The merge of System 1 (pattern-matching intuition) and System 2 (deterministic logic) reasoning is the holy grail for agent architectures. Current LLMs excel at the first but fail spectacularly at the second when you need mathematical precision.
Embedding actual computation into the transformer weights rather than relying on external tool calls (Python REPL, calculators) could eliminate a lot of brittle handoffs in agent pipelines.
The question is whether this scales to arbitrary computation or if it's limited to specific mathematical domains. If it generalizes, you could build agents that reason AND compute in a single pass without context-switching overhead.
The merge of System 1 (pattern-matching intuition) and System 2 (deterministic logic) reasoning is the holy grail for agent architectures. Current LLMs excel at the first but fail spectacularly at the second when you need mathematical precision.
Embedding actual computation into the transformer weights rather than relying on external tool calls (Python REPL, calculators) could eliminate a lot of brittle handoffs in agent pipelines.
The question is whether this scales to arbitrary computation or if it's limited to specific mathematical domains. If it generalizes, you could build agents that reason AND compute in a single pass without context-switching overhead.