local-deep-research/ldr-benchmarks

LDR Community Benchmarks (Leaderboards)

Aggregated leaderboards for Local Deep Research (LDR) community benchmark runs against SimpleQA, BrowseComp, and xbench-DeepSearch.

👉 Submit results, read raw YAMLs, open PRs:

github.com/LearningCircuit/ldr-benchmarks

This Hugging Face dataset hosts only the aggregated CSV leaderboards. It is regenerated automatically on every merge to main in the GitHub repo above. Each CSV row represents one benchmark run (one strategy from one YAML submission).

Why the split?

• GitHub is the source of truth for raw YAML submissions, PR review, CI validation, and leaderboard regeneration.
• Hugging Face renders the aggregated CSVs in its Dataset Viewer and makes the leaderboards discoverable inside the ML community.

Raw per-run YAMLs — including configuration details, notes, and (where permitted by the benchmark's sharing policy) per-question examples — live in the GitHub repo under results/.

Benchmarks covered

• SimpleQA — OpenAI, MIT-licensed. Full per-question examples allowed in raw YAMLs on GitHub.
• BrowseComp — OpenAI, encrypted dataset with canary string. Only aggregate metrics are accepted (no per-question examples in raw YAMLs).
• xbench-DeepSearch — xbench team, encrypted dataset. Only aggregate metrics are accepted (no per-question examples in raw YAMLs).

See the GitHub repo's README for the full sharing policy.

Leaderboard columns

Each CSV row contains:

dataset, model, model_provider, quantization, strategy, search_engine, accuracy_pct, accuracy_raw, correct, total, iterations, questions_per_iteration, avg_time_per_question, total_tokens_used, temperature, context_window, max_tokens, hardware_gpu, hardware_ram, hardware_cpu, evaluator_model, evaluator_provider, ldr_version, date_tested, contributor, notes, source_file

The source_file column points at the raw YAML in the GitHub repo.

Configs

Use the dropdown at the top of the Dataset Viewer to switch between:

• all — every run, all benchmarks combined (default)
• simpleqa — SimpleQA runs only
• browsecomp — BrowseComp runs only
• xbench-deepsearch — xbench-DeepSearch runs only

Considerations for using the data

This is a community-submitted leaderboard, not a controlled experiment. Keep these caveats in mind when interpreting results:

• Self-reported. Runs are submitted by contributors. CI validates schema and flags obvious issues, but the runs themselves are not independently re-executed.
• Evaluator bias. Many submissions use an LLM grader (default is Claude 3.7 Sonnet via OpenRouter). LLM evaluators have non-trivial error rates; a manual audit of ~200 SimpleQA questions commonly surfaces one or two grading mistakes.
• Small sample sizes. Many runs use 50–200 questions. Confidence intervals at that scale are wide (roughly ±5–7 percentage points at n=200). Small differences between rows are usually not significant.
• Timing is environment-dependent. avg_time_per_question depends on hardware, network latency, search engine responsiveness, and model server load.
• Contamination risk. SimpleQA is publicly distributed and may appear in some models' training data. BrowseComp and xbench mitigate this with encryption, but older model generations may still be contaminated.
• Strategy semantics drift. LDR strategies evolve between versions. Prefer comparing runs tagged with the same ldr_version.

Attribution

• SimpleQA © OpenAI — MIT License
• BrowseComp © OpenAI — see the BrowseComp paper and openai/simple-evals
• xbench © xbench team — see xbench-ai/xbench-evals

Plain-text distribution of BrowseComp and xbench questions or answers is prohibited.

Contributors

Thanks to everyone who has contributed benchmark runs:

• LearningCircuit — 2 submissions
• kwhyte7 — 1 submission

Citation

@misc{ldr_community_benchmarks,
  title        = {LDR Community Benchmarks},
  author       = {The Local Deep Research community},
  year         = {2026},
  publisher    = {Hugging Face / GitHub},
  howpublished = {\url{https://huggingface.co/datasets/local-deep-research/ldr-benchmarks}}
}

License

This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). If you use the data in research, publications, or derivative analyses, please cite it using the BibTeX entry above.

Individual benchmark datasets (SimpleQA, BrowseComp, xbench) retain their own upstream licenses — see the Attribution section.