# Amplifying human performance in combinatorial competitive programming

arXiv: https://arxiv.org/abs/2411.19744 · HTML: https://mlsanity.com/paper/2411.19744/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)

## Built on by (most cited first, 10 of 10)

- [AlphaEvolve: A coding agent for scientific and algorithmic discovery](https://mlsanity.com/paper/2506.13131/) (2025)
- [ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering](https://mlsanity.com/paper/2506.09050/) (2025)
- [Complex LLM Planning via Automated Heuristics Discovery](https://mlsanity.com/paper/2502.19295/) (2025)
- [(X)-evolve: Solution space evolution powered by large language models](https://mlsanity.com/paper/2508.07932/) (2025)
- [Diffusion Large Language Models for Black-Box Optimization](https://mlsanity.com/paper/2601.14446/) (2026)
- [GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models](https://mlsanity.com/paper/2509.21593/) (2025)
- [Learning the ARTS of Search for Automated Discovery](https://mlsanity.com/paper/2606.21891/) (2026)
- [From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective](https://mlsanity.com/paper/2607.06786/) (2026)
- [ABCDWaveNet: Advancing Robust Road Ponding Detection in Fog through Dynamic Frequency-Spatial Synergy](https://mlsanity.com/paper/2504.05112/) (2025)
- [An Open-Source Training Dataset for Foundation Models for Black-box Optimization](https://mlsanity.com/paper/2605.23417/) (2026)

## Abstract (arXiv)

Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors. While steady progress has been made, the highest percentiles still remain out of reach for these methods on standard competition platforms such as Codeforces. Here we instead focus on combinatorial competitive programming, where the target is to find as-good-as-possible solutions to otherwise computationally intractable problems, over specific given inputs. We hypothesise that this scenario offers a unique testbed for human-AI synergy, as human programmers can write a backbone of a heuristic solution, after which AI can be used to optimise the scoring function used by the heuristic. We deploy our approach on previous iterations of Hash Code, a global team programming competition inspired by NP-hard software engineering problems at Google, and we leverage FunSearch to evolve our scoring functions. Our evolved solutions significantly improve the attained scores from their baseline, successfully breaking into the top percentile on all previous Hash Code online qualification rounds, and outperforming the top human teams on several. Our method is also performant on an optimisation problem that featured in a recent held-out AtCoder contest.