SymbolicAI: A neuro-symbolic perspective on LLMs

I spent some time toying around with LLM-guided "symbolic regression", basically having an LLM review documents in order to come up with primitives (aka operators) that could be fed into github.com/MilesCranmer/PySR

I didn't get very far because I had difficulty piping it all together, but with something like this I might give it another go. Cool stuff.

Oh, definitely. I recommend you go for contracts. I've used something similar for a contract that iteratively "stitched together" a broken ontology graph. Here's some of the data models for inspiration -- you could have something similar for your ops, and write the contract to solve for one op, then apply the op, etc.

---

    class Merge(LLMDataModel):
        indexes: list[int] = Field(description="The indices of the clusters that are being merged.")
        relations: list[SubClassRelation] = Field(
            description="A list of superclass-subclass relations chosen from the existing two clusters in such a way that they merge."
        )
    
        @field_validator("indexes")
        @classmethod
        def is_binary(cls, v):
            if len(v) != 2:
                raise ValueError(
                    f"Binary op error: Invalid number of clusters: {len(v)}. The merge operation requires exactly two clusters."
                )
            return v
    
    
    class Bridge(LLMDataModel):
        indexes: list[int] = Field(description="The indices of the clusters that are being bridged.")
        relations: list[SubClassRelation] = Field(
            description="A list of new superclass-subclass relations used to bridge the two clusters from the ontology."
        )
    
        @field_validator("indexes")
        @classmethod
        def is_binary(cls, v):
            if len(v) != 2:
                raise ValueError(
                    f"Binary op error: Invalid number of clusters: {len(v)}. The merge operation requires exactly two clusters."
                )
            return v
    
    
    class Prune(LLMDataModel):
        indexes: list[int] = Field(description="The indices of the clusters that are being pruned.")
        classes: list[str] = Field(description="A list of classes that are being pruned from the ontology.")
    
        @field_validator("indexes")
        @classmethod
        def is_unary(cls, v):
            if len(v) > 1:
                raise ValueError(
                    f"Unary op error: Invalid number of clusters: {len(v)}. The prune operation requires exactly one cluster."
                )
            return v
    
    
    class Operation(LLMDataModel):
        type: Merge | Bridge | Prune = Field(description="The type of operation to perform.")

---