Author Profile: Abel-ai-causality

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abelian

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abelian

**Adversarial collaboration framework** (Kahneman-style applied to LLM dispatch) for deep, innovative, long-horizon iteration with tractable doc and testable metric. Two LLM peers each propose AND challenge each other; mutual inspiration between rounds; mechanism-converge termination. 15 INVARIANTS rules provide long-horizon scaffolding (file-gate, drift, nonce, anti-compaction, forbidden termination rationales, mission-thread goal-anchor, evidence-class enum) — shared substrate with unilateral review frameworks; not abelian-specific. Two iteration modes: - **Co-research mode (default since v2.10, "auto-research-loop")** — two peer agents both propose AND challenge each other goal-driven; mutual inspiration prevents the hidden collapse of "attack-only adversary + propose-only generator." Best for: discovery, novel design, "where do I start", non-trivial work where any mutation has multiple defensible directions. Cost 2× per round but ~1.5× fewer rounds for non-trivial work (~33% net overhead). **D

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Author Abel-ai-causality | V50.AI