Hypothesis Generation Loop

A multi-agent, literature-grounded generation loop. The artifact is a growing pool of hypotheses; the feedback signal is the count of strong, distinct hypotheses that clear the bar — where "strong" is decided against real retrieved literature, not assertion. Each round: generate → ground → judge → keep → mutate toward the gaps, until the pool stops growing (saturation).

The discipline: a hypothesis enters the pool only if the literature says it is not already established (novelty), prior work makes it plausible (grounding), and a feasible test exists. Generating is not confirming — the output is a ranked set of strong candidates to test, each stated with how to test it.

The cast (all in roles/):

• roles/Generator.md — proposes a batch of candidate hypotheses aimed at the open gaps.
• roles/LiteratureScout.md — grounds each candidate in real literature (novelty · support · gap); emits litscout.json (validates schemas/litscout.schema.json).
• roles/Judge.md — scores each against the fixed rubric and decides keep/kill/dedupe; emits verdict.json (validates schemas/verdict.schema.json).