team, Inria Lille
Learning to Solve the Quadratic Assignment Problem with Warm-Started MCMC Finetuning
Apr 22, 2026Yicheng Pan, Ruisong Zhou, Haijun Zou +2Repair-Assignment Problem Annealing
School of Mathematical Science, Peking University, Beijing 100871, China · State Key Laboratory of Mathematical Sciences, Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China · Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China
SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary
Feb 20, 2026Shafkat Farabi, Didac Marti Pinto, Wei Lu +4Repair-Assignment Problem Mediation
Virginia Tech · University College London · The World Bank
A Game-Theoretic Spatio-Temporal Reinforcement Learning Framework for Collaborative Public Resource Allocation
Oct 30, 2025Songxin Lei, Qiongyan Wang, Yanchen Zhu +6Repair-Assignment Problem Game Theory
The Hong Kong University of Science and Technology (Guangzhou) · Peking University · Beijing Institute of Technology +1
Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility
May 24, 2025Yiheng Li, Feng Liang, Dan Kondratyuk +3Diffusion Models Diffusion Dynamics
UC Berkeley, Berkeley CA 94720, USA · UT Austin, Austin TX 78712, USA · Rekursiv.ai, Mountain View CA, 94041
Context-Aware System Synthesis, Task Assignment, and Routing
Jun 14, 2017Jason Ziglar, Ryan Williams, Alfred WicksRepair-Assignment Problem Humanoid Robotics

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TRACE: Tackling Real-World Resource Assignment Problems via Agentic Heuristic Design

Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez

NEC Laboratories Europe · i2CAT Foundation and ICREA

Automated Heuristic Design Repair-Assignment Problem Heuristics Traces Synthetic Benchmark

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Dynamic resource assignment, the real-time allocation of task streams to heterogeneous processing nodes, is the backbone of modern computing infrastructure. While learning-based schedulers excel in research, industrial deployments still rely on hand-written rules that operators can read, audit, and execute within tight latency budgets. LLM-based Automatic Heuristic Design (AHD) promises to automate writing such rules. However, existing AHD frameworks were developed for combinatorial problems fully specified to the LLM, and they learn only from a scalar fitness score. In real systems, the behaviour that determines a good heuristic, such as processor speeds or power consumption, is unknown a priori: the score reveals which heuristic performs better, but not why. This missing information is recorded in the system logs that every evaluation produces. Exploiting it is non-trivial: logs are massive and noisy, the relevant signals depend on the objective, and their content and format vary across hardware and software stacks, so they can neither be fed to an LLM as is nor processed by a fixed parser. We propose TRACE, which couples an evolutionary AHD loop with an agentic knowledge-extraction workflow. A Reasoner agent analyzes the log schema in light of the objective and formulates hypotheses about the system dynamics; a Coder agent writes and executes schema-specific code to test them, producing insights or executable tools for the evolved heuristics. We evaluate TRACE on a synthetic cloud benchmark and a 5G vRAN scenario built from industrial testbed measurements and operational traffic traces. TRACE consistently outperforms state-of-the-art AHD methods in resource assignment problems and yields more auditable heuristics at under 2% overhead.

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