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

A new benchmark called IdeaGene-Bench (IG-Bench) has been proposed for evaluating AI systems' ability to follow the inheritance structure of scientific ideas, similar to how biological genomes pass traits from one generation to another. The benchmark contains 1,961 golden lineage traces, 1,085 curated Idea Genome objects, and 920 pairwise GenomeDiff records across 10 scientific domains. It includes two evaluations: IG-Exam tests the AI's ability to reason about lineages and verify ideas, while IG-Arena assesses the AI's ability to generate new ideas that fit into a given lineage population.

What the Document Says

• IdeaGene-Bench (IG-Bench) is a benchmark for scientific lineage reasoning and lineage-grounded idea generation.
• IG-Bench is organized around the IdeaGene framework, representing each paper or proposal as a set of minimal, typed, evidence-grounded Idea Genome objects.
• 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.
• IG-Bench supports two evaluations: IG-Exam and IG-Arena.
• IG-Exam 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.
• Experiments on 14 LLM-based scientists expose a compositional bottleneck.

Key Concepts Mentioned

• IdeaGene-Bench
• IG-Exam
• IG-Arena
• GenomeDiff
• Idea Genome objects
• scientific lineage reasoning
• lineage-grounded idea generation

Claims Made by the Document

• The proposed benchmark, IG-Bench, can evaluate AI systems' ability to follow the inheritance structure of scientific ideas.

Evidence Used

• The description and organization of IG-Bench as presented in the text.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

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.