Five interrelated cleanups:
1. Lead -> Phenomenon provenance
- Phenomenon.from_lead_id field on the dataclass
- BaseAgent.run(lead_id=...) writes self._current_lead_id
- _add_phenomenon auto-injects from agent state (LLM unaware)
- Orchestrator dispatch passes lead.id; Phase 1/2-auto/4/5 stay None
- Merge path preserves the first non-None lead_id on collision
2. Unified Phenomenon <-> Hypothesis link path
- HypothesisAgent only adds hypotheses, never links
- link_phenomenon_to_hypothesis tool + executor removed
- All links go through Orchestrator._judge_new_phenomena
- Phase 2 unconditionally judges after hypothesis generation
- Gap Analysis judges after each dispatch round
(Three previously-missing judge calls now in place.)
3. SSOT in agent subclasses
- Remove RoleTemplate dataclass, ROLE_TEMPLATES dict,
_instantiate_from_template method
- Each agent subclass owns name, role, and tool list
- agent_factory.py shrinks from 299 to 153 lines
- All 7 agents now route through _AGENT_CLASSES (filesystem,
registry, communication, network, timeline were previously dead
subclasses overridden by templates)
4. Configurable edge weights
- HYPOTHESIS_EDGE_WEIGHTS -> _DEFAULT_EDGE_WEIGHTS (private default)
- EvidenceGraph(edge_weights=...) override via config.yaml
- hypothesis_edge_weights section in config.yaml (commented example)
- main.py and regenerate_report.py read and pass through
5. regenerate_report.py auto-picks the latest run/*/graph_state.json
when no CLI arg is given (was a hardcoded date path)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
