一键导入
literature-research
// Deep literature research — raw full text reading and targeted PDF queries for rigorous analysis
// Deep literature research — raw full text reading and targeted PDF queries for rigorous analysis
| name | Literature Research |
| description | Deep literature research — raw full text reading and targeted PDF queries for rigorous analysis |
| type | sop |
| layer | sop |
| agents | ["alphaxiv","semantic-scholar"] |
| tools | {"alphaxiv":["discover_papers","get_paper_content","answer_pdf_queries"],"semantic-scholar":["relevanceSearch","paper","paperBatch","citations","references"]} |
| input | query (string), focus (experiment-design | methodology | comparison | replication) |
| output | DeepAnalysis[] with metadata + raw full text + targeted query results |
Deep reading. Raw full text, targeted PDF queries. For rigorous analysis, experiment design, and paper writing. This is the highest-depth skill — you read the actual paper content, not summaries.
Use this when you need to:
This skill reads RAW FULL TEXT. AI summaries are not acceptable at this depth.
| Tool | Purpose | Returns |
|---|---|---|
alphaxiv.discover_papers | Primary search — arXiv semantic search | Ranked paper list with metadata |
ss.relevanceSearch | Supplementary search — non-arXiv papers | Title, abstract, authors, citationCount |
ss.paper / ss.paperBatch | Metadata enrichment | Citation count, DOI, S2 ID |
ss.citations | Papers that cite this paper | Citing paper list with context |
ss.references | Papers this paper cites | Referenced paper list |
alphaxiv.get_paper_content | Raw full text (fullText: true) | Complete paper text as markdown |
alphaxiv.answer_pdf_queries | Targeted PDF questions | Relevant page content as XML |
AI summaries (fullText: false) are NOT acceptable for this skill.
PROHIBITED:
REQUIRED:
get_paper_content(fullText: true) for every key paper (minimum 3)answer_pdf_queries for targeted extraction of specific detailsPrimary (arXiv):
alphaxiv.discover_papers(
keywords: ["keyword1", "keyword2", "keyword3"],
question: "Detailed description of papers needed for deep analysis",
difficulty: 7
)
Supplementary (non-arXiv):
ss.relevanceSearch(
query: "search terms",
limit: 20,
year: "2022-2024"
)
Use higher difficulty (7-10) for research-depth searches — you need comprehensive coverage.
ss.paperBatch(
paper_ids: ["ARXIV:2301.xxxxx", "ARXIV:2302.xxxxx", ...]
)
Choose 3-10 papers for deep reading based on:
Fewer papers, read deeply > many papers, read shallowly.
For each selected paper:
alphaxiv.get_paper_content(
url: "https://arxiv.org/abs/XXXX.XXXXX",
fullText: true
)
fullText: true returns the raw extracted text — complete paper content including:
For specific details that need precise extraction:
alphaxiv.answer_pdf_queries(
url: "https://arxiv.org/pdf/XXXX.XXXXX",
queries: [
"What is the exact model architecture?",
"What hyperparameters were used for training?",
"What datasets were used for evaluation?",
"What are the ablation study results?"
]
)
Notes:
Find important related work:
ss.citations(paper_id: "ARXIV:XXXX.XXXXX", limit: 50)
ss.references(paper_id: "ARXIV:XXXX.XXXXX", limit: 50)
For promising papers from the graph, repeat Steps 3-5.
<page num="N"> tags showing relevant content# Step 1: Search
alphaxiv.discover_papers(
keywords: ["LoRA", "parameter-efficient", "fine-tuning", "PEFT"],
question: "Papers proposing variants or improvements to LoRA for LLM fine-tuning",
difficulty: 7
)
# Step 2: Enrich
ss.paperBatch(paper_ids: ["ARXIV:2106.09685", "ARXIV:2305.14314", ...])
# Step 3: Select top 5 most relevant
# Step 4: Read full text
alphaxiv.get_paper_content(url: "https://arxiv.org/abs/2106.09685", fullText: true) # Original LoRA
alphaxiv.get_paper_content(url: "https://arxiv.org/abs/2305.14314", fullText: true) # QLoRA
# ... repeat for all 5
# Step 5: Extract specific details
alphaxiv.answer_pdf_queries(
url: "https://arxiv.org/pdf/2106.09685",
queries: [
"What is the rank r used in experiments?",
"What is the training compute compared to full fine-tuning?",
"Which layers have LoRA applied?"
]
)
# Step 1: Search
alphaxiv.discover_papers(
keywords: ["diffusion", "sampling", "DDPM", "DDIM", "DPM-Solver"],
question: "Papers proposing fast sampling methods for diffusion models",
difficulty: 8
)
# Step 4: Read full text of key papers
alphaxiv.get_paper_content(url: "https://arxiv.org/abs/2010.02502", fullText: true) # DDPM
alphaxiv.get_paper_content(url: "https://arxiv.org/abs/2010.02502", fullText: true) # DDIM
alphaxiv.get_paper_content(url: "https://arxiv.org/abs/2211.01095", fullText: true) # DPM-Solver++
# Step 5: Compare specific details
alphaxiv.answer_pdf_queries(
url: "https://arxiv.org/pdf/2211.01095",
queries: [
"What is the FID score with 10 sampling steps?",
"How does it compare to DDIM at the same step count?",
"What is the computational overhead of the solver?"
]
)
# Step 6: Find newer work
ss.citations(paper_id: "ARXIV:2211.01095", limit: 30)