I don't understand why this continues to be a concern.    It is only of 
academic interest, it seems to me, to wonder how good native LLM reasoning is.
Yes, there is a small cost to dispatch from the LLM for MCP.  Tokens need to be 
generated, and tokens need to be absorbed.  But for anything that is deep 
reasoning it is a vanishingly small overhead.   It is exactly what 
computational scientists would do too.   They'd reach for their Matlab or at 
least a chalkboard.   Let the LLM write the Lean 4, the Answer Set Programming, 
the Mathematica, the Matlab, the Magma, whatever.  If they do a bad job, there 
will be correction cycles, if they do a good job, there won't.   But frontier 
models are good coders now.

-----Original Message-----
From: Friam <[email protected]> On Behalf Of glen
Sent: Thursday, May 21, 2026 8:13 AM
To: [email protected]
Subject: Re: [FRIAM] More meat for the metavores

What is "reasoning" if not a sequence of tokens, the latter depending on the 
former in some way? I'd like to offer up 3 links that might help us understand 
where the "reasoning" of LLMs is only kindasorta reasoning:

• https://logicalintelligence.com/kona-ebms-energy-based-modelshttps://github.com/SkyworkAI/Matrix-Game/tree/main/Matrix-Game-2https://github.com/facebookresearch/vjepa2

The 1st one isn't quite like the other 2. But it's in the same vein, I think. 
There's some kind of something to be said about cumulative puzzles or 
meta-games. But I don't know quite what I'm trying to say. Although I loathe 
the term, Systemic Games <https://the-artifice.com/systemic-games-philosophy/> 
comes to mind.

Reasoning engines have (at least) 2 modes, maybe akin to Kahneman's systems 
1&2, where some input simply clicks or doesn't and is tossed away, but other 
inputs *modify* the lattice ... change the game. I say "at least" because 
there's a distinction between something like self-modifying code - where an 
execution can modify, add, or delete axioms or even the language - and 
"emergent play" where nothing fundamental changes, but one plays games atop or 
within the base game. So I guess there are at least 3 modes.

All 3 are appropriately called "reasoning". But along with the gist of 
Hullman's post, failing to distinguish them is lazy. But we need generalized, 
non-jargonal nicknames for them, otherwise every mention requires a detailed 
glossary ... or perhaps an entire, pickled runtimeworldclosure, attached to 
every message passed.

On 5/14/26 8:54 PM, Roger Critchlow wrote:
> https://statmodeling.stat.columbia.edu/2026/05/14/as/ 
> <https://statmodeling.stat.columbia.edu/2026/05/14/as/>
> 
> "Previously it didn’t feel like such a crime to talk about intelligence or 
> learning in machines because nothing really worked that well, so the labels 
> were clearly aspirational. But now it’s much easier to believe the simulacra. 
> And so it becomes harder to tell when we are using human-oriented terms as a 
> predictive convenience versus a scientific claim versus a marketing device."
> 
> 
> "Too much casualness with words is unscientific. There was no good reason in 
> the first place to call the token sequences a model produces when we ask it 
> to “explain its reasoning” reasoning, other than that’s what we wish we could 
> see."


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