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This paper proposes Latent Heuristic Search (LHS), a framework that shifts heuristic discovery to a learned continuous latent manifold, using gradient-based optimization and normalizing flows to generate novel heuristics conditioned on large language models, achieving competitive results on TSP, CVRP, KSP, and Online Bin Packing.
This paper models the Resource-Constrained Project Scheduling Problem as optimal search over a Petri net reachability graph and solves it with A* guided by a consistent heuristic combining critical path and resource lower bounds, outperforming MIP baselines on PSPLIB benchmarks.
This paper proposes a unified knowledge-embedded reinforcement learning framework for generalized capacitated vehicle routing problems, combining route-first cluster-second heuristics with dynamic programming to achieve superior solution quality and strong generalization across diverse variants.
This paper introduces a framework for distribution-aware algorithm design where LLM agents learn to generate solver code specialized to target distributions, achieving high solution quality and significant speedups over standard solvers.
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.