{"total":22,"rows":[{"id":"2602.23413","title":"EvoX: Meta-Evolution for Automated Discovery","created":"2026-02-26","cited_by_count":60,"area":null},{"id":"2507.15887","title":"AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?","created":"2025-07-19","cited_by_count":27,"area":null},{"id":"2512.15699","title":"FrontierCS: Evolving Challenges for Evolving Intelligence","created":"2025-12-17","cited_by_count":23,"area":null},{"id":"2605.08678","title":"MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI","created":"2026-05-09","cited_by_count":17,"area":null},{"id":"2509.08269","title":"A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving","created":"2025-09-10","cited_by_count":11,"area":null},{"id":"2601.21526","title":"KAPSO: A Knowledge-grounded framework for Autonomous Program Synthesis and Optimization","created":"2026-01-29","cited_by_count":11,"area":null},{"id":"2509.18180","title":"Large Language Models in Operations Research: Methods, Applications, and Challenges","created":"2025-09-18","cited_by_count":6,"area":null},{"id":"2606.29082","title":"Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks","created":"2026-06-27","cited_by_count":6,"area":null},{"id":"2606.30182","title":"MirrorCode: AI can rebuild entire programs from behavior alone","created":"2026-06-29","cited_by_count":6,"area":null},{"id":"2601.19924","title":"OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling","created":"2026-01-09","cited_by_count":5,"area":null},{"id":"2507.13337","title":"FormulaOne: Measuring the Depth of Algorithmic Reasoning Beyond Competitive Programming","created":"2025-07-17","cited_by_count":3,"area":null},{"id":"2605.14445","title":"FrontierSmith: Synthesizing Open-Ended Coding Problems at Scale","created":"2026-05-14","cited_by_count":3,"area":null},{"id":"2605.15308","title":"SMCEvolve: Principled Scientific Discovery via Sequential Monte Carlo Evolution","created":"2026-05-14","cited_by_count":2,"area":null},{"id":"2606.01975","title":"Algorithmic algorithm development with LLMs: A Case Study on LLM-Usage for Contraction Order Optimization in Tensor Networks","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2606.27369","title":"Reinforcement Learning without Ground-Truth Solutions can Improve LLMs","created":"2026-06-25","cited_by_count":0,"area":null},{"id":"2608.02612","title":"BBOWP-Bench: Evaluating LLMs on Black-Box Optimization Word Problems","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2609.34427","title":"LLMs as Adaptive Meta-Solvers: Strategy-Diverse RL for Industrial-Scale Optimization","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.40340","title":"EvoDuet: Bilevel Co-Evolution of Web Searching and Task Solving for Scientific Discovery","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.03675","title":"FrugalEvo: Towards Cost-Aware LLM-Guided Program Evolution","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.05334","title":"AgentDiscover: Autonomous Discovery with Minimal Search Scaffolding","created":"2026-10-04","cited_by_count":0,"area":null},{"id":"2610.14539","title":"FDE-Bench: Evaluating End-to-End Delivery from Underspecified Real-World Business Requests","created":"2026-10-10","cited_by_count":0,"area":null},{"id":"2610.17370","title":"HarnessTTT: Learn to Optimize Harness at Test-Time for Scientific Discovery","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}