Hypotheses
BioSkepsis turns an assembled evidence base into empirically testable hypotheses. The agent synthesizes findings across the literature and proposes novel, mechanistically reasoned hypotheses — each grounded in specific source papers, and each explicit about where the proposed work extends beyond what has already been shown.
#Testable hypotheses from the evidence
A hypothesis in BioSkepsis is not a free-floating idea. It is a claim built directly on the evidence the agent has already gathered and verified for your research question — the same corpus of 40M+ papers drawn from the life-science literature that grounds every other output. The agent reads across those findings, identifies where mechanisms connect or gaps remain, and proposes a statement that is specific enough to be tested and falsifiable enough to be worth testing.
Because the hypothesis is anchored to real papers, you can trace every part of its rationale back to the finding that motivated it. Where the proposed work reaches past the current evidence — which any genuinely novel hypothesis must — the agent marks that reach explicitly rather than papering over it. The result is a hypothesis you can defend in a grant, a lab meeting, or a review: transparent about what is established, what is inferred, and what remains to be demonstrated.
In a grant-writing workflow, this output maps directly to a Specific Aims section — a falsifiable claim with the reasoning, predictions, and controls a reviewer expects to see.
#How a hypothesis is built
The agent constructs a hypothesis through a disciplined, evidence-first sequence rather than by guessing at a plausible-sounding claim:
- Evidence analysis. The agent reads across the papers assembled for your question, extracting the key findings, molecular mechanisms, and relationships that recur in the literature.
- Pattern recognition. It identifies patterns, unresolved contradictions, and candidate connections across studies — the seams in the field where a new claim can do useful work.
- Hypothesis synthesis. From those patterns it composes a testable statement driven by mechanistic reasoning, not by surface similarity between papers.
- Citation grounding. Every element of the hypothesis is tied to specific papers, each tagged with an evidence tier and a confidence level so you can see exactly how far the underlying data stretches.
#Fundamental and cross-cutting framing
Depending on the research focus, a hypothesis can be framed in two ways. A fundamental hypothesis is discovery-driven and mechanism-first — it targets basic biological processes, causal relationships, and pathway-level predictions, and suits basic-science questions about how a system works. A cross-cutting hypothesis is integrative — it connects findings across different domains or research areas to surface unexpected links and translational opportunities. Both are held to the same standard of evidence grounding; they differ only in the kind of connection they are reaching for.
#Anatomy of a hypothesis
Each generated hypothesis is a structured artefact, not a single sentence. Every component is designed to make the claim testable and to expose the reasoning behind it.
#Mechanistic rationale
A reasoning chain that explains why the hypothesis should hold — the underlying biological logic connecting the cited findings to the proposed claim. This is what distinguishes a mechanistic hypothesis from a correlation: it names the pathway, interaction, or process that would produce the predicted effect.
#Explicit predictions
Concrete, testable predictions that would confirm or refute the hypothesis. Good predictions are directional and measurable — they state what you should observe if the hypothesis is true, and by implication what you should observe if it is false.
#Proposed study design
A suggested experimental approach for testing the hypothesis — the model systems, interventions, and assays that would put the predictions to the test. This is the bridge from a claim to a research plan; in a grant workflow the agent can expand it into a full lab-ready methodology with sample-size and power analysis, endpoints, and Go/No-Go thresholds.
#Confounders and controls
The variables that could produce a misleading result and the controls that would rule them out. Surfacing confounders up front keeps the design honest and signals to a reviewer that the proposed experiment can actually distinguish the hypothesis from its alternatives.
#Risks and limitations
An explicit account of where the hypothesis is fragile — assumptions that may not hold, evidence that is thinner than ideal, and practical constraints on testing it. The agent does not hide the weak points; it names them so you can decide whether and how to address them.
#Falsification criteria
The conditions under which the hypothesis would be considered refuted. A hypothesis you cannot falsify is not a scientific hypothesis, so the agent states in advance what result would force you to abandon or revise the claim.
#Evidence grounding and confidence
Every hypothesis statement must be tied to at least one source paper and classified into one of three evidence tiers. The tier describes how directly the cited evidence supports the claim.
| Tier | What it means |
|---|---|
| Direct | The cited paper explicitly reports the finding in the same disease or tissue context as the claim. |
| Derived | The claim is synthesized from information across cited papers within the same disease area. |
| Indirect | The evidence comes from a different disease, organism, or model system and is applied to the target context by analogy. |
Each tier is paired with a confidence level — High, Medium, or Low — so you can gauge how far the data stretches beyond what was directly measured. A Direct claim at High confidence rests on solid ground; an Indirect claim at Low confidence is a genuine reach that the hypothesis is proposing to close.
#Generating materially distinct hypotheses
A single question rarely has a single answer worth pursuing. You can ask the agent for additional hypotheses to explore different angles on the same evidence base, and each new hypothesis is required to be materially distinct from the ones before it — a different mechanistic rationale, a different pathway focus, or a different experimental angle, rather than a restatement of an earlier claim.
This is what makes hypothesis generation useful for framing competing aims: instead of one plausible story, you get a spread of genuinely different mechanistic bets, each grounded and each falsifiable, so you can choose which to pursue on scientific merit.
#Getting stronger hypotheses
- Assemble a relevant evidence base. The hypothesis is only as good as the literature behind it — make sure the papers the agent is working from cover the key aspects of your question.
- Mix foundational and recent work. Combining established mechanisms with the latest findings gives the agent room to propose connections that neither would suggest alone.
- Ground the direction in the landscape first. Running a research-landscape analysis to surface hubs, bridges, and clusters gives the agent a map of where the field's seams are before it proposes where to cut.
- Iterate. Generate several distinct hypotheses, then refine toward the ones that best fit your research priorities and resources.
The agent compresses the literature backbone of a hypothesis; the scientific judgement about which claim is worth testing, and how, remains yours.