I built a "Research Paper Writing Army" – 20+ autonomous agents, 6 novelty engines, and 10 adversarial reviewers

I’ve been frustrated by the current state of "AI writing assistants." Most are just fancy wrappers that hallucinate citations and spit out that distinct, flavorless "AI-written" prose. I decided to stop using LLMs as a word processor and start using them as a research lab. I just open-sourced **Sisyphus Academica**, a self-coordinating swarm of 20+ specialized agents designed to produce publication-ready research papers that don't sound (or look) like they were generated by a bot. **The core difference:** Instead of a single "writer" agent, this uses a pipeline-based architecture that forces the system to actually *think* before it writes: * **6 Novelty Engines:** These are the real "moat." They include a *Contrarian* (inverts claims), *Cross-Pollinator* (imports solutions from unrelated fields), and the *Heretic* (generates wild hypotheses to score against actual research). * **The Adversarial Review Board:** Before anything is formatted, the draft is independently evaluated by 10 distinct personas—including the *Skeptic*, the *Methodologist*, and the *Ethicist*. If they don't all pass it, it goes back to the drawing board. * **Zero-Hallucination Citations:** The agent performs a 2-source verification check for every reference. If it can’t be verified, it’s stripped out. * **Humanizer Integration:** It applies 41 specific "humanizer" patterns from the token level to bypass AI-pattern detection. It even enforces stylistic constraints, like zero em-dashes and specific voice calibration based on your own writing samples. **The Tech Stack:** * Orchestration: OpenCode + OhMyOpenAgent * Writing Engine: A 5-agent swarm tuned with humanizer patterns * Tech: Python, LaTeX-ready, modular agent architecture This isn't about faster writing; it's about better research architecture. I’ve built this to actually find the "gaps" in the literature that human researchers often miss due to domain bias. I’m looking for feedback from the agent-dev community. If you end up running a paper through the pipeline, I’d love to know how the "Heretic" engine performed for your specific domain.