Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Yifan Zhou, Qihao Yang, Yan Li, Donggang Li, Xiru Hu, Hokin Deng, Ziyang Gong, Xuanyi Zhou, Huacan Wang, Xiangchao Yan, Wanghan Xu, Wenlong Zhang, Shaofeng Zhang, Yue Zhou, Yifan Yang, Zhihang Zhong, Xue Yang
• Venue: arXiv: cs.AI
• Published: 2026-07-09
• Collected: 2026-07-10
• URL: http://arxiv.org/abs/2607.08758v1
• PDF: https://arxiv.org/pdf/2607.08758v1
• DOI: UNKNOWN

Plain-Language Brief

The text introduces IdeaGene-Bench (IG-Bench), a benchmark for evaluating AI systems' ability to follow the inheritance structure of scientific ideas, similar to how biological genomes evolve. IG-Bench uses the IdeaGene framework, which represents each paper or proposal as a set of minimal, typed, evidence-grounded objects called Idea Genomes. The benchmark consists of 1,961 golden lineage traces, 1,085 curated Idea Genome objects, and 920 pairwise GenomeDiff records across 10 scientific domains.

What the Document Says

• IG-Bench is a benchmark for evaluating AI systems' ability to follow the inheritance structure of scientific ideas.
• The benchmark consists of 1,961 golden lineage traces, 1,085 curated Idea Genome objects, and 920 pairwise GenomeDiff records across 10 scientific domains.
• IG-Bench is based on the IdeaGene framework that represents each paper or proposal as a set of Idea Genome objects.

Key Concepts Mentioned

• IdeaGene-Bench
• Idea Genome
• GenomeDiff
• scientific lineage reasoning
• lineage-grounded idea generation
• 10 scientific domains

Claims Made by the Document

• The current benchmarks say little about whether AI systems can follow the inheritance structure of scientific ideas.
• IG-Exam and IG-Arena are evaluations supported by IG-Bench.

Evidence Used

• The text mentions that current benchmarks do not evaluate scientific lineage reasoning in AI systems.
• The text describes IG-Exam and IG-Arena as evaluations supported by IG-Bench.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

Scientific ideas rarely start from a blank page. They inherit mechanisms, repair known limitations, and recombine pieces of earlier work, much like biological genomes. Current benchmarks still say little about whether AI systems can follow this inheritance structure. We present IdeaGene-Bench (IG-Bench), a benchmark for scientific lineage reasoning and lineage-grounded idea generation. IG-Bench is organized around the IdeaGene framework: each paper or proposal is represented as a set of minimal, typed, evidence-grounded Idea Genome objects, and a GenomeDiff aligns these objects to record inheritance, mutation, loss, external import, and novel insertion under six operational evolutionary dynamics. The benchmark contains 1,961 golden lineage traces, 1,085 curated Idea Genome objects, and 920 pairwise GenomeDiff records across 10 scientific domains. It supports two evaluations. IG-Exam (42 task types, 1,029 instances) tests closed-form lineage reasoning across Idea Genome abstraction, inheritance tracing, evolutionary reasoning, and lineage verification. IG-Arena evaluates generation with a lineage-conditioned Population-Evolution Score(PES), asking whether a proposal can be inserted as a coherent descendant of a given lineage population: it should inherit the right Idea Genome objects, vary meaningfully from nearby work, and offer selection value for future research. Experiments on 14 LLM-based scientists expose a compositional bottleneck. The strongest system reaches only 27.3% exact accuracy on lineage reasoning, and structured lineage context reshuffles system rankings rather than helping every participant uniformly.