Depending on what you mean by "this", I guess. I totally agree that LLMs "reason" ... and it's a bit reactionary to
claim they don't. But my problem is with words like "learn", "train", "inference", "deep", etc.
I forget which talking head said it, but they said something like "I'm not one to
change my mind." LoL Do they even hear their self when they talk? Changing one's
mind is *the* hallmark of intelligence. Defeasible reasoning is the only sign there's
anybody in there. But, of course, there are those amongst us who do all their
mind-evolution almost entirely alone ... like some schizophrenic wunderkind, deep
learning from all their hallucinated voices. I'm told David Lewis (ala possible worlds)
was like this ... speaking in fully formed worldclosureruntimes. But for the rest of us,
more than half of what we know is only available through the APIs of the deeply
interactive objects we have strewn about us.
So even if I don't have a nickname for all the other nuanced concepts, I need some for
"changing one's mind" versus "arbitrary pontification from which nobody learns".
On 5/21/26 8:28 AM, Marcus Daniels wrote:
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-models
• https://github.com/SkyworkAI/Matrix-Game/tree/main/Matrix-Game-2
• https://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."
--
8647 ⊥ ɐןןǝdoɹ ǝ uǝןƃ
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