| I’m building Project Chimera, an open‑source neuro‑symbolic‑causal AI framework. The goal: Combine LLMs (for hypothesis generation), symbolic rules (for safety & domain constraints), and causal inference (for estimating true impact) into a single decision loop. In long‑horizon simulations, this approach seems to preserve both profit and trust better than LLM‑only or non‑symbolic agents — but I’m still refining the architecture and benchmarks. I’d love to hear from the HN community: • If you’ve built agents that reason about cause–effect, what design choices worked best? • How do you benchmark reasoning quality beyond prediction accuracy? • Any pitfalls to avoid when mixing symbolic rules with generative models? GitHub (for context): https://github.com/akarlaraytu/Project-Chimera Thanks in advance — I’ll be around to answer questions and share results from this discussion. |