research-agent-benchmark

Benchmarking local LLMs as autonomous research subagents requires a defined prompt suite and a controlled evaluation protocol to generate structured academic deliverables, reference results, and metadata for scoring model capabilities across prompt planning, tool use, and synthesis.

Evaluating LLM capabilities on literature search and synthesis involves automating end-to-end benchmark orchestration, which includes reference generation, contender runs, and scoring on a structured academic topic to produce reproducible references and experiment results.

To start generating reference responses for an LLM evaluation benchmark, prompt the system to initiate Phase 1 reference generation for a chosen topic, then review the generated reference and metadata suitable for comparison and replication.

Yes, you can use this benchmarking approach to compare model autonomy and synthesis quality across different LLMs, as it produces a composite score and structured deliverables designed for fair model comparison on scholarly topics.

Running a controlled evaluation protocol for research subagents requires a defined prompt suite, local LLMs to act as contenders, and a structured topic to generate the reference responses and metadata necessary for systematic scoring.