Chain of Thought Enhancement

Potential for models to modify programs mid-execution similar to 'aha moments' observed in chain-of-thought reasoning, enabling on-the-fly debugging.

Integrating LLMs with real-time execution environments could mirror the "aha moments" observed in chain-of-thought reasoning, allowing models to debug and refine their logic mid-process rather than relying on static code generation. Commenters suggest that for a system to truly internalize the nature of computation, it should utilize near-zero-latency engines like WebAssembly or Elixir to minimize the overhead of external tool calls. This seamless integration of code and thought suggests a potential "x factor" in reasoning, where models might leverage internal computation to solve complex problems and shatter existing benchmarks in entirely unexpected ways. Ultimately, the discussion highlights a tension between compiling programs directly into transformer weights and optimizing the specialized tools that allow an LLM to "think" through execution.

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The key difference is that the model is able to write the program as it’s executing it.

Before it needs to write the code and have an external program execute it. Here it can change its mind mid execution. Kinda like what was observed in the CoT’s ah ha moment

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I spent the entire time reading it pondering the same thing.

1. The article presents that calling out to a tool like python is "expensive" because of the overhead of forking a process, loading up the python env etc, but why not just eliminate that overhead and embed WebAssembly so this "tool call" is near zero? This feels very similar to the discussion in the 90's around the overhead of threads v.s. processes or kernel space v.s. user space. Could even go further and have a running beam vm so the LLM can write elixir which is ideal for LLM's that stream out code? Elixir programs will be a lot shorter than webassembly.

2. The core argument stated is "A system that cannot compute cannot truly internalize what computation is." The idea being that it could write a program, execute it and by seeing all of the steps maybe even part way through stop and change its mind or when writing new programs write them better, aka be able to debug on the fly?

3. Not mentioned, but there is a 3rd x factor that LLM's will use this new found computation engine to do overall better at "thinking". Computing in very unexpected ways and to unexpected problems. Maybe it would do dramatically better at some benchmark because of this?

Unfortunately these are not explored and it is just an execution engine even resulting in the conclusion stating "arbitrary programs can be compiled directly into the transformer weights, bypassing the need to represent them as token sequences at all." which goes to point number 1 of if we are compiling to weights why not just optimize the tool calling?

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