Literature and Method Scoping with Citation Verification

Tier: Foundation · Workflow area: Research & method scoping

The deliverable is a method brief your team can bet an implementation on, with a verified reading list.

When to use

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  Evaluating an unfamiliar technique for adoption (conformal prediction, causal forests, DPO, semantic chunking).
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  Preparing the related-work grounding for a design doc or internal proposal.
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  Rapidly mapping a subfield before an interview panel, client engagement, or build-vs-adopt call.

Skip the full protocol for techniques your team already runs in production — go read your own postmortems first.

Prerequisites

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  An assistant with web search / deep-research mode (Claude or GPT with browsing), plus a citation-native index — Semantic Scholar or Elicit — for verification and forward-citation walking.
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  Access to the papers themselves (arXiv covers most ML; know your library route for the rest).
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  A written decision context: what you would do differently depending on the answer. Scoping without a decision degenerates into infinite survey.

The workflow

1. 1.

   Frame the question around your constraints, not the field:

   Copy

   Research task: conformal prediction for uncertainty in our setting —
   tabular gradient-boosted regressor, n≈500k, covariate shift between
   regions, need per-prediction intervals with ~90% coverage, batch scoring.
   Produce: (1) the 3-5 distinct approaches relevant to THIS setting (split/
   full CP, CQR, Mondrian/conditional, shift-robust weighted CP), (2) for
   each: assumptions, failure modes under covariate shift, compute cost,
   library maturity, (3) the 8-10 load-bearing papers with authors + year +
   venue, (4) open disagreements in the field, (5) what practitioners report
   in production, separated from academic claims.
   

   Demanding "open disagreements" and the academic/practitioner split counters the model's consensus-flattening habit.

2. 2.

   Verify every citation before reading anything. For each claimed paper: find it on Semantic Scholar/arXiv by title; confirm authors, year, and venue; confirm the abstract supports the attached claim. Expect a 10–30% real-but-misattributed rate: the paper exists but says something adjacent. Fabrications and mismatches get struck, their dependent claims flagged.

3. 3.

   Walk citations both directions on the survivors. For the 2–3 load-bearing papers, use Semantic Scholar's cited-by and references. This is where you find the correction papers and "this fails under X" follow-ups the synthesis omitted; Elicit's similar-papers search accelerates the sweep.

4. 4.

   Interrogate the papers with the source attached. Upload the 3–4 papers that survive triage and ask decision-linked questions per paper: Under covariate shift between calibration and test data, what does THIS paper claim about coverage guarantees — exact quote — and what evidence supports it? What settings did they NOT test? Grounded QA over an attached paper is reliable in a way open-web synthesis is not.

5. 5.

   Hunt the practitioner layer separately. Ask specifically for implementation experience: library issue trackers (MAPIE for conformal prediction), engineering blogs, benchmark repos, conference-workshop talks. Weight a documented production failure mode above a paper's clean result — and verify these links resolve too.

6. 6.

   Write the brief with epistemic labels. One page: recommendation with confidence level; the method landscape table; assumptions we would violate; per-claim labels — [verified in paper], [practitioner report], [model synthesis — unverified]; the reading list (verified only); and a "what would change our mind" line. Anything still labeled model-synthesis either gets verified or explicitly carried as risk.

Tool variants

• Deep-research mode (Claude/GPT): best first pass for the landscape sweep; treat its bibliography as candidate leads, never as a bibliography.
• Semantic Scholar / Elicit: the verification and expansion layer — citation graphs, influence signals, structured claim extraction across papers.
• Assistant + attached PDFs: the deep-dive layer; most reliable mode of the three because generation is grounded in the document. Use it for every claim your recommendation leans on.

Verification

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  100% of citations in the final brief resolve to real papers whose content matches the attached claim (title-search + abstract check minimum; full-text check for load-bearing ones).
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  At least one claim from the initial synthesis was struck or corrected during verification — if nothing was, your check was too gentle, not the model too good.
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  The brief names at least one genuine open disagreement with both positions cited.
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  Forward-citation walk performed on the top papers (correction/refutation sweep).
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  Recommendation explicitly maps to your constraints (data size, shift, latency), not to general merit.

Pitfalls

• Fluent survey, fabricated spine. The synthesis reads authoritatively even when a third of its references are wrong; verification order matters — check citations before investing reading time.
• Consensus flattening. Models present the median view as settled; contested areas (e.g., conditional coverage claims) need the disagreement surfaced or your team inherits a hidden bet.
• Recency blindness and recency bias, both. The model may miss the last six months (check arXiv for the current year) or overweight a hyped zero-citation preprint. Note date and citation context for load-bearing claims.
• Scope creep into a literature review. The decision context from step 0 is the stop condition: when additional papers stop changing the recommendation, you are done.
• Skipping the practitioner layer. A method can be theoretically sound and practically miserable (compute, brittleness, library quality) — papers will not tell you; issue trackers will.

Restricted-environment variant

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  This workflow involves public literature, so data restrictions rarely bind — the sensitive element is your question revealing strategy. Generalize the framing (drop client names, scale hints, product specifics) and re-specialize conclusions locally.
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  No-browsing environments: run the sweep on an open network from the generalized question; do grounded PDF interrogation locally with downloaded papers and a local model.
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  Keep the verified reading list and brief in your internal knowledge base, not in the assistant's chat history, as the durable artifact.

Success criteria

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  A one-page method brief with a constraint-specific recommendation, produced in a half-day.
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  Every citation verified; the strike-rate from verification is recorded (it calibrates your trust for next time).
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  A teammate can implement or reject the method from the brief without redoing the survey.
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  Six weeks later, nothing in the brief has been discovered to be misattributed — the real test.