{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/combinatorial-optimization/papers/5","list_of":"/task/combinatorial-optimization","task":"Combinatorial Optimization","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":5,"pages_in_order":13,"rows_per_page":100,"rows":[401,500],"of":1277,"counts":{"archive_papers_tagged":1277,"with_a_code_link":401,"where_syntology_ran_a_sample":122,"not_listed_spam_title":0,"listed":1277,"listed_where_code_ran":122,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":105,"every_run_a_failure_of_syntologys_instrument":17,"listed_with_a_run_with_no_instrument_failure":105,"listed_every_run_a_failure_of_syntologys_instrument":17,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/combinatorial-optimization","prev":"/task/combinatorial-optimization/papers/4","next":"/task/combinatorial-optimization/papers/6","papers":[{"url":"/paper/optimization-by-simulated-annealing","slug":"optimization-by-simulated-annealing","title":"Optimization by Simulated Annealing","date":"1983-05-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":null,"slug":"large-language-models-for-combinatorial-1","title":"Large Language Models for Combinatorial Optimization: A Systematic Review","date":"2025-07-04","arxiv_id":"2507.03637","repositories_listed":0,"syntology":null},{"url":null,"slug":"lrm-1b-towards-large-routing-model","title":"LRM-1B: Towards Large Routing Model","date":"2025-07-04","arxiv_id":"2507.03300","repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-neuromorphic-ising-machines","title":"Higher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability","date":"2025-06-24","arxiv_id":"2506.19964","repositories_listed":0,"syntology":null},{"url":null,"slug":"greedyprune-retenting-critical-visual-token","title":"GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models","date":"2025-06-16","arxiv_id":"2506.13166","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthesizing-min-max-control-barrier","title":"Synthesizing Min-Max Control Barrier Functions For Switched Affine Systems","date":"2025-06-12","arxiv_id":"2506.10296","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-for-design-structure","title":"Large Language Models for Design Structure Matrix Optimization","date":"2025-06-11","arxiv_id":"2506.09749","repositories_listed":0,"syntology":null},{"url":null,"slug":"synergizing-reinforcement-learning-and","title":"Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization","date":"2025-06-11","arxiv_id":"2506.09404","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08448","title":"Systematic and Efficient Construction of Quadratic Unconstrained Binary Optimization Forms for High-order and Dense Interactions","date":"2025-06-10","arxiv_id":"2506.08448","repositories_listed":0,"syntology":null},{"url":null,"slug":"preference-driven-multi-objective","title":"Preference-Driven Multi-Objective Combinatorial Optimization with Conditional Computation","date":"2025-06-10","arxiv_id":"2506.08898","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08073","title":"Domain Switching on the Pareto Front: Multi-Objective Deep Kernel Learning in Automated Piezoresponse Force Microscopy","date":"2025-06-09","arxiv_id":"2506.08073","repositories_listed":0,"syntology":null},{"url":null,"slug":"hycolor-an-efficient-heuristic-algorithm-for","title":"HyColor: An Efficient Heuristic Algorithm for Graph Coloring","date":"2025-06-09","arxiv_id":"2506.07373","repositories_listed":0,"syntology":null},{"url":null,"slug":"adam-assisted-fully-informed-particle-swarm","title":"Adam assisted Fully informed Particle Swarm Optimzation ( Adam-FIPSO ) based Parameter Prediction for the Quantum Approximate Optimization Algorithm (QAOA)","date":"2025-06-07","arxiv_id":"2506.06790","repositories_listed":0,"syntology":null},{"url":null,"slug":"ealg-evolutionary-adversarial-generation-of","title":"EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization","date":"2025-06-03","arxiv_id":"2506.02594","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-the-pod-repositioning-problem-with","title":"Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search","date":"2025-06-03","arxiv_id":"2506.02746","repositories_listed":0,"syntology":null},{"url":null,"slug":"thinking-out-of-the-box-hybrid-sat-solving-by","title":"Thinking Out of the Box: Hybrid SAT Solving by Unconstrained Continuous Optimization","date":"2025-05-31","arxiv_id":"2506.00674","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-distributions-over-permutations-and","title":"Learning Distributions over Permutations and Rankings with Factorized Representations","date":"2025-05-30","arxiv_id":"2505.24664","repositories_listed":0,"syntology":null},{"url":"/paper/rethinking-neural-combinatorial-optimization","slug":"rethinking-neural-combinatorial-optimization","title":"Rethinking Neural Combinatorial Optimization for Vehicle Routing Problems with Different Constraint Tightness Degrees","date":"2025-05-30","arxiv_id":"2505.24627","repositories_listed":0,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/rethinking-neural-combinatorial-optimization#ran","syntology_url":"https://syntology.ai/paper/2505.24627","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.24627"}},"official":null}},{"url":null,"slug":"llm-oddr-a-large-language-model-framework-for","title":"LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioning","date":"2025-05-28","arxiv_id":"2505.22695","repositories_listed":0,"syntology":null},{"url":"/paper/generalizable-heuristic-generation-through","slug":"generalizable-heuristic-generation-through","title":"Generalizable Heuristic Generation Through Large Language Models with Meta-Optimization","date":"2025-05-27","arxiv_id":"2505.20881","repositories_listed":0,"syntology":{"n":13,"n_ran":9,"n_constructed":2,"n_ran_checked":3,"n_instrument":6,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"9 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 6 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/generalizable-heuristic-generation-through#ran","syntology_url":"https://syntology.ai/paper/2505.20881","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.20881"}},"official":null}},{"url":null,"slug":"efficient-optimization-accelerator-framework","title":"Efficient Optimization Accelerator Framework for Multistate Ising Problems","date":"2025-05-26","arxiv_id":"2505.20250","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-for-dynamic-combinatorial","title":"Learning for Dynamic Combinatorial Optimization without Training Data","date":"2025-05-26","arxiv_id":"2505.19497","repositories_listed":0,"syntology":null},{"url":null,"slug":"redahd-reduction-based-end-to-end-automatic","title":"RedAHD: Reduction-Based End-to-End Automatic Heuristic Design with Large Language Models","date":"2025-05-26","arxiv_id":"2505.20242","repositories_listed":0,"syntology":null},{"url":null,"slug":"moose-chem2-exploring-llm-limits-in-fine","title":"MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery via Hierarchical Search","date":"2025-05-25","arxiv_id":"2505.19209","repositories_listed":0,"syntology":null},{"url":null,"slug":"lmask-learn-to-solve-constrained-routing","title":"LMask: Learn to Solve Constrained Routing Problems with Lazy Masking","date":"2025-05-23","arxiv_id":"2505.17938","repositories_listed":0,"syntology":null},{"url":null,"slug":"strcmp-integrating-graph-structural-priors","title":"STRCMP: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization","date":"2025-05-22","arxiv_id":"2506.11057","repositories_listed":0,"syntology":null},{"url":"/paper/tropical-attention-neural-algorithmic","slug":"tropical-attention-neural-algorithmic","title":"Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms","date":"2025-05-22","arxiv_id":"2505.17190","repositories_listed":0,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tropical-attention-neural-algorithmic#ran","syntology_url":"https://syntology.ai/paper/2505.17190","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.17190"}},"official":null}},{"url":null,"slug":"a-quantum-enhanced-power-flow-and-optimal","title":"A Quantum-Enhanced Power Flow and Optimal Power Flow based on Combinatorial Reformulation","date":"2025-05-21","arxiv_id":"2505.15978","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-algorithm-feedback-one-shot-sat","title":"Learning from Algorithm Feedback: One-Shot SAT Solver Guidance with GNNs","date":"2025-05-21","arxiv_id":"2505.16053","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-quantum-digital-twins-for-optimizing","title":"Neural Quantum Digital Twins for Optimizing Quantum Annealing","date":"2025-05-21","arxiv_id":"2505.15662","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-local-search-mcmc-layers","title":"Learning with Local Search MCMC Layers","date":"2025-05-20","arxiv_id":"2505.14240","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-normalized-cut-problem-with","title":"Normalized Cut with Reinforcement Learning in Constrained Action Space","date":"2025-05-20","arxiv_id":"2505.13986","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-alignment-for-benchmarking-graph-neural","title":"Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings","date":"2025-05-19","arxiv_id":"2505.13087","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-computing-and-ai-perspectives-on","title":"Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering","date":"2025-05-15","arxiv_id":"2505.10012","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generative-neural-annealer-for-black-box","title":"A Generative Neural Annealer for Black-Box Combinatorial Optimization","date":"2025-05-14","arxiv_id":"2505.09742","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-bias-generalized-rollout-policy","title":"Adaptive Bias Generalized Rollout Policy Adaptation on the Flexible Job-Shop Scheduling Problem","date":"2025-05-13","arxiv_id":"2505.08451","repositories_listed":0,"syntology":null},{"url":null,"slug":"preference-optimization-for-combinatorial","title":"Preference Optimization for Combinatorial Optimization Problems","date":"2025-05-13","arxiv_id":"2505.08735","repositories_listed":0,"syntology":null},{"url":null,"slug":"exact-spin-elimination-in-ising-hamiltonians","title":"Exact Spin Elimination in Ising Hamiltonians and Energy-Based Machine Learning","date":"2025-05-12","arxiv_id":"2505.07163","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-large-language-model-enhanced-q-learning","title":"A Large Language Model-Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Windows","date":"2025-05-09","arxiv_id":"2505.06178","repositories_listed":0,"syntology":null},{"url":null,"slug":"primal-dual-algorithm-for-contextual","title":"Primal-dual algorithm for contextual stochastic combinatorial optimization","date":"2025-05-07","arxiv_id":"2505.04757","repositories_listed":0,"syntology":null},{"url":null,"slug":"unico-towards-a-unified-model-for","title":"UniCO: Towards a Unified Model for Combinatorial Optimization Problems","date":"2025-05-07","arxiv_id":"2505.06290","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-column-generation-and-large","title":"Integrating Column Generation and Large Neighborhood Search for Bus Driver Scheduling with Complex Break Constraints","date":"2025-05-05","arxiv_id":"2505.02485","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-learn-with-quantum-optimization","title":"Learning to Learn with Quantum Optimization via Quantum Neural Networks","date":"2025-05-01","arxiv_id":"2505.00561","repositories_listed":0,"syntology":null},{"url":null,"slug":"qaoa-parameter-transferability-for-maximum","title":"QAOA Parameter Transferability for Maximum Independent Set using Graph Attention Networks","date":"2025-04-29","arxiv_id":"2504.21135","repositories_listed":0,"syntology":null},{"url":null,"slug":"fitness-landscape-of-large-language-model","title":"Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search","date":"2025-04-28","arxiv_id":"2504.19636","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-the-brain-drain-optimization","title":"Application of the Brain Drain Optimization Algorithm to the N-Queens Problem","date":"2025-04-26","arxiv_id":"2504.18953","repositories_listed":0,"syntology":null},{"url":null,"slug":"qaoa-pca-enhancing-efficiency-in-the-quantum","title":"QAOA-PCA: Enhancing Efficiency in the Quantum Approximate Optimization Algorithm via Principal Component Analysis","date":"2025-04-23","arxiv_id":"2504.16755","repositories_listed":0,"syntology":null},{"url":null,"slug":"pgu-sgp-a-pheno-geno-unified-surrogate","title":"PGU-SGP: A Pheno-Geno Unified Surrogate Genetic Programming For Real-life Container Terminal Truck Scheduling","date":"2025-04-15","arxiv_id":"2504.11280","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-10-8mw-mixed-signal-simulated-bifurcation","title":"A 10.8mW Mixed-Signal Simulated Bifurcation Ising Solver using SRAM Compute-In-Memory with 0.6us Time-to-Solution","date":"2025-04-14","arxiv_id":"2504.10384","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-problem-parameter-transfer-in-quantum","title":"Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach","date":"2025-04-14","arxiv_id":"2504.10733","repositories_listed":0,"syntology":null},{"url":null,"slug":"erl-mpp-evolutionary-reinforcement-learning","title":"ERL-MPP: Evolutionary Reinforcement Learning with Multi-head Puzzle Perception for Solving Large-scale Jigsaw Puzzles of Eroded Gaps","date":"2025-04-13","arxiv_id":"2504.09608","repositories_listed":0,"syntology":null},{"url":null,"slug":"annealed-mean-field-descent-is-highly","title":"Annealed Mean Field Descent Is Highly Effective for Quadratic Unconstrained Binary Optimization","date":"2025-04-11","arxiv_id":"2504.08315","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-reduction-with-unsupervised-learning-in","title":"Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application","date":"2025-04-11","arxiv_id":"2504.08401","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-vehicle-routing-via-ai","title":"Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms","date":"2025-04-08","arxiv_id":"2504.06126","repositories_listed":0,"syntology":null},{"url":null,"slug":"algorithm-discovery-with-llms-evolutionary","title":"Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning","date":"2025-04-07","arxiv_id":"2504.05108","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-assisted-high-speed","title":"Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems","date":"2025-03-31","arxiv_id":"2503.23966","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-learning-for-quadratic","title":"Unsupervised Learning for Quadratic Assignment","date":"2025-03-25","arxiv_id":"2503.20001","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-variational-quantum-algorithms-by","title":"Enhancing variational quantum algorithms by balancing training on classical and quantum hardware","date":"2025-03-20","arxiv_id":"2503.16361","repositories_listed":0,"syntology":null},{"url":null,"slug":"combinatorial-optimization-for-all-using-llms","title":"Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms","date":"2025-03-14","arxiv_id":"2503.10968","repositories_listed":0,"syntology":null},{"url":null,"slug":"preference-elicitation-for-multi-objective","title":"Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation","date":"2025-03-14","arxiv_id":"2503.11435","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-constraint-based-adaptive-hypergraph","title":"Towards Constraint-Based Adaptive Hypergraph Learning for Solving Vehicle Routing: An End-to-End Solution","date":"2025-03-13","arxiv_id":"2503.10421","repositories_listed":0,"syntology":null},{"url":null,"slug":"combinatorial-optimization-via-llm-driven","title":"Combinatorial Optimization via LLM-driven Iterated Fine-tuning","date":"2025-03-10","arxiv_id":"2503.06917","repositories_listed":0,"syntology":null},{"url":"/paper/neural-combinatorial-optimization-via","slug":"neural-combinatorial-optimization-via","title":"Neural Combinatorial Optimization via Preference Optimization","date":"2025-03-10","arxiv_id":"2503.07580","repositories_listed":0,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/neural-combinatorial-optimization-via#ran","syntology_url":"https://syntology.ai/paper/2503.07580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.07580"}},"official":null}},{"url":null,"slug":"object-packing-and-scheduling-for-sequential","title":"Object Packing and Scheduling for Sequential 3D Printing: a Linear Arithmetic Model and a CEGAR-inspired Optimal Solver","date":"2025-03-07","arxiv_id":"2503.05071","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-penalty-based-learning-for","title":"Self-Supervised Penalty-Based Learning for Robust Constrained Optimization","date":"2025-03-07","arxiv_id":"2503.05175","repositories_listed":0,"syntology":null},{"url":null,"slug":"l2r-learning-to-reduce-search-space-for","title":"Learning to Reduce Search Space for Generalizable Neural Routing Solver","date":"2025-03-05","arxiv_id":"2503.03137","repositories_listed":0,"syntology":null},{"url":null,"slug":"a2perf-real-world-autonomous-agents-benchmark","title":"A2Perf: Real-World Autonomous Agents Benchmark","date":"2025-03-04","arxiv_id":"2503.03056","repositories_listed":0,"syntology":null},{"url":null,"slug":"lattice-protein-folding-with-variational","title":"Lattice Protein Folding with Variational Annealing","date":"2025-02-28","arxiv_id":"2502.20632","repositories_listed":0,"syntology":null},{"url":null,"slug":"preference-based-gradient-estimation-for-ml","title":"Preference-Based Gradient Estimation for ML-Guided Approximate Combinatorial Optimization","date":"2025-02-26","arxiv_id":"2502.19377","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizn-a-python-library-for-developing","title":"optimizn: a Python Library for Developing Customized Optimization Algorithms","date":"2025-02-25","arxiv_id":"2503.00033","repositories_listed":0,"syntology":null},{"url":null,"slug":"text2zinc-a-cross-domain-dataset-for-modeling","title":"Text2Zinc: A Cross-Domain Dataset for Modeling Optimization and Satisfaction Problems in MiniZinc","date":"2025-02-22","arxiv_id":"2503.10642","repositories_listed":0,"syntology":null},{"url":null,"slug":"position-graph-learning-will-lose-relevance","title":"Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks","date":"2025-02-20","arxiv_id":"2502.14546","repositories_listed":0,"syntology":null},{"url":null,"slug":"ccja-context-coherent-jailbreak-attack-for","title":"CCJA: Context-Coherent Jailbreak Attack for Aligned Large Language Models","date":"2025-02-17","arxiv_id":"2502.11379","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphthought-graph-combinatorial-optimization","title":"GraphThought: Graph Combinatorial Optimization with Thought Generation","date":"2025-02-17","arxiv_id":"2502.11607","repositories_listed":0,"syntology":null},{"url":null,"slug":"planning-of-heuristics-strategic-planning-on","title":"Planning of Heuristics: Strategic Planning on Large Language Models with Monte Carlo Tree Search for Automating Heuristic Optimization","date":"2025-02-17","arxiv_id":"2502.11422","repositories_listed":0,"syntology":null},{"url":null,"slug":"tss-gaz-ptp-towards-improving-gumbel","title":"TSS GAZ PTP: Towards Improving Gumbel AlphaZero with Two-stage Self-play for Multi-constrained Electric Vehicle Routing Problems","date":"2025-02-17","arxiv_id":"2502.15777","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-existing-optimization-algorithms","title":"Improving Existing Optimization Algorithms with LLMs","date":"2025-02-12","arxiv_id":"2502.08298","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-discrete-diffusion-samplers","title":"Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics","date":"2025-02-12","arxiv_id":"2502.08696","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-evaluation-for-job-shop-scheduling","title":"Self-Evaluation for Job-Shop Scheduling","date":"2025-02-12","arxiv_id":"2502.08684","repositories_listed":0,"syntology":null},{"url":null,"slug":"currency-arbitrage-optimization-using-quantum","title":"Currency Arbitrage Optimization using Quantum Annealing, QAOA and Constraint Mapping","date":"2025-02-08","arxiv_id":"2502.15742","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-stochastic-combinatorial","title":"Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning","date":"2025-02-08","arxiv_id":"2502.05537","repositories_listed":0,"syntology":null},{"url":null,"slug":"unrealized-expectations-comparing-ai-methods","title":"Unrealized Expectations: Comparing AI Methods vs Classical Algorithms for Maximum Independent Set","date":"2025-02-05","arxiv_id":"2502.03669","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-tsp-solvers-tend-to-be-overly","title":"Learning-Based TSP-Solvers Tend to Be Overly Greedy","date":"2025-02-02","arxiv_id":"2502.00767","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolving-hard-maximum-cut-instances-for","title":"Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms","date":"2025-01-30","arxiv_id":"2502.12012","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-quantum-combinatorial-optimization","title":"Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver","date":"2025-01-28","arxiv_id":"2501.16986","repositories_listed":0,"syntology":null},{"url":null,"slug":"making-sense-of-distributed-representations","title":"Making Sense Of Distributed Representations With Activation Spectroscopy","date":"2025-01-26","arxiv_id":"2501.15435","repositories_listed":0,"syntology":null},{"url":null,"slug":"pso-and-the-traveling-salesman-problem-an","title":"PSO and the Traveling Salesman Problem: An Intelligent Optimization Approach","date":"2025-01-25","arxiv_id":"2501.15319","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-visualization-and-optimization","title":"Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimization","date":"2025-01-21","arxiv_id":"2501.11968","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-constrained-beam","title":"Reinforcement Learning Constrained Beam Search for Parameter Optimization of Paper Drying Under Flexible Constraints","date":"2025-01-21","arxiv_id":"2501.12542","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-instance-specific-algorithm","title":"Fast instance-specific algorithm configuration with graph neural network","date":"2025-01-20","arxiv_id":"2501.11240","repositories_listed":0,"syntology":null},{"url":null,"slug":"convergence-and-running-time-of-time","title":"Convergence and Running Time of Time-dependent Ant Colony Algorithms","date":"2025-01-18","arxiv_id":"2501.10810","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulation-of-hypergraph-algorithms-with","title":"Neural Algorithmic Reasoning for Hypergraphs with Looped Transformers","date":"2025-01-18","arxiv_id":"2501.10688","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-key-algorithms-for-optimizing","title":"Random-Key Algorithms for Optimizing Integrated Operating Room Scheduling","date":"2025-01-17","arxiv_id":"2501.10243","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-gain-estimation-for-running-time-of","title":"Multiple-gain Estimation for Running Time of Evolutionary Combinatorial Optimization","date":"2025-01-13","arxiv_id":"2501.07000","repositories_listed":0,"syntology":null},{"url":null,"slug":"pareto-optimization-with-robust-evaluation","title":"Pareto Optimization with Robust Evaluation for Noisy Subset Selection","date":"2025-01-12","arxiv_id":"2501.06813","repositories_listed":0,"syntology":null},{"url":null,"slug":"annealing-machine-assisted-learning-of-graph","title":"Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization","date":"2025-01-10","arxiv_id":"2501.05845","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-for-deep-unfolded","title":"Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver with Quantum Annealer","date":"2025-01-07","arxiv_id":"2501.03518","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-approximation-algorithms-for-low","title":"Improved Approximation Algorithms for Low-Rank Problems Using Semidefinite Optimization","date":"2025-01-06","arxiv_id":"2501.02942","repositories_listed":0,"syntology":null},{"url":null,"slug":"relaxation-assisted-reverse-annealing-on","title":"Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization","date":"2025-01-03","arxiv_id":"2501.02114","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-non-uniform-timestep-sampling-for-1","title":"Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"02fb23137bd706cece5c7d907a984d032fcfe4beecb4f1e6d828f03fb52dd0af","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}