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?
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.