Co-Scientist

Run the full Co-Scientist pipeline for one research run.

Evaluate whether a hypothesis newly entered the current top-k set and update the convergence counter deterministically.

Assemble a dashboard-ready snapshot from the canonical state.

Choose exactly one concrete evolution strategy for the active evolution round.

Run the assumption decomposition and deep verification review for a hypothesis.

Run the iterative evolution loop from the current persisted run state until convergence.

Generate exactly one child hypothesis that improves logical coherence, causal consistency, and assumption hygiene.

Generate exactly one child hypothesis by combining complementary strengths from multiple parent hypotheses.

Generate exactly one child hypothesis that is more experimentally and operationally feasible than its parent.

Generate exactly one grounded child hypothesis by strengthening evidence, specificity, and literature support.

Generate exactly one cross-parent child hypothesis by transferring a useful principle from one parent context into another.

Generate exactly one divergent but still testable child hypothesis that challenges the shared assumptions of the parent set.

Generate exactly one child hypothesis that preserves the core idea while reducing unnecessary complexity.

Run the full literature-grounded review for a hypothesis.

Generate exactly one hypothesis candidate by enumerating and combining testable assumptions.

Generate exactly one hypothesis candidate through a structured scientific debate.

Generate exactly one literature-grounded hypothesis candidate for the active round.

Dispatch one enabled generation strategy for a given `ResearchPlan`.

Run the initial review gate for a hypothesis.

Evaluate a hypothesis against prior observations.

Judge one placement-tournament matchup between a candidate hypothesis and one opponent.

Update hypothesis proximity state by invoking the canonical embedding bridge for one hypothesis.

Judge one ranked-tournament matchup between two top frontier hypotheses.

Update ranking artifacts for one reviewed hypothesis using canonical placement-opponent selection, ranked-frontier selection, tournament judgments, and Elo updates.

Run the decomposed review pipeline for a single hypothesis and persist each review stage as a structured artifact.

Summarize the completed reviews for a hypothesis.

Simulate the hypothesis mechanism and identify failure scenarios.

Extract recurring critique patterns from the completed review of a hypothesis.

Select the next island strategy and parent hypothesis set for one evolution round.

Run the canonical literature search bridge for one evidence query and persist traceable evidence artifacts.

Apply deterministic Elo updates for one completed tournament batch, persist touched hypotheses, and write the ranking update receipt.

Generate a `ResearchPlan` from `input.md` or equivalent raw research input.

Generate the final research overview from the top-ranked hypotheses.

Interpret the user's high-level research intent into `RUN_POLICY.yaml` and `state/POLICY_DECISION.json`.

Resolve dashboard links for one Co-Scientist run from Codex.

Diagnose the local Co-Scientist host-agent environment from Codex.

Show the supported Co-Scientist start parameters for Codex users.

Resume an interrupted Co-Scientist run from Codex.

Bootstrap an existing Co-Scientist run directory from Codex.

Start one Co-Scientist run from Codex using a research goal or imported brief.