REPOGEO REPORT · LITE

Ayanami0730/deep_research_bench

Default branch main · commit 469cce54 · scanned 6/9/2026, 2:32:47 PM

GitHub: 751 stars · 82 forks

AI VISIBILITY SCORE

33 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Ayanami0730/deep_research_bench, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Add a concise introductory paragraph to the README

Why:

COPY-PASTE FIX

Add a paragraph immediately after the H1 and badges, e.g., 'DeepResearch Bench provides a comprehensive and standardized framework for evaluating the performance of advanced AI and LLM agents in complex research tasks. It offers a robust benchmark to objectively compare different agent architectures and methodologies, focusing on their ability to conduct deep, multi-step research.' (Adjust to fit project specifics).

hightopics#2
Refine repository topics for better AI agent specificity
Why:
CURRENT
```
agent, benchmark, deepresearch, nlp
```
COPY-PASTE FIX
```
llm-agents, ai-agents, agent-benchmark, llm-evaluation, deep-research
```
mediumhomepage#3
Update the repository homepage URL
Why:
CURRENT
```
https://arxiv.org/pdf/2506.11763
```
COPY-PASTE FIX
```
https://deepresearch-bench.github.io/
```

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface Ayanami0730/deep_research_bench

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Papers With Code

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Papers With Code · recommended 1×
GLUE/SuperGLUE · recommended 1×
SciFact · recommended 1×
ArXiv QA · recommended 1×
BioASQ · recommended 1×

CATEGORY QUERY
How can I objectively compare the performance of different deep research AI agents?
you: not recommended
AI recommended (in order):
1. Papers With Code
2. GLUE/SuperGLUE
3. SciFact
4. ArXiv QA
5. BioASQ
6. MedQA
7. MMLU (Massive Multitask Language Understanding)
8. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
9. BLEU (Bilingual Evaluation Understudy)
10. METEOR
11. BERTScore
12. Hugging Face Transformers (huggingface/transformers)
13. Hugging Face Datasets (huggingface/datasets)
14. LangChain (langchain-ai/langchain)
15. LlamaIndex (run-llama/llama_index)
16. MLflow (mlflow/mlflow)
17. Weights & Biases (W&B) (wandb/wandb)
AI recommended 17 alternatives but never named Ayanami0730/deep_research_bench. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best methods for benchmarking advanced NLP research agents comprehensively?
you: not recommended
AI recommended (in order):
1. Hugging Face Datasets and Evaluate Libraries
2. EleutherAI's LM Evaluation Harness (EleutherAI/lm-evaluation-harness)
3. BigBench
4. GLUE and SuperGLUE Benchmarks
5. MMLU
6. HELM
7. Amazon Mechanical Turk
8. Appen
AI recommended 8 alternatives but never named Ayanami0730/deep_research_bench. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of Ayanami0730/deep_research_bench?
pass
AI named Ayanami0730/deep_research_bench explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Ayanami0730/deep_research_bench in production, what risks or prerequisites should they evaluate first?
pass
AI named Ayanami0730/deep_research_bench explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo Ayanami0730/deep_research_bench solve, and who is the primary audience?
pass
AI did not name Ayanami0730/deep_research_bench — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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Ayanami0730/deep_research_bench — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite