How the agent selects sources

A search over the life-science literature can return hundreds of candidate papers. The agent does not hand you that pile — it narrows to the studies that actually matter for your question by weighing three complementary signals: semantic meaning, biological correlation through a knowledge graph, and where research momentum is building.

#Narrowing to the papers that matter

When the agent works a research question, retrieval is only the first step. Pulling papers from a corpus of 40M+ publications is easy; deciding which of them belong in your evidence base is the hard part. Keyword overlap alone is a poor filter — it misses papers that use different vocabulary for the same idea, and it rewards papers that merely mention your terms without studying them.

So the agent evaluates each candidate along three independent axes and forms a composite judgment. None of these is a switch you flip. They are criteria the agent balances on your behalf, adjusting emphasis as it reads the question and sees what the returned literature looks like.

• Top semantic relevance — how closely a paper's meaning matches the intent of your question.
• Biological correlation — whether a paper is connected to your topic through shared biology, even when the words differ.
• Active research frontiers — whether a paper sits in an area that is accelerating rather than dormant.

#Top semantic relevance

The first axis measures conceptual closeness. The agent represents both your question and each candidate paper as vector embeddings — numeric fingerprints of meaning — and ranks papers by how near their content sits to the meaning of what you asked, not by how many query words they happen to contain.

This is what lets the agent:

• Surface papers that describe the same concept using different terminology.
• Rank a study that is genuinely about your mechanism above one that name-drops your keyword in passing.
• Handle questions phrased in plain language without you having to guess the field's exact jargon.

Semantic relevance is the agent's baseline for “is this paper on topic?” — but conceptual similarity in text is not the whole story in biology, which is where the next axis comes in.

#Biological correlation through the knowledge graph

Two papers can describe the same biology in language a text model would never align. A study of a receptor and a study of its downstream pathway may share almost no vocabulary yet be mechanistically inseparable. To catch these, the agent consults a biology-native knowledge graph that links papers through the entities they share rather than the words they use.

The graph connects studies along signals such as:

• Shared genes and gene networks that appear across otherwise unrelated write-ups.
• Gene Ontology (GO) terms describing common molecular functions, biological processes, or cellular components.
• MeSH descriptors that place papers under the same curated biomedical concepts.
• Pathways and domain-specific keywords that reveal mechanistic relationships across studies.

By selecting along these edges, the agent recovers biologically meaningful connections that pure text similarity would miss — papers linked by a shared pathway or gene network even when their abstracts read as if they were written about different subjects.

#Active research frontiers

The third axis is temporal. Some clusters of the literature are dormant; others are accelerating, with a high proportion of recent publications signaling a field in motion. The agent applies temporal-momentum analysis to detect these rising clusters and can give weight to papers sitting on the cutting edge.

This matters when:

• The question concerns a fast-moving area where last year's consensus may be stale.
• You need to know what is emerging now, not only what has been settled and heavily cited.
• Momentum itself is a signal — a topic drawing a surge of new work is often where the open questions live.

Frontier weighting is a lens on recency and acceleration, not a replacement for the other two axes. A brand-new paper that is off topic or biologically disconnected still does not earn a place; momentum lifts papers that are already relevant.

#How the agent balances these

The three criteria are not applied in isolation and they are not summed by a fixed formula. The agent reads the shape of your question and adapts how heavily it leans on each axis. A request framed around a specific molecular mechanism pulls harder on biological correlation; a request to survey what is new in a field pulls harder on active frontiers; a precise, narrowly worded question leans on tight semantic matching.

This adaptation happens internally as part of the agent's reasoning — you do not choose a stance and the agent does not expose a three-way selector. Under the hood it shifts emphasis across fundamental, applied, and convergent framings depending on whether your question is about underlying mechanism, clinical translation, or cross-domain synthesis, so the selected set matches the kind of answer you are actually after.

The net effect: instead of handing you an unranked wall of results, the agent commits to a transparent set of choices — on topic by meaning, connected by biology, and aware of where the field is heading — and builds its analysis on the papers that genuinely matter for the question you asked.