hyperresearch-3-contradiction-graph

name

hyperresearch-3-contradiction-graph

description

Step 3 of the hyperresearch V8 pipeline. Builds an explicit graph of opposing claims across the corpus. Ranked fight clusters feed step 4's loci analysis so that loci emerge from where evidence actually forks, not from agent intuition. Also identifies consensus claims (3+ independent agreements) for confident assertion in the draft. Invoked via Skill tool from the entry skill after step 2 completes.

Step 3 — Contradiction graph

Tier gate: SKIP for light. Run for full.

Goal: before loci analysis, build an explicit graph of opposing claims. Loci should emerge from where the evidence actually forks, not from agent intuition about what seems interesting.

---

Recover state

Read these inputs:

• research/scaffold.md — vault_tag
• research/prompt-decomposition.json — atomic items, pipeline_tier
• All research/temp/claims-*.json files (one per fetched note)

If no claims files exist (e.g., fetchers didn't produce them), skip this step entirely — the next step (loci analysis) falls back to corpus prose-scanning.

---

Procedure

1. Load all claims from research/temp/claims-*.json files.

2. Pair contradictions. For each claim, find claims from OTHER sources that contradict it. Match on:

   • Same stance_target with opposing stance (supports vs. refutes)
   • Same entities with opposite conclusions
   • Same scope but different numbers (e.g., "market grew 15%" vs. "market shrank 3%")
   • Overlapping scope_conditions but different evidence_type pointing different directions

3. Cluster contradiction pairs into fights. Group related pairs into clusters — each cluster is one contested question:

   {
     "cluster_id": "short-slug",
     "fight": "one-sentence description of what's contested",
     "side_a": {"position": "...", "claims": ["claim-text-1"], "sources": ["note-id-1"]},
     "side_b": {"position": "...", "claims": ["claim-text-1"], "sources": ["note-id-1"]},
     "evidence_quality_delta": "which side has stronger evidence types (empirical > theoretical > anecdotal)",
     "scope_overlap": "genuine disagreement, or scoped differently and both right?",
     "decision_relevance": "high|medium|low — does resolving this matter for the research_query"
   }
   

4. Rank clusters by decision_relevance (high first), then by evidence_quality_delta (tighter fights rank higher).

5. Write research/temp/contradiction-graph.json — array of ranked fight clusters.

6. Identify consensus claims. Claims where 3+ INDEPENDENT sources (after redundancy audit if step 2.6 ran) agree. Write these to research/temp/consensus-claims.json. These are the "settled ground" the draft can assert confidently without hedging.

---

Exit criterion

• research/temp/contradiction-graph.json exists (may be empty array if corpus is univocal)
• research/temp/consensus-claims.json exists (may be empty array)

---

Next step

Return to the entry skill (hyperresearch). Tier-based routing:

• full tier: Invoke Skill(skill: "hyperresearch-4-loci-analysis")