Elythian Framework targets self-correcting agents

This is a genuinely interesting attempt to turn agent reliability into one optimization target instead of a pile of guardrails and prompt tricks. The real test is whether lowering K improves task success in the world, not just produces a more elegant story about the model's own behavior.

• –Evidence anchoring is the strongest idea here, because it keeps the agent tied to externally verifiable state instead of letting it optimize into a self-consistent but wrong system.
• –The hardware-routing piece is genuinely practical: balancing VRAM, thermal headroom, and task affinity is the sort of control-plane logic local-LLM builders actually need.
• –Persistent memory plus six sub-agents can compound capability fast, but it also raises the odds of stale evidence, hidden coupling, and brittle recovery paths.
• –The "no RLHF, no human in the loop" pitch is bold, but it raises the bar for evaluation, because the only proof is outperforming strong baselines on real tasks.
• –Keeping the framework LLM-agnostic is smart, because it makes the consistency objective portable across backends instead of binding the idea to one model family.