Adventures in Symbolic Algebra with Model Context Protocol

I think this is the proper way to use llms for tasks that require high fidelity. currently im working on binary analysis using llms for natural language and letting ghidra/codeql do the symbolic work. scalability is a massive issue, perhaps the biggest besides fidelity.

its interesting to see many people come to the same neuro-symbolic conclusion around the same time.

Wonderfully cheeky but also helpfully informative writeup. Also appreciate the hat-tip to all the (as yet) unsolved security issues. Clearly MCP is onto something important, although undoubtedly the standard (or some replacement standard) will mature a fair bit before we're done with it. The flip side to that is, MCPs are probably as 'easier' to experiment with now than they are ever going to be.

So, we’ve come full circle to symbolic AI! This article essentially suggests that LLMs could be effective translators of our requests to command-line code or input to symbolic AI software, which would then yield precise solutions. However, I feel this approach is overly mechanical, and I don’t believe AGI would be achieved by creating thousands, if not millions, of MCP servers on our machines. This is especially true because MCP lacks scalability, and anyone who has had to send more than three or four function schemas to a language model knows that excessive JSON schema complexity confuses the model and reduces its performance.

Awesome stuff! We use a similar approach (without MCP) to great effect with Prolog currently and feels like we're only just starting to scratch the surface here.

A great paper from Nasim Borazjanizadeh and Steven Piantadosi at UC Berkeley for those interested: Reliable Reasoning Beyond Natural Language https://arxiv.org/abs/2407.11373

For anyone digging in who wants to hack on this: arun [at] aloe.inc

How does the LLM know that it can use the factor tool to factor integers? Just by looking at the string "factor an integer"?