experiment-running
Execute the plan by dispatching fresh subagents per task, monitoring status, and collecting results
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Execute the plan by dispatching fresh subagents per task, monitoring status, and collecting results
Dispatch execution subagent — select model by complexity, construct prompt with full task context
Identify which specific comparison pairs are most responsible for preference cycles and inconsistencies.
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Campaign: Multi-agent structured debate for adversarial validation. Core question: Can this artifact survive structured adversarial debate? Methods: Irving AI Safety via Debate, Du Society of Mind, Liang MAD, Toulmin Argumentation, D3 framework.
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the backprop attribution is a judgment call.
Build a detailed adversarial persona with background, motivation, expertise, blind spots, and preferred attack patterns.
Incorporate a new judgment into the rating model and return updated ratings for all candidates.
Decide whether to continue iterating or stop based on consensus score, round number, and stability.
Strategy: Multi-agent collaborative debate based on Du et al. Society of Mind. Agents share perspectives iteratively until convergence or divergence is detected.