The three most common instructions graduate students receive are all very short: “do a sweep of the literature,” “see what’s new,” “walk me through that XX paper.” Vague instructions aren’t your advisor’s problem—you’re missing a “translation” step: turning one sentence into concrete actions. This article translates these three instructions using MuchSci’s paper retrieval assistant. It’s connected to your local paper library, and its answers are based only on real literature in the library, with source sections cited. In essence, it’s a graduate-student AI assistant that lives inside your literature library.
Figure 1 | Translation comparison for the three instructions: one sentence expanded into a task sheet.
Instruction 1: “Do a sweep of the literature in this direction”
Translate it into three actions:
1. Two rounds of semantic search—“Find me a few papers on XXX.” Use topic terms in the first round, then switch to synonyms in the second. Semantic search goes by meaning, not literal wording; only by asking in different ways can you catch what the first pass missed;
2. Take inventory once—ask “What papers are in the database?” The paper list query lists every paper already in the library, so you can see exactly what you have;
3. Produce a role-based list—label each paper with a role: core literature, comparison literature, or background literature. The deliverable of “going through the literature” is this list, not the words “I’ve looked at it.”
Figure 2 | The three actions of “going through the literature” and the role-based list.
Instruction 2: “See what’s new in the past two years”
Translate it into two conditions plus one page:
l State theyear range in the query when searching (past two years/past three years);
l Then addcitation count as an extra filter to see which new works are already being discussed;
l Produce a “one-page recent developments” summary: three to five papers, each positioned in one sentence. Next time your advisor asks “What’s new lately?”, pull out this page.
Instruction 3: “Walk me through that XX paper”
The shelf life of “walk me through it” depends on depth. After asking it “What is this XXX paper about?”, use deep analysis to fill in the three layers:
l Paragraph summaries—follow the logic paragraph by paragraph as in the original, so when you present you can say “why this part was designed this way”;
l Technical terms—verify each piece of jargon one by one, so you won’t misuse a single term when presenting to your advisor;
l Figure and table analysis—the evidence in methods papers is all in the figures; you need to be able to say what each figure is proving;
l Chunked data—the chunked content and detailed metadata in the paper details let every paraphrase trace back to “which paper, which chunk.”
Once the three instructions are translated, they map to four capabilities: semantic search, conditional filtering, list queries, and deep analysis—an advisor’s one sentence always needs to be expanded by you into a task sheet.
Figure 3 | The four layers of “walk me through it”: the four-piece deep analysis toolkit.
The bottom line beyond translation
It has two hard rules: if the library contains relevant content, quote the original text and cite the source paper and section; if the library doesn’t, say plainly “No relevant information found” and never fabricate using training knowledge. These two rules are your confidence when relaying to your advisor—every “the literature says” you utter is truly in the literature.
(Note: The feature description is based on the publicly stated capabilities of MuchSci’s paper retrieval assistant (semantic search, year/citation filtering, paper list queries, deep analysis, chunked data and metadata, and the two honesty rules), subject to the actual product version.)