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The paper proposes the Plan, Learn, Adapt (PLA) framework for personalized on-device itinerary generation, combining feasibility-guaranteed combinatorial planning with human preference learning via a Bradley-Terry reward model. In deployment, it achieved a 91% increase in itinerary completion rates with low latency, outperforming frontier LLMs in feasibility.
QDEvo integrates Quality-Diversity optimization with LLM-driven heuristic search to overcome mode collapse in automated heuristic design, outperforming state-of-the-art methods on benchmarks and real-world applications.
This paper proposes C2TSP, an end-to-end unsupervised learning method for the Traveling Salesman Problem that learns a tractable distribution over near-tour structures using a connected-by-construction Gibbs family, incorporating implicit differentiation and certificate-guided sharpening to preserve interpretable Hamiltonian structure.
Proposes GES, a learning-based graph sparsification method for Euclidean TSP that adaptively prunes up to 95% of edges while maintaining solution quality within 1% of optimal, demonstrating strong generalization.
This paper presents a hybrid search framework that combines Thompson sampling with parallel self-avoiding walks to adaptively allocate computational effort across restriction classes for the LABS problem. The method improves previously best-known merit factors for 35 sequence lengths and achieves a new longest sequence with merit factor exceeding 8.0.
This survey explores how combinatorial optimization can be used to enhance trustworthy machine learning, covering topics like interpretability, robustness, fairness, and privacy with formal guarantees.
This paper presents a 2048-spin bulk acoustic wave Ising machine using microwave delay lines, achieving all-to-all connectivity and solving MAX-CUT, number partitioning, and Sudoku problems with higher thermal stability and performance compared to state-of-the-art coherent Ising machines.
This paper proposes formalizing scientific discovery as a meta-optimization problem where LLMs generate and aggregate objective functions via correlation-weighted voting, applied to 3-SAT algorithm discovery using digital MemComputing, achieving a 67x speedup on large instances.
This paper studies parallel Continuous Local Search (CLS) for Boolean satisfiability with pseudo-Boolean constraints, revealing that redundant constraints can inhibit convergence and that CLS shows promise as a sub-solver in hybrid settings.
This paper presents Accelerated Fourier SAT (AFSAT), a GPU-accelerated solver for pseudo-Boolean satisfiability based on continuous local search. It improves upon prior proof-of-concept implementations by supporting heterogeneous constraints and leveraging JAX for parallel computation.
Researchers from the University of Amsterdam propose a tabular reinforcement learning approach to the Metro Network Expansion Problem, showing it achieves comparable performance to Deep RL while reducing training episodes by 18x and carbon emissions by 12x on average. The method also incorporates social equity criteria and is evaluated on real-world metro networks in Xi'an and Amsterdam.
Proposes CoEvo-AHD, an LLM-driven dual-population co-evolutionary framework for automated heuristic design in bi-component coupled combinatorial optimization problems. It leverages LLMs to co-evolve route and selection operators, using cooperative evaluation and joint crossover to discover complementary heuristics for problems like TTP and TPP.
This paper presents a graph-learning-aided optimization approach for designing active tether-net systems to capture space debris, using a GNN to recommend candidate designs and reduce mixed-combinatorial nonlinear programming to standard NLP problems, achieving faster convergence.
Introduces LoRe, a training-free wrapper that enforces per-step interaction budgets for iterative graph solvers, achieving substantial speedups and memory reductions on combinatorial optimization problems like MIS and TSP.
TriVAL introduces a tri-validation framework that performs explicit validation at three stages of automatic optimization modeling (semantic specification, mathematical formulation, code generation) to improve faithfulness, and also presents NL4COP, a new benchmark for combinatorial optimization problems.
This paper presents the aircraft disassembly scheduling problem, a large-scale combinatorial optimization task involving thousands of tasks, precedence relations, balance constraints, and limited space. It proposes a Constraint Programming model and a MIP model tested on real operational instances with up to 1450 tasks.
Presents WeCon, a weight-conditioned neural solver for multi-objective combinatorial optimization problems that achieves comparable hypervolume to the state-of-the-art while reducing inference time by 40%.
COAgents is a cooperative multi-agent framework for solving Vehicle Routing Problems that models search as a graph, using specialized agents for node selection, move selection, and jumps to escape local minima. It achieves state-of-the-art results on CVRP and VRPTW benchmarks, reducing the gap to best-known solutions by up to 44% compared to prior learning-based methods.
This paper presents a method to automatically generate local search neighborhoods from constraint specifications using symmetry properties, evaluated on six optimization problems.
This paper identifies mode collapse in on-policy RL methods like GRPO and proposes DMPO, which approximates forward KL minimization to maintain solution diversity. It achieves significant improvements on NP-hard combinatorial optimization and mathematical reasoning tasks.