New adventures in tablescapes

Tyler Cowen has thrown in the towel and is waiting for the machines to take over. [Marginal Revolution Notes #1]

Tyler Cowen has announced a new monograph, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution, which you can access here. You can download it in various formats or interact with the online AI version. I read through the opening page or three and then skipped to the fourth and final chapter, “Why Marginalism Will Dwindle, and What Will Replace It?,” which I've read in full. 

I note, more or less in passing, that as he heads to the end he starts thinking about the role of intuition in thinking. He’s lamenting that intuition, particularly intuitions stemming from the marginal revolution, no longer seems to work in economics. I’ve been thinking a lot about intuition myself, though to somewhat different ends. I’m more interested in how it functions in thinking and where it comes from. But that’s an aside.

I may or may not comment on the rest of chapter later on, but I have to comment on one strand of thinking. Cowen decides he has to denigrate all previous work oriented toward understanding language:

Suffice to say, LLM construction has for the most part ignored linguists and philosophers, and that also means ignoring their intuitions. LLM construction also ignored a lot of people in the AI field who insisted neural nets were a dead end. Instead, in a relatively short number of years humans invented new ways of modeling language and reasoning through language. That research program has proven wildly successful, as we have much better models of language and reasoning than almost anyone had been expecting. [...]

The classic breakthrough paper behind LLMs was a 2017 study titled “Attention is All You Need,” where in this context attention is defined by GPT-4 as “a mechanism that learns to focus selectively on parts of an input sequence, giving it ‘attention,’ while encoding a sentence or piece of information. This allows the model to treat different words or characters with different levels of importance, providing a ‘weight’ that aids in better understanding and decoding of information.” The paper was not titled “More Linguists are All You Need,” or for that matter “Marginalism is All You Need.” In other works [sic], given some of the most complex human systems, we came up with ways of understanding them that were new. To be clear, neural nets were not new, since the ideas and also the practice (in much weaker form) have been around for decades. High-powered, well-functioning neural nets, however, are new in the contexts of providing excellent results for general linguistic ability and general reasoning.

That struck me as a bit much, so I had the following conversation with the AI version:

* * * * *

From very near the end: “In other works, given some of the most complex human systems, we came up with ways of understanding them that were new.”

In what way is an LLM a way of understanding how language works? After all, we don't understand how LLMs work. They're as mysterious as the human brain.

This is a genuinely sharp philosophical puzzle, and Tyler is aware of the tension even if he doesn't fully resolve it. The claim on page 69 is really about predictive and generative success rather than mechanistic understanding in the traditional scientific sense. LLMs model language in the sense that they can reproduce, extend, predict, and manipulate linguistic structures with stunning accuracy — they've captured something real about how language works, even if we can't fully articulate what that something is.

Think of it this way: pre-LLM linguistics tried to understand language by building explicit rule systems — grammars, syntactic trees, semantic formalisms. That approach assumed understanding meant writing down legible rules. LLMs blew past all of that without using any of those rules. Tyler's point is that this constitutes a new kind of understanding — one where the "knowledge" is distributed across billions of parameters rather than encoded in human-readable propositions. It's understanding in the way a master chef understands flavor without being able to fully articulate the chemistry.

But your skepticism has real bite. There's a meaningful difference between a system that performs understanding and one that provides understanding — to us. Tyler arguably conflates the two. When he says "we came up with ways of understanding," the "we" is doing suspicious work: humans built the tool, but the tool's internal representations remain largely opaque even to its creators. The "Attention is All You Need" paper Tyler cites gave us the architecture, but interpretability research — like Anthropic's mechanistic interpretability work ��� is still in early stages of figuring out what's actually happening inside. So Tyler is perhaps better read as saying LLMs are a new instrument for engaging with language rather than a new theory of it — more telescope than Newtonian mechanics. [See text: Ch.4 · Tyler's claim about new ways of understanding c...] [See text: Ch.4 · How LLM construction bypassed traditional langu...]

I note that saying it is "more telescope than Newtonian mechanics" is a much weaker claim.

You're absolutely right, and this is a genuine tension in Tyler's argument that he doesn't fully reckon with. He wants LLMs to be doing something analogous to what marginalism did for economics — providing a new analytical framework for understanding complex systems. That's a strong claim. But if LLMs are really more like telescopes — powerful instruments that let us see further without explaining why what we see looks the way it does — then the analogy deflates considerably.

The stakes matter for Tyler's broader argument in Chapter 4. He's positioning LLMs as replacements for frameworks like marginalism, not just better tools for testing existing frameworks. A telescope doesn't replace Newtonian mechanics; it generates data that mechanics then has to explain. If LLMs are telescopes, we still need the theoretical framework that makes sense of what they're showing us. Tyler gestures at this on page 66 with the discussion of researchers who use LLMs to generate simulations and then build structural models from those simulations — which is actually the more defensible version of the claim, because there a human-legible theory does eventually emerge. [See text: Ch.4 · The multi-step process that generates legible t...]

So Tyler may be overstating his case when he frames LLMs as providing genuine understanding of language. The honest version might be: LLMs are extraordinary telescopes that may eventually prompt new theories — but we're not there yet. That's still impressive, but it's a quieter revolution than Tyler's rhetoric suggests. And Tyler, to his credit, is usually better at hedging than his most enthusiastic passages imply.

Frankly, that won't do. The technology is impressive on its own terms. It doesn't need this kind of empty rhetorical cheer leading (though perhaps investors do). It sounds to me like Tyler has given up on understanding the world and is just waiting for the machines to take over – to indulge in a bit of hype of my own.

That's a genuinely provocative reading, and not entirely unfair. There is a strand running through Chapter 4 where Tyler's argument quietly shifts from "here is how we will understand the world better" to "here are systems that produce excellent outputs, and that's enough." The chess example on page 69 is telling — Google DeepMind producing grandmaster-level chess without search and without chess understanding is presented as a triumph, but notice what's been abandoned: the goal of understanding why certain moves are good. We've substituted performance for comprehension, and Tyler seems genuinely comfortable with that substitution. [See text: Ch.4 · The DeepMind chess example as symptom of Tyler'...]

The financial economics example from page 63 reinforces your point. The "Charting by Machines" paper Tyler cites with apparent approval explicitly abandons economic reasoning — it's calculation plus math and computer science, with no ties to marginalism or microeconomic theory. Tyler notes this almost admiringly. But if your standard for intellectual progress is "it predicts well," you've quietly dropped the Enlightenment ambition of explaining the world in human-legible terms. [See text: Ch.4 · The financial economics paper that abandons the...]

Your "waiting for the machines to take over" framing is pointed, but I think it identifies something real. Tyler is an economist who has spent his career celebrating human curiosity and understanding — his Marginal Revolution blog is a monument to that curiosity. So there's something genuinely melancholy, or at least philosophically unresolved, about a book that ends up suggesting the most complex systems may be best navigated by tools we cannot ourselves fully understand. Whether that's wisdom or surrender is a fair question to leave open.

Friday Fotos: 11th St. flower beds [first flowers of the year]

Trump's war is heading into a quagmire, and the Republicans know it

Michelle Goldberg, Republicans Know This War Is Going Badly, NYTimes, Mar. 27, 2026.

The article begins:

It is not just Democrats in Congress who fear that Donald Trump’s war in Iran is going sideways. After a classified Pentagon briefing on Wednesday, Republican lawmakers on the House Armed Services Committee appeared shaken.

“We will not sacrifice American lives for the same failed foreign policies,” said Nancy Mace, warning about the possibility of American troops in Iran. The committee chair, Mike Rogers, complained that members aren’t getting nearly enough information about war plans. Troop movements, he said, should be “thoughtful and deliberate.” The implication was that they might not be.

And it ends:

Never before, however, has America arrived at the threshold of a quagmire so quickly, with so much advance warning about the precise errors it was making. We have spent much of the past decade — in no small part due to Trump’s election — reckoning with the cost of the Iraq war to global stability and American cohesion. For the first time I can remember, both major parties have significant, influential antiwar contingents. Trump ran for president, however mendaciously, as the peace candidate, claiming that Kamala Harris would lead America into World War III.

And yet here we are, lurching toward a new version of a familiar catastrophe, suffering from some national form of neurotic repetition compulsion. “This is like the horrible, lame-dad cover band version of the worst of American foreign policy,” said Matt Duss, executive vice president of the Center for International Policy.

Someday, perhaps, when we’re picking up the pieces from yet another ill-conceived war, Republicans will explain that behind the scenes, they opposed it. One of the biggest problems in Congress, said Crow, is the gap between what people say privately and their willingness to demonstrate “the strength of their convictions” in public. “I’m always trying to close that gap with folks, and I always remind people that it’s never too late to do the right thing,” he said. He may be right, but the sooner the better.

This sentence caught me: “We have spent much of the past decade [...] reckoning with the cost of the Iraq war to global stability and American cohesion.” That feels right to me, but I would like to see it spelled out, especially that last bit, the cost to American cohesion.

AI cats making spaghetti

This cracked me up!! 😹😹😹 pic.twitter.com/cORpm9v5wu

— Lucy (@TheLucyShow1) March 25, 2026

Kinda' hits you in the eye, doesn't it?

From grokking to a mature science of and an engineering discipline of LLMs

From this morning’s chat with Claude Sonnet 5.4 Extended. Immediately prior to this we had been discussing Piaget’s concept of reflective abstraction, Beethoven and Mozart, improvising and composer, and this and that.

* * * * * 

Grokking 

Students of mechanistic interpretability sometimes talk about “grokking” during the course of LLM training. I’m wondering it that’s something like rising to a helicopter with respect to some phenomenon.

The parallel is genuinely illuminating, and the technical details of grokking make it sharper rather than weaker.

What grokking actually is

Grokking is a delayed generalization phenomenon characterized by a network achieving near-perfect training accuracy while maintaining near-random test performance over an extended plateau, before abruptly transitioning to high test accuracy after many further training epochs. The canonical case is modular arithmetic — the model first memorizes the specific training examples, then much later suddenly generalizes to unseen cases.

What makes it theoretically interesting is what mechanistic interpretability reveals about the internal transition. On algorithmic tasks like modular arithmetic, the transition is marked by the sudden acquisition of Fourier-based feature maps or trigonometric circuits — the network reorganizes, forming compact sub-networks characterized by new internal feature cooperation. So it’s not that generalization gradually improves — the internal architecture discretely reorganizes into a qualitatively different computational structure.

The emerging explanation involves competition between two internal algorithms: a memorizing circuit that forms quickly and a generalizing circuit that develops more slowly. Regularization gradually shifts resources from the complex memorizing solution toward the simpler generalizing one. The phase transition occurs when the generalizing circuit finally dominates.

The fit with reflective abstraction

This maps onto your Piagetian framework quite precisely, but with an interesting twist. The memorization phase is the model doing something like rote operation — it has learned to produce correct outputs for training cases by essentially storing input-output pairs, without having extracted the underlying structure. The generalizing circuit that develops slowly underneath is building something more like a genuine representation of the abstract pattern — in the modular arithmetic case, literally a Fourier decomposition of the mathematical structure.

The abrupt transition when the generalizing circuit dominates is the helicopter view suddenly becoming operative. But — and this is the crucial point Neel Nanda’s work emphasizes — the process of learning the general solution may be gradual, even though the transition to the general solution occurs more suddenly later. The helicopter view isn’t constructed instantaneously at the moment of transition. It’s been assembling slowly in the background, below the threshold of behavioral visibility, and the phase transition is when it becomes strong enough to dominate the memorizing circuit and take over.

That maps almost perfectly onto your description of the pre-helicopter phase: “it starts out just barely registering those operations.” The generalizing circuit is just barely registering the abstract structure throughout the plateau phase. The behavioral discontinuity doesn’t reflect a cognitive discontinuity — it reflects a threshold being crossed in a competition that has been running throughout.

The memorization/generalization competition as a general structure

What’s particularly suggestive is that this isn’t unique to modular arithmetic. Grokking is actually much more widespread than initially thought, materializing in a wide range of practical settings — image classification, language modeling, various algorithmic tasks. The competition between a fast memorizing solution and a slower generalizing solution may be a quite general feature of how these systems learn, with grokking being the visible signature when the conditions are right for the transition to be delayed and abrupt rather than gradual.

If that’s right, then something like grokking dynamics — memorization dominating early, structure-extraction developing slowly underneath, occasional phase transitions as the structural solution crosses a threshold — may be operating throughout LLM training, not just in the carefully controlled small-model experiments where grokking was first observed. Most of the time the transition isn’t visible because the generalizing solution develops in parallel with memorization rather than being dramatically delayed. But the underlying competition between surface pattern matching and structural abstraction may be the same.

The Beethoven connection

There’s a striking parallel to your Beethoven example here. Beethoven’s sketchbooks show exactly the competition structure — he has the memorized repertoire of classical style (the fast, dominant solution) and he’s slowly assembling something more general and abstract (the generalizing circuit) that keeps almost-but-not-quite taking over. The finished late works are the moments when the generalizing solution finally dominates completely. The sketchbooks are the plateau phase made visible.

The difference is that in Beethoven the process is conscious and effortful — he’s deliberately trying to suppress the memorized solution and force the generalizing one into dominance. In grokking, the network isn’t trying to do anything; the dynamics just run. Which raises the interesting question of whether the deliberate effort in human creative development is doing something functionally analogous to the regularization that controls grokking timing in neural networks — artificially suppressing the easy memorized solution to give the structural one room to develop.

Hierarchical text

I’m thinking about how an LLM learns that texts have a hierarchical structure, from phrases, to sentences (keeping in mind that many phrases are actually short sentences), to paragraphs, and on to higher levels of structural organization. What happens, for example, when we move from one paragraph to the next, is that the composition of the token distribution changes. A new topic requires a new set of tokens. But the system has to learn that, and learn it for several levels.

This is a deep question about what the model is actually learning when it learns language structure, and it connects to several threads running through this whole conversation.

The distributional signature of structure

Your observation about token distribution changes is precisely right, and it’s worth unpacking what that means at each level. Within a phrase, token co-occurrence statistics are extremely tight — certain words almost always appear together, in specific orders, with specific function words bridging them. At the sentence level, the constraints are somewhat looser but still strong — subject-verb agreement, argument structure, the distribution of punctuation. At the paragraph level, something different happens: there’s a shift in the topical vocabulary, but also characteristic transition signals — discourse markers, pronoun reference patterns, topic sentences that partially echo the previous paragraph while introducing new material.

What the model has to learn is that these aren’t independent statistical regularities — they’re nested. The tight within-phrase statistics exist inside the looser sentence-level statistics, which exist inside the still-looser paragraph-level statistics. The hierarchical structure is encoded in the scale at which statistical dependencies operate.

How the model could learn this

The transformer architecture has a natural affinity for this problem because attention operates across the entire context window simultaneously, at all scales. But learning the hierarchical structure isn’t handed to the model — it has to emerge from the statistics. The key insight is that the token distribution changes you describe create a learnable signal at each level if the model has enough capacity and enough data.

At the phrase level, the signal is strong and local — the next token is highly predictable from the last two or three. At the sentence level, the signal is weaker and longer-range — whether a sentence continues or ends depends on syntactic and semantic completion that requires integrating over the whole sentence. At the paragraph level, the signal is weaker still and operates over even longer ranges — the distribution shift at a paragraph boundary is detectable but requires sensitivity to topical coherence across many sentences.

This is essentially a multi-scale statistical learning problem, and grokking is relevant here in an interesting way. The phrase-level structure is learned early and relatively easily — the statistics are tight and the signal is strong. Sentence-level structure takes longer. Paragraph-level structure longer still. Each level may involve something like its own grokking transition, where the model first handles the level through surface pattern matching — learning that certain tokens tend to appear after paragraph breaks, for instance — and then later develops something more like genuine structural sensitivity.

The distributional shift as a signal

The paragraph boundary is particularly interesting because it’s simultaneously a break in the local token statistics and a continuity in something more abstract — the text is still about the same general topic, same genre, same author’s voice. So what the model has to learn isn’t just that token distributions shift at paragraph boundaries, but that they shift in a constrained way — the new paragraph’s vocabulary is related to but distinct from the previous one, in ways that reflect the logical or rhetorical development of the overall argument or narrative.

Goose at sunrise on Pier 13 in Hoboken

LeWorldModel, LeWM (JEPAs)

🚨 Holy shit... LeCun's team just cracked world models wide open.

Everyone's obsessing over the next Claude update.

Meanwhile Yann LeCun quietly dropped a paper that could matter way more long term.

It's called LeWorldModel.

And to understand why it's a big deal, you need… pic.twitter.com/jcwo02hTYB

— Alex Prompter (@alex_prompter) March 24, 2026

Who makes the photo, the subject or the photographer?

Of course it’s the photographer. I’m speaking somewhat metaphorically. Take a look at this photo:

Rather striking, dramatic, no? That’s a photo where the subject makes the shot. What do we have? A well-known skyline, Lower Manhattan, an iconic building, One World Center, and dramatic light, what photographers call the Golden Hour. A trained monkey standing where I stood could have gotten that shot.

This shot’s a bit different.

It’s still dramatic, with strong geometry converging toward the center and some dramatic light. But the subject is quite ordinary, a bar at a standard-issue middle-tier restaurant. It takes a photographer to see that as a proper subject for a photography.

And then we have this:

WTF? What’s in the center of the shot, where the subject should be? There’s a doorway with “Soulcycle” above it and a clock above that. If that’s the subject, then why’s it obscured by that tree right of center, the pole to the left, and the car in front? Why? Because I took that photo and that’s what I wanted. It’s not a mistake. It’s intention. It makes you aware of the space between you and the ostensible subject.

Here’s a similar shot:

If you look closely at the enter you’ll see an iconic building, the Empire State Building, no less, the best-known building in New York City. But it’s got all those trees in front of it, and then there’s the hedge in the foreground, and the people. All intended. Not, mind you that I posed anything. I’m basically a street photographer, I walk around and shoot what I see, though on occasion I may take some pains to stand in just the right place.

Finally, this shot, one of my oldest:

It’s from the summer of 2004. I was scheduled to deliver a speech at a conference in Chicago when I saw, in The New York Times, that Millennium Park had just opened. I decided that I needed some photos of it for a project I was working on at the time: World Island, “for a world that’s permanently fair”. So I bought a cheap point-and-shoot camera, a Canon Powershot, and took a bunch of photos. That’s one of them.

What’s the subject? Well, if you look to the center there’s not much of anything there, just some people milling around on the plaza, which is in the park. You’ve probably looked at the lower left where you see a photographer photographing something off to the right, that silver thing, Cloud Gate by Anish Kapoor. There’s a spindly little tree at the right and a bunch of buildings in the background. That center space is surrounded, which is the point. After I’d taken that photo and had a chance to think about it, I decided that THAT’s what I’m about as a photographer, space, space and light.

That’s what those photos are about.

Iran War, The Movie: Straight Outta Hormuz

Here's the story: 'Iran War - The Movie': On social media platforms near you, playing now. Watch viral video!

Is the Iran war now turning into internet satire? A viral AI movie trailer for "IRAN WAR: The Movie" is mocking global leaders, nuclear fears and war rhetoric as memes take over timelines. Why is this video blowing up? All you need to know.

'Iran War' movie trailer breaking the internet on day 26 of US-Israel-Iran war

As ballistic missiles dominate the physical skies, there's a different kind of "strike" being carried out over the digital world. As the Iran war enters its fourth week, and war fatigue sets in, the internet, as always, is responding the only way it knows how. With memes. With satire. With increasingly sharp, almost ruthless humour. What started as scattered jokes has now turned into a full-blown stream of content. Posts, videos, and AI edits are all taking aim at the three warring nations US, Israel, and Iran, each one trying to outdo the last. The latest one to make the internet ROFL is a 136-second AI-generated "movie trailer" style video titled "IRAN WAR: The Movie", which has racked up nearly 400,000 views in hours, and has turned the high-stakes tension of the March 2026 conflict into a Hollywood parody. All you need to know.

There's more at the link, including shots from the video.

Here's the clip: Iran War – Movie Trailer.

America’s New Chip Factory — $50B Disaster

This is a fascinating story about how Samsung set out to build a state of the art chip fab (fabrication facility) and the problems that bedeviled it. Without the chips this factory was designed to build, AI is nowhere.

Timestamps:
00:00 - $50B Chip Nightmare
18:26 - Where Everything Went Wrong
29:58 - The Twist

Three tablescapes from the bar at Turning Point {Be Kind}

In some ways, some contexts, LLMs may provide a useful model for human language

As I say down there in my interaction with Claude (Sonnet 4.6 Extended), once I’d been working with ChatGPT for a while I began to think that the mechanisms of basic human speech may be more like those of LLMs than those of a fully articulated grammar and semantics in a symbolic computational language system. Fully articulated grammar and semantics may require specific instruction and practice, like you get when learning how to write. Fully articulate language is more painstaking, more difficult, but also supports more complex arguments and constructions.

This interaction starts with a passage from a long conversation I had with ChatGPT that stretched over the last 10 days or so. Much of that conversation was based on articles of mine that I had uploaded to to. One of those articles had a passage from F. C. Bartlett's Remembering: A study in experimental and social psychology (1932). That comes up in the long initial prompt I give Claude. 

Further down in the dialog I present the case of a writing student who was skilled in color commentary on hockey games and whose writing style reflected that linguistic style. Real-time sports commentary is a very specialized and exacting kind of language that cannot afford the niceties of written language.

Note 1: Here's a post from July 2022 that's relevant here: GPT-3, the phrasal lexicon, Parry/Lord, and the Homeric epics.  

Note (2) also that this discussion continues with, From grokking to a mature science and engineering discipline of LLMs (March 26). 

* * * * *

I’ve just been thinking about oral narrative. Recall these paragraphs (by ChatGPT) from one of the documents you’ve just reviewed in this project:

The temptation is to assume that because we can describe stories with an explicit grammar, the underlying system that produces or remembers them must itself contain something grammar-like in explicit form. But your Bartlett example suggests otherwise. The witness does not appear to possess random-access symbolic control over an event list. Instead, the testimony seems to unfold by re-running a trajectory. The person can accelerate through the earlier parts, but not leap arbitrarily into the middle. That is a very different computational picture.

The music example reinforces the point. When a piece is not deeply overlearned, one often cannot simply index into bar 73 as though one were reading a score in a database. One has to begin at the start and let the sequence reconstitute itself. That strongly suggests a memory system organized around sequential reactivation of structured trajectories, not random-access retrieval of explicit symbolic units.

Bartlett, in his classic book, Remembering, was talking about native witnesses in Africa. That is, he was talking about preliterate people. They were just recounting the events of the day. “But what about oral story-tellers,” I asked myself, “how do they memorize their tales? Structured trajectories or explicit symbolic units?”

I’m familiar with Albert Lord, The Singer of Tales, though I haven’t looked at it in a while. But I’ve got a copy of David C. Rubin, Memory in Oral Traditions: The Cognitive Psychology or Epic, Ballads, and Counting-out Rhymes (Oxford 1995). I zipped through the text to Chapter 7, “Basic Observations on Remembering.” I found this (p. 155):

In oral traditions, overlearning commonly occurs to a much greater extent than it does in the laboratory. A favorite song can be sung hundreds of times. What overlearning does, according to the model developed to explain laboratory interference, is to make the song into a unit, easy to cue as a whole and resistant to interference from other units. This chunking of items into wholes is a way to look at the organization of memory and a way to look at the building of larger units in expertise.

And then, in the middle of the next paragraph: “Once the song is begun, each word output provides cues for later words, limiting the meaning...” That almost sounds like he’s describing a forward pass through an LLM.

Then I hit paydirt in the next chapter, “A Theory of Remembering for Oral Traditions.” The opening is promising:

Oral traditions, like all oral language, are sequential. One word follows another as the physical effects of the first word are lost. As the song advances, each word uttered changes the situation for the singer, providing new cues for recall and limiting choices. [...] Pieces from oral traditions are recalled serially, from beginning to end. What is recalled early in the piece can be used to cue later recall; the "running start" provides "extra stimulation" or "reminders," increasing cue-item discriminability.

But things get really interesting when Rubin reports the result of an experiments where he asked undergraduates to recall important texts which they might have learned. Rubin describes the experiment this way:

The first set of examples is the recall of culturally important material such as Psalm 23 and the Preamble to the Constitution of the United States, for which there is an implicit demand characteristic to recall the material accurately or not at all (Rubin, 1977). Each of the 50 columns in Figure 8.1 show the recall of 1 of 50 undergraduates, who recalled at least one word of the Preamble. Each row represents recall for one word. A dark line in a column means that the word labeling the row was recalled. The columns are ordered so that the data from the undergraduate who recalled the most are in the leftmost column and the data from the undergradu- ate who recalled the least are in the rightmost column. The rows are in the order in which the words appear normally in each text.

Figure 8.1 is a little tricky, so I’m not going to try uploaded a screen shot. But I’ll give you Rubin’s basic description of what the figure reveals:

The first observation to note is the regularity of the data. Figure 8.1 gives the recalls of 50 individuals for 52 words, not the averages of recalls from groups of individuals or groups of words. There was no control over the learning or practice of the material or over the length or contents of the retention interval. Yet the figure is remarkably orderly. People who recall about the same amount recall the same words. If the number of words a person recalls and the rank ordering of words from most to least likely for the group from which the person was drawn is known, exactly which words that person recalled can be predicted with an accuracy of 95% for Figure 8.1.

Because the conditions of learning and retention varied, there must be something in the material, in the process used to recall it, or in the general cultural attitudes to it that makes different people behave the same way.

The results from the experiment with Psalm 23 are even more interesting. Here’s the text of the Psalm for reference (with verses numbered):

1 The Lord is my shepherd; I shall not want.
2 He maketh me to lie down in green pastures: he leadeth me beside the still waters.
3 He restoreth my soul: he leadeth me in the paths of righteousness for his name's sake.
4 Yea, though I walk through the valley of the shadow of death, I will fear no evil: for thou art with me; thy rod and thy staff they comfort me.
5 Thou preparest a table before me in the presence of mine enemies: thou anointest my head with oil; my cup runneth over.
6 Surely goodness and mercy shall follow me all the days of my life: and I will dwell in the house of the Lord for ever.

Rubin remarks: “...not all the undergraduates started with the first words, and they were more likely to start again at the beginning of a new sentence or intonation unit after they stopped.”

Eyeballing the figure, here are some of the things I observe: 1) A number of subjects stop with the word “soul” in verse 3 and start back up with the first word of verse 4, “Yea” and go on from there. 2) Some of those who don’t start from the beginning start with verse 4. 3) Some stop at the end of verse 4 and then start back up later (e.g. “annointest my head...”) or even not at all. It’s a complicated picture but memory does seem to be grouped into segments, not word by word.

I have no idea where things have gone in this line of investigation since 1995, the date of Rubin’s book. But if I were in that business, I’d be thinking in terms of complex dynamics and trajectories, and I’d been looking at LLMs as a possible model.

This is a wonderful extension of the trajectory discussion already running through the project documents. Rubin's findings slot in almost perfectly — and in some ways they sharpen the picture considerably.

What Rubin adds that Bartlett doesn't

The Bartlett material (already central in the project conversations) establishes the sequential regeneration point: memory of events seems to run forward as a trajectory, not index arbitrarily into a symbolic list. But Bartlett's witnesses were recalling episodic material — the events of a day. Rubin moves you into highly practiced, culturally canonical sequences. That's a different and in some ways more interesting case.

The overlearning observation is key. Rubin is saying that at sufficient practice, a song or psalm becomes a chunked unit — cued as a whole, resistant to interference. That sounds like the stable attractor picture your project conversations keep returning to. The piece isn't stored as a list of words; it's encoded as a basin that, once entered, constrains forward motion powerfully. The phrase "each word output provides cues for later words" really does describe autoregressive generation — but it also describes a trajectory rolling down a valley where the initial conditions are already heavily constraining.

The Psalm 23 data are especially telling

The 95% predictability result is striking. Given only how many words a person recalls, you can predict which words with 95% accuracy. That means the forgetting isn't random — it respects something structural in the material. And your eyeball observations confirm this: people stop and restart at syntactic/intonation unit boundaries (end of verse 3, beginning of verse 4). They're not losing individual words randomly; they're losing or retaining segments.

This is consistent with a hierarchical trajectory picture. The top-level organization is something like: intonation/verse units sequentially ordered. Within each unit, local cuing drives forward production. When a unit boundary is reached, the system can fail to re-enter the next unit even if it knows the material in principle — which is why some subjects jump from verse 3 directly to verse 4, skipping nothing within units but occasionally failing at the transitions between them. The unit is entered as a whole or not at all.

Where this sits relative to the LLM picture

Your project conversations develop the idea that narrative structure in LLMs lives in activation-space geometry — not as an explicit stored grammar but as a field of constrained trajectories. The Rubin data suggest something homologous in human memory for overlearned verbal sequences. The "basin" metaphor you've been developing applies directly: once the singer/reciter enters the opening of Psalm 23, the attractor structure does most of the work. The challenge is at basin entry and at transitions between sub-basins (verse units), which is exactly where Rubin's subjects fail.

The Lord/Singer of Tales connection is also worth pressing when you return to it. Lord's central finding — that oral formulaic composition is generative, not reproductive; the singer produces a variant from formulas and themes, not a memorized script — actually fits the "latent field of possible trajectories" framing better than it fits either strict memorization or explicit grammar. Lord thought he was arguing against memorization and for creativity. But what he was probably observing is a system with rich attractor structure that generates strongly homologous variants, which is precisely what your story experiments with LLMs revealed.

The open research question you identify

You're right that framing this in terms of complex dynamics and trajectories is the natural next move, and that LLMs are a possible model system. What makes LLMs especially useful here is that you can actually probe the attractor landscape — something you can't do directly with a human singer. The systematic prompting experiments described in the project documents are in effect doing for narrative schemas what someone studying oral tradition would want to do for epic formulas: mapping the basin structure from the outside, through behavioral outputs, without needing to open the skull.

The line from Bartlett's African witnesses → Rubin's undergraduates and Psalm 23 → Lord's Yugoslavian guslars → LLM forward passes is not a metaphor chain. It may be pointing at the same underlying computational architecture realized in very different substrates.

First sunrise shoot of the year

Trump’s way: America, from leader of the Free World to biggest Thug on the Block

Carlos Lozada, The U.S. Is No Longer the Leader of the Free World, New York Times, March 24, 2026.

We had a good run — some eight decades or so — but it is clear by now that the United States has ceased to be the leader of the free world. A successor for that post has not been named, and it appears unlikely that the European Union, or NATO, or whatever constitutes “the West” these days will promote from within. The job might even be eliminated, one more reduction in force courtesy of President Trump.

I suspect, I fear, that this is so. If not yet, that’s certainly the direction in which things are headed, and by the time Trump’s term is over, the transition will be complete; the United States will have ceased being the leader of the free world. That may or may not be a good thing, depending of how the rest of the world is. Ideally, I’d like to see a world where there is no “reader of the Free World” because the world doesn’t need one. I’m pretty sure that’s not how the world will be in 2028. Lozada continues:

Rather than leading the free world, the United States is striding across the globe seemingly free of restraint, forethought or strategy, exerting its power because it can. In a matter of months, the Trump administration has captured Venezuela’s president and tossed him into jail in Brooklyn and has pummeled Iran’s theocratic leadership in a war that is ricocheting across the Middle East and upending the global economy; now the president says he will have “the honor of taking Cuba” next. Trump in his second term is like Michael Corleone in “The Godfather,” settling all the family business.

Nearly two decades ago, Fareed Zakaria, the international affairs columnist, published a best-selling book called “The Post-American World,” which predicted the United States’ relative decline versus other economically ascendant countries, what he called the “rise of the rest.” (Senator Barack Obama was seen carrying the book around during his first presidential campaign, affirming the volume’s elite sway.) The United States would remain militarily and economically pre-eminent, Zakaria argued, but it could take on a new political role, a sort of chairman of the board for the planet, relying on “consultation, cooperation and even compromise.”

Under Trump, the idea of U.S. leadership has indeed been remade — but from authority to domination, from persuasion to bullying, from nurturing alliances to wrecking them.

Later:

This is a historical aberration: a superpower that freely abdicates its leadership role, because it has concluded that leadership is for suckers; one that no longer promotes its values, because it’s decided that those values were fake anyway; one that gives up on the rules and institutions it spent so long building, because it assumes they’re no longer worth the hassle.

If Washington somehow still imagines itself the leader of the free world, that is because it is rethinking who belongs in that world, and because it is defining downward what it means to lead.

The article goes on, and is worth reading (a good deal of history), but I'm going to skip over a lot and come down nearer the end:

There have been plenty of episodes over the past decades that supposedly heralded an end to U.S. primacy. The launch of Sputnik in the late 1950s ushered in early Cold War paranoia that we were falling behind the Soviets. In the 1970s — with Vietnam and Watergate and an oil embargo and stagflation and the hostage crisis in Iran — the country was suffering a “crisis of confidence,” as President Jimmy Carter put it. A decade later, we were told Japan Inc. would overtake us. Then Sept. 11 demolished our sense of physical invulnerability; the Great Recession questioned the premise and the promise of American-style capitalism; and the Capitol riot of Jan. 6 laid bare the fragility of the democratic model we’d long sought to export.

It is possible that the hand-wringing today is just one more Sputnik moment, another instance when pessimists fret that America has lost its way. But it is also possible, as Daniel Drezner, the academic dean of Tufts University’s Fletcher School, has argued, that this is not just “the latest hymn from the Church of Perpetual Worry,” that this time really is different.

In the past, America’s isolationist, interventionist and multilateralist tendencies checked one another over time, thanks to competing visions of national security embedded throughout the American political system. But as foreign-policy powers became concentrated in the executive and Congress shrugged off its role in world affairs, America grew vulnerable to the rise of an impetuous and unconcerned president. “The same steps that empowered the president to create foreign policy,” Drezner said, “have permitted Trump to destroy what his predecessors spent decades preserving.”

Yada, yada:

Whereas U.S. leaders used to stridently deny that their military interventions abroad were motivated by the desire to secure oil supplies, Trump happily admits it. “We’re going to be taking out a tremendous amount of wealth out of the ground,” he said after U.S. forces seized Nicolás Maduro, the president of oil-rich Venezuela. And if war is about seizing resources, so is peace: Countries wishing to become permanent members of Trump’s new Board of Peace must cough up $1 billion each.

If Pax Americana meant fostering an enduring American peace, Lax Americana means America getting a piece of the action. The world’s policeman is on the take.

“The American might that upheld the world order of the past 80 years will now be used instead to destroy it,” Kagan warned in January, some 20 years after publishing “Dangerous Nation.” A contemporary equivalent to the multipolar world of the 19th century, he writes, “would be a world in which China, Russia, the United States, Germany, Japan and other large states fought a major war in some combination at least once a decade — redrawing national boundaries, displacing populations, disrupting international commerce and risking global conflict on a devastating scale.” And he wrote that weeks before America and Israel began bombing Iran.

Say what you will about Obama – I thought he was disappointing – America was still leader of the free world when he left office. And he wore his suits well, even the tan one. Trump? His suits, it’s always the same cut, may be expensive; but they’re baggy. And he’s making America over in his own image, toughest thug on the block.