SciConBench: AI Agent Benchmark for Scientific Conclusion Synthesis

SciConBench is a large-scale, live benchmark for evaluating AI agents on open-domain scientific conclusion synthesis. The dataset, created by hayoungjung, focuses on the long-horizon task of retrieving and assessing evidence from the open web to produce expert-level scientific conclusions. It was last updated on June 11, 2026.

Use Cases

• Benchmarking AI agents on long-horizon scientific reasoning based on the described task of conclusion synthesis.
• Evaluating evidence retrieval and quality assessment capabilities based on the open-domain web evidence mentioned.
• Training models to reconcile conflicting scientific findings based on the described task of information integration.
• Developing systems for filtering irrelevant or unreliable scientific evidence as described.

Strengths

• Designed as a large-scale benchmark, suggesting substantial scope.
• Focuses on a live, multi-step task involving evidence retrieval, assessment, and synthesis.
• Targets the production of expert-level, long-form scientific conclusions.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.

Provenance

Source

huggingface

Collection Method

Likely gathered for benchmarking AI agents on scientific tasks.

Freshness

Last updated 2026-06-11 13:35:31; freshness should be verified.