Academic research guide
Token compression for research paper analysis
Research papers are long (3,000-10,000 tokens each) and dense. When analyzing papers with LLMs, most of the text is irrelevant to a specific research question. Compression keeps the findings and methodology sections that matter.
Paper analysis with compression
from supercompress import Compressor
comp = Compressor()
def analyze_paper(paper_text, research_question):
result = comp.compress(paper_text, research_question)
return llm.generate(
f"Research question: {research_question}\nPaper:\n{result.compressed_text}"
)Frequently asked questions
Does compression preserve citations?
Yes. Lines containing citations relevant to the research question are preserved.
Can I analyze multiple papers simultaneously?
Yes. Compress each paper independently and combine the compressed results for cross-paper analysis.
Build with less context
Put compression in front of your next LLM call.
Use the hosted API or run SuperCompress locally. Keep the evidence, drop the token waste, and measure the savings before it reaches your model.