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
its interesting to see many people come to the same neuro-symbolic conclusion around the same time.
this is what the tools response for the mcp server looks like:
{ tools: [ 0: { name: "factor" description: "Factor an integer" inputSchema: { ... } 4 items } ] }
@mcp.tool()
def factor(a: int) -> int:
"""Factor an integer"""
return factor_number(a)
The documentation and source code seem to be:
- (official SDK): https://github.com/modelcontextprotocol/python-sdk/blob/e80c... -> using the function's docstring: https://github.com/modelcontextprotocol/python-sdk/blob/e80c...
- (v2?): https://gofastmcp.com/servers/tools#the-%40tool-decorator and https://github.com/jlowin/fastmcp/blob/998de22a6e76fc8ae323e... -> using the function's docstring: https://github.com/jlowin/fastmcp/blob/998de22a6e76fc8ae323e...
The LLM handles the natural language interaction and orchestration, while the computer algebra system does what it does best ... exact symbolic manipulation.
this smells like claude :D
Great quote.
> So, we’ve come full circle to symbolic AI!
Case in point, Wolfram Alpha is not much more than an attempt to market Mathematica to lazy and failing college students. If that cost, and localization, can be offloaded to LLMs as the universal front end to technical software, it'd free up SWE resources to focus on core functionality.
If Magma, my favorite math/cryptography tool, had an LLM frontend, I could save time wasted onboarding new cryptographers.
It will regularly use it and reliably when asked to.
It's a bit unfortunate that the field is so dominated by extremes of hype and skepticism, both of which aren't particularly helpful to getting problems really solved.
On tensor notation: Tensor indices aren't bad (a good notation should guide a calculation and they do) but I can't help but feel they're far too error prone.
What are the alternatives? Penrose diagrams?
I have never used a symbolic algebra system, but came across a problem where I am trying to model a deterministic simulation system. I can write out the computation graph (~20 million equations for final state), but Sympy chokes on seemingly dozens of symbols. No hope of processing the final state or being able to express a simulation state in terms of my desired input variables.
Not sure if my expectations are mismatched with reality, I am hugely bungling the tool, or Sympy has laughable performance vs the more capable commercial options.
behnamoh•5h ago
pona-a•3h ago