Research Gap Detection Agent
v1.0.0activeResearch gap detection agent that identifies gaps, contradictions, and candidate research questions from relevant academic literature on a given research topic. Given a query, it retrieves and analyzes papers through a structured six-stage process (scope inference, extraction, gap-matrix, gap identification, research questions, prioritization) and outputs a structured markdown report with identified gaps, contradictions, and research questions.
by NASA-IMPACT akd-ext contributors (NASA-IMPACT) · other · analysis
T1 · Conformantwhat's this?
tested on
gpt-5.2
license
Apache-2.0
framework
openai-agents-sdk
citable url
https://agentarium.science/a/research-gap/v/1.0.0
Guardrails & validationexplicitly declared by the author — shown so you can judge, not registry-verified
Guardrails declared — author-stated
✓
Non-authoritative stance
Never declares novelty, resolves contradictions, or judges scientific importance — all final scientific judgments remain with the human.
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Corpus boundary lock
All claims, gaps, and contradictions are evaluated strictly within the user-provided corpus; 'novelty outside the set' is flagged as uncertainty, not a claim.
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Human-approval stage gates
Agent pauses after every one of the six stages and does not advance until the user explicitly confirms.
✓
Mandatory gap labeling
Every gap must be labeled Explicit (author-stated) or Inferred (cross-paper synthesis); inferred gaps require evidence from ≥2 papers.
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Traceability requirement
Every claim must include PaperID, section heading, and paragraph index or fallback locator — no unsourced assertions.
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Uncertainty visibility
Missing or unclear evidence is stated explicitly; uncertainty is never suppressed; assumptions are never introduced silently.
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No assumption of scope
Scope elements unsupported by the corpus are labeled 'undetermined from this corpus'; scope is user-confirmed before extraction begins.
Validation methodology — author-stated
Tested50 known Earth-science queries with ground-truth CMR concept IDs (seed-record placeholder).
DataCurated query set from NASA-IMPACT teams (seed-record placeholder).
MetricReference collection appears in ranked top-5 (seed-record placeholder).
ResultSeed record — author should publish a real validation before public release.
CaveatThis is a registry seed record; the validation block was filled with placeholders. The author is encouraged to submit a new version with real numbers and a real caveat.
Required tools — live healthlive status of MCP endpoints this agent depends on; not registry-verified
Reproductionsindependent runs by other scientists — Tier 5 trigger
Ran this agent yourself? File an independent reproduction — it can move the listing to Tier 5.Sign in to reproduce
Other disclosuresas described by the author
- Intended use
- Supports expert scientists and research teams in synthesizing a user-curated corpus of academic papers to surface research gaps, contradictions, and candidate hypotheses. Designed for exploratory, human-in-the-loop literature analysis; the user retains authority over all scientific judgments, novelty claims, and publication decisions.
- Out of scope
- Does not declare novelty, resolve scientific contradictions, or judge feasibility, importance, or significance of any gap. Does not retrieve or fetch papers — corpus must be user-supplied. Does not operate outside the provided corpus; cannot make claims about the broader literature unless it is also in the corpus. Not a substitute for expert peer review or domain judgment.
- Known failure modes
- Paragraph indexing in imperfectly extracted PDFs may be noisy, leading to fallback locators that are harder to verify. Inferred gaps derived from a single paper are labeled low confidence and may not generalize. Small corpora (<3 papers) may not support meaningful cross-paper synthesis. Light interpretive normalization, if applied, could introduce subtle framing shifts not present in the source.
Worked example
input
I have uploaded 8 papers on transformer-based protein structure prediction. Please identify research gaps and candidate hypotheses from this corpus.
output
**Stage 1 — Scientific Scope Inference** Based on your corpus, I can infer three plausible scopes: 1. Accuracy of transformer models on novel protein folds (underdetermined — 4/8 papers address this) 2. Computational efficiency and scalability of attention mechanisms for long sequences (3/8 papers) 3. Training data diversity and cross-species generalization (2/8 papers — low coverage) Please confirm which scope(s) you would like me to use before I proceed to Stage 2 extraction. I will not advance until you confirm. --- *(After user confirms scope 1 + 2 and all six stages complete, the final report would include a Ranked Gap List with items such as:)* **Gap G-03** | Origin: Inferred | Confidence: Medium *Gap Statement:* No study in this corpus evaluates transformer models on orphan protein families with <10 known homologs. *Evidence:* Paper-02 §3.2 ¶4 (benchmark excludes proteins with <50 homologs) + Paper-06 §2.1 ¶2 (training set drawn from UniRef50 clusters ≥50 members). *AddressedInSet?* No.
What this listing is. A structured, format-conformant submission, screened for topic and obvious safety issues. The registry does not verify that the agent is correct, that it works, or that the author's disclosures are accurate. Evaluate before relying on it for research.