BBO-Pile is the first large-scale open dataset of real optimization trajectories used to train and scale foundation models that imitate black-box optimization methods.
Veliˇckovi´c, A
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abstract
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.
years
2026 4representative citing papers
LLM-driven evolutionary program synthesis discovers Generalized Superfast Encodings with exact distance 5 (and 6 on one instance) for molecular Hamiltonians, the first beyond distance 3.
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Autogenic network management extends agentic AI with self-programming, self-reflection, self-orienting, and self-architecting so 6G management planes can generate and evolve their own automation software at runtime.
citing papers explorer
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An Open-Source Training Dataset for Foundation Models for Black-box Optimization
BBO-Pile is the first large-scale open dataset of real optimization trajectories used to train and scale foundation models that imitate black-box optimization methods.
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Evolving Quantum Error-Correcting Encodings for Molecular Simulation
LLM-driven evolutionary program synthesis discovers Generalized Superfast Encodings with exact distance 5 (and 6 on one instance) for molecular Hamiltonians, the first beyond distance 3.
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Learning the ARTS of Search for Automated Discovery
ARTS improves automated scientific discovery by using reasoning LMs with test-time training to separate hypothesis merit from execution quality in tree search, achieving 15.3% relative gains on 22 MLGym and MLEBench tasks.
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From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective
Autogenic network management extends agentic AI with self-programming, self-reflection, self-orienting, and self-architecting so 6G management planes can generate and evolve their own automation software at runtime.