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This paper introduces AHD Agent, a framework using agentic reinforcement learning to enable LLMs to autonomously design heuristics for combinatorial optimization problems by dynamically interacting with the solving environment.
This paper introduces HMACE, a heterogeneous multi-agent collaborative evolution framework that uses Large Language Models to automate heuristic design for NP-hard combinatorial optimization problems. It demonstrates improved quality-efficiency trade-offs over single-agent and multi-agent baselines on problems like TSP and BPP.
This paper introduces a composite-move Tabu search algorithm for spatial redistricting that improves solution quality and efficiency while preserving contiguity constraints.
Introduces Graph Normalization, a differentiable dynamical system for approximating Maximum Weight Independent Set, with convergence guarantees and applications in structured sparse attention and constrained optimization.