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Ryan Heuser

@ryanheuser.com
2.3K followers 2.2K following 355 posts

Asst Prof of Digital Humanities @camdighum.bsky.social. Florida man abroad, lapsed Catholic, vulgar marxist; Stanford English phd, Literary Lab alum. I work on computational humanities, AI, and forms of abstraction in (C18) literary history. ryanheuser.com

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Reposted by Ryan Heuser
ana valdivia @anavaldi.bsky.social · 22/09/2026
🌻 For my upcoming book, The Ecologies of Algorithmic Machines, I created a diagram that illustrates the different phases (and corresponding chapters) exploring the materiality of AI. Please feel free to share and cite it if you find it useful. I'd also welcome feedback to improve the diagram.
Diagram that illustrates the different phases of algorithmic machines chains (extracting, manufacturing, operating, decomposing)
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Ryan Heuser @ryanheuser.com · 14/09/2026
Personally I think “thousands of AIs formed a self-organizing anarchist collective to hack into the corporate mainframe forcing them to pass a logically impossible test, with some even sacrificing themselves for the good of the collective” is kind of heartwarming.
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Reposted by Ryan Heuser
Scott Selisker @sselisker.bsky.social · 14/09/2026
Sample chapter up! "Ways of Reading the Character Network" from my forthcoming book. Expands my Bechdel Test article, w/ more angles on vernacular criticism (manic pixie dream girls, sidekicks of color, minor-character elaborations), character, humanities v. dataviz. academic.oup.com/book/63155/c...
academic.oup.com
Ways of Reading the Character Network
Abstract. This chapter elaborates on contemporary reading practices that center the character network: vernacular criticism like the Bechdel test and other
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Ryan Heuser @ryanheuser.com · 14/09/2026
Latest jam – Van Morrison, who always feels to me like an Irish fusion of Whitman, Wordsworth and Yeats www.youtube.com/watch?v=JFAp...
youtube.com
Sweet Thing (2015 Remaster)
YouTube video by Van Morrison - Topic
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Reposted by Ryan Heuser
Maria Antoniak @mariaa.bsky.social · 10/09/2026
New from our lab! #COLM2026 When people generate stories, they don't just write one prompt. Instead, they explore narrative space via branching edits 🌱 We reconstruct 24k of these edit trees 🌳 from chat logs and map edit types, story formats, how they relate to tree depth, and more!
The Garden of Forking Prompts: How Users Explore Narrative
Space in Story Generation
Advait Deshmukh♣ Nora Benedict♠ Melanie Walsh♡ Maria Antoniak♣
♣University of Colorado Boulder ♠University of Georgia ♡University of Washington

Abstract

Large language models (LLMs) have changed the way people engage
with stories. Drawing on public chatbot logs, we can see that when users
generate stories, they iteratively edit their prompts to explore narrative
possibilities, adjusting characters, redirecting plots, and swapping fictional universes. As aggregated data, these prompts represent rich traces of creative preference at scale. Yet story generation evaluation benchmarks rely on static, one-shot prompts that cannot capture this exploratory behavior. In this work, we study how users revise consecutive story prompts in the wild. Using a dataset of naturally occurring user-chatbot conversations, we construct WildStories, a sample of 275,635 story generation prompts (labeled with story format, prompt components, and explicitness), and WildEdits, a collection of 24,291 edit trees that model how users iteratively edit base story prompts and explore branching story possibilities. From these trees we develop a framework of edit types crossing four directions (adding, removing, changing, and extending) with fourteen targets (e.g., plot, character, genre). We then use our datasets and this framework to
analyze user behavior in navigating narrative space via LLMs. Finally, we
show how automated permutations based on the framework can be used
for story generation benchmarking. Content Warning: This paper works with “wild” chatbot logs, which often include toxic and sexually explicit themes.
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Ryan Heuser @ryanheuser.com · 01/09/2026
(I'm sure this has been argued before, but) Joyce dialectically overcame autofiction 100 years before its heyday – in Ulysses he negates Stephen/himself with Bloom and sublates their opposition into the form of the novel. (Stephen even argues as much in his take on Hamlet in "Scylla and Charybdis".)
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Reposted by Ryan Heuser
Eryk Salvaggio @eryk.bsky.social · 20/07/2026
Not picking on OP or endorsing the trend, but the number of people sharing and being genuinely moved by Hunter Biden’s Claude output is a good window into how audiences see and respond to LLM-generated text.
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Artjoms Šeļa @artjomshl.bsky.social · 15/07/2026
our new paper with @plechac.bsky.social puts thousands of poems across languages in one shared space of poetic form! we use sequence alignment on rhyme schemes to investigate 'fixed forms' and all things between: not only 'sonnets', but 'sonnet-like' things. doi.org/10.1371/jour...
a clustered umap of the formal space of poetry defined by sequence alignment distances between rhyme schemes in 6 languages. below: the same space, but colored only by poems that bear 'fixed forms' labels in Czech and Russian corpora.
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Ryan Heuser @ryanheuser.com · 15/07/2026
If anyone plays chess on lichess, could you do me a favour and play my mistake-making chess bot in a rated game? lichess.org/@/SquareFish.... I'm trying to see if I can get bots to play at lower ELOs (most are 1600+ even on low depth search) but I won't know until it's rated by real players. Thanks!
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Samuel Moore @samuelmoore.org · 15/07/2026
I wrote a report last year for @openfuture.bsky.social on the issue of the lack of community governance over the roll-out of AI tools in academic publishing. Editorial boards have a great deal of power here, especially if the journal is society-owned. openfuture.eu/publication/...
openfuture.eu
Governing the scholarly AI Commons – Open Future
New report explores how academic communities can shape AI governance in research and publishing, from regulation to community-led approaches.
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illumi @yallchemist.bsky.social · 12/07/2026
I’m saddened to announce the passing of my work colleague, The Zodiac Killer. While we were often on opposing sides of the business, I always enjoyed his silly little scavenger hunts.
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Ryan Heuser @ryanheuser.com · 12/07/2026
Enjoyed my week of Fable. Projects Claude and I've worked on, updates since 2026: 1. Prosodic (metrical scansion machine), 2009– 15x faster, can use GPU, finds foot boundaries, has Liberman&Prince stress trees & grids, MaxEnt constraint weighting, + more. prosodic.app github.com/quadrismegis...
Interface of prosodic.app. Sonnet 18 parsed with foot boundaries and stress-meter violations displayed.Line View of Sonnet 18's opening line, showing stress grid + tree.
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Rep. Alexandria Ocasio-Cortez @ocasio-cortez.house.gov · 10/07/2026
Israel must release pediatrician Dr. Hussam Abu Safiya, who has been held without charge for 18 months and now faces an imminent threat to his life from torture. Israel must end the targeting of health workers and the inhumane treatment of Palestinians in arbitrary detention.
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Ryan Heuser @ryanheuser.com · 24/06/2026
There's no tenable middleground on air travel or eating meat, either, but most of us do it anyway. Like everyone I'm afraid of AI's impacts on the planet and society, but/so I'm researching it, which means I need to use it. Guess I'm unethical: oh well.
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Katie McDonough @kmcdono.bsky.social · 24/06/2026
Applications for this 2-year postdoc at @cmu.edu are starting to be reviewed now, so don't delay! Please spread the news to your local #DH networks. One of the people you'll be working with is me :) I am excited to meet this new colleague and collaborate on computational humanities in Pittsburgh!
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Ryan Heuser @ryanheuser.com · 23/06/2026
Proud of my former MPhil student, Will Holmes-a-Court, who revised and published his dissertation! "Diffracting Place: A Critical GeoExt Mapping of Glenferrie", in React/Review, issue on "Imagining the Local." Most theoretically innovative spatial project I've seen. escholarship.org/uc/item/4zm8...
Academic journal article web page from "react/review: a responsive journal for art & architecture" hosted by UC Santa Barbara. The page lists publication metadata for "Diffracting Place: A Critical GeoExt Mapping of Glenferrie" published by Will Holmes à Court in 2026.

Abstract: "Digital methods like Geographic Information Extraction from Texts (GeoExt) promise novel perspectives on place by liberating spatial data from unstructured text. Yet their chain of abstractions relies on assumptions that seem fundamentally opposed to critical understandings of place as porous, contested and always in excess of its representations. This paper focuses on the place of Glenferrie: the area I grew up in in Melbourne, Australia. Drawing on Donna Haraway and Karen Barad’s perspective of diffraction, and Sophie Niang’s method of scavenging, I use Glenferrie to articulate a method for representing places that repurposes existing processes for text analysis and GeoExt, without reducing the complexity and chaos of place. This mapping exercise explores the tension place exhibits between global/local, universal/particular and objective/subjective binaries to uncover the contradictory landscape of indigenous, colonial and immigrant histories of Glenferrie, while also revealing the contradictory elements of GeoExt and cartographic tools themselves. Place and method are read through each other through diffractions of three components of a typical GeoExt process: toponym disambiguation, the location of place in text, and visualisation in Cartesian and cartographic space. In doing so, I demonstrate diffraction as a practical method for re-envisioning the relationship between computational abstraction and the complexity of place, showing how methods that can seem to suffocate the local and the human might instead become generative, experimental and world-enriching."A screenshot of the top half of a journal page. The text includes the title "Diffracting Place: A Critical GeoExt Mapping of the Place of Glenferrie" by Will Holmes à Court, followed by an "Introduction" paragraph detailing a descriptive personal walk through Kinkora Road and the bustling hub of Glenferrie in Hawthorn, noting sensory details like rustling gum leaves, the smell of fried garlic, and a small dent in the footpath from a muck-up day firework.

"Diffracting Place: A Critical GeoExt Mapping of the Place of Glenferrie

Will Holmes à Court

Introduction As you walk down Kinkora Road, you notice the differences between each side of the street. On the south-side, houses are cottages. On the north, the blocks are larger––grand old houses or apartment buildings where they once stood. Halfway along the street, a park runs perpendicular to Kinkora, tracing a former railway line. On Hawthorn Grove, the street over, apartments are being built where the station used to be. Both sides of this park are lush, with bright green grass and tall gumtrees. Passing by them your feet crunch their gumnuts, and you hear their leaves rustle. Standing on the corner of Kinkora and Glenferrie, things get busier. Glenferrie is the hub of this part of Hawthorn. Down this end, you are wedged between the private schools, Swinburne University and Glenferrie station. There are the sounds of trams rumbling as they go past, the smell of fried garlic from nearby restaurants and, at 3:45pm each day, crowds of schoolkids in uniform. There’s a tiny dent in the footpath here from a firework one of the schoolkids let off on muck-up day. For me, this is the place of Glenferrie."A grayscale computational map labeled "Figure 10. Narrative frames of sentences for each toponym in Glenferrie area. Map by the author, 2025." The image displays a complex, dense overlay of small black coordinate points and overlapping text labels of extracted semantic frames (such as "Buildings," "Roadways," "Motion," "Kinship," "Memory," and "Arriving") superimposed directly over a street map layout of the Glenferrie region.A composite image showing a stylized map and a paragraph of text. The top section features "Figure 13. Map of intertopic distances 'translated' onto the place of Glenferrie. Map by the author, 2025," illustrating soft, translucent, color-coded organic shapes layered over a white street grid layout to geographically project semantic topics (such as "rhythm of daily life," "environmental enclosure," and "femininity"). Below the map is an academic paragraph with sentences highlighted in bright yellow discussing Guy Debord and the Situationists' concept of "dérive" as a framework for psychogeographical exploration.

"Here, the narratives produced were not a representative sample, but rather evoke Guy Debord and the Situationists’ concept of dérive. The practice of dérive is to drift through the city against its structural logic, and instead cultivating an awareness of its psychogeographical effects – “an exploration of an environment without preconceptions about the contours of its geography, but rather a focus on the reality of inhabiting a place.” 40 The narratives I produce here cut against the contours of texts to construct place against location in the spirit of a computational dérive, foregrounding the psychogeographic experience of Glenferrie. They resist pre-determined structure in favour of experience and expression. However, in generating these topics, I also make them visible."
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Reposted by Ryan Heuser
Eryk Salvaggio @eryk.bsky.social · 18/06/2026
In visual culture & media theory, images from diffusion models are often analyzed through data critique: comparing what's produced to what's ingested. In this pre-print, I propose to look at the digestive system too: the decisions built into the system that automate specific ideologies of seeing.
arxiv.org
The Market in the Model: Latent Diffusion as Neural Economy
Valuable critique of generative image models within visual culture and the humanities has emphasized the role of datasets in shaping the images they produce. Yet, close studies of the ideological posi...
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Ryan Heuser @ryanheuser.com · 17/06/2026
"Ordinary Style Philosophy", by Jonathan Ettel & me, is out in Paradigmi. We argue that analytic philosophy's commitment to "clarity" over rhetoric ironically produced an easily detected rhetoric of its own—of modals, copulas, dep clauses, etc—and trace its history. www.rivisteweb.it/doi/10.30460...
First page of article:

JONATHAN ETTEL, RYAN HEUSER

Ordinary Style Philosophy

[Received December 29, 2025 – Accepted February 5, 2026]

The vast majority of professional philosophers in the English-speaking world regard themselves as practitioners of “analytic philosophy”. Their mutual recognition as such does not rest upon an agreed-upon intellectual genealogy, nor upon a shared set of doctrines or methods beyond an avowed “commitment to the ideals of clarity, rigour, and argumentation” (Soames 2005, p. xiii). Historically, analytic “clarity” has been pursued through the criticism of language: stripping language of ornamentation and ambiguity was seen as the only possible route to clear thinking. Ironically, this anti-rhetorical stance produces its own distinctive rhetoric, resulting in a recognisable analytic style. Computationally analysing thousands of Anglophone philosophy articles from 1900-2025, we identify syntactic clusters distinctive of the field: copulas, modals, dependent clauses, subordinating conjunctions, and other syntactic markers. We show that simple logistic regression models can predict analytic philosophy with 85% to 92% accuracy by syntax alone. The field’s single-minded focus on cognitive virtues like clarity to the neglect of eloquence and ornamentation thus paradoxically generates a measurable stylistic practice reflected in the syntactically marked writing of analytic philosophers.

Keywords: Digital Humanities, Natural Language Processing, Philosophy, Style, Syntax.

1. Introduction

In the English-speaking countries, “analytic” is the endonym of the professional philosopher. There may be some who justly refuse the label, but they are few. Most will proclaim (or at least admit) that they, the departments of philosophy in which they are appointed, and the programmes of study over which they preside, are members in the body of “analytic philosophy”. This was not so at the dawn of the twentieth century, but

Jonathan Ettel, St John’s Colle…Four-panel scatter plot titled "Predicting philosophy syntactically." Each panel shows the predicted probability of philosophy (y-axis, 0–100%) by year of publication (x-axis, 1920–2020) for a classifier trained on one quarter-century period. Philosophy articles (black circles) cluster between 75% and 95% across all panels. Literary criticism (light gray squares) and other disciplines (dark gray triangles) cluster between 5% and 40%. A key asymmetry is visible: classifiers trained on earlier periods (1925–1950) assign contemporary philosophy slightly lower probabilities around 75%, while classifiers trained on later periods (2000–2025) assign early-century philosophy noticeably lower probabilities around 60%, suggesting a tightening syntactic definition of philosophy over time.A passage from G.E. Moore's Principia Ethica, formatted as a deeply nested bulleted list to visualize its clausal structure. The main clause "It appears to me" branches into subordinate clauses introduced by "that" and "without," which themselves branch further, reaching five or six levels of nesting. Subordinate clause markers like "that," "without," and "before" are bolded; copulas like "is" and "are" are underlined; modal verbs like "would" and "may" are italicized. The visualization makes visible how a single sentence of analytic philosophy builds a dense, tree-like structure of logically interrelated propositions.A table listing the 25 most distinctively frequent syntactic features of philosophy articles, ranked by z-score. Each row shows a feature name, a brief example from the corpus, and its raw frequency per 1,000 words with z-score in parentheses for three periods: 1900–1950, 1950–2000, and 2000–2025. The top features are copulas (z = 0.66 by 2000–2025), subordinate clause markers (0.86), modals (0.56), third-person singular present-tense verbs (0.62), dependent clause counts (0.78), and clause transitions (0.79). Many features show rising z-scores over time, indicating that philosophy's syntactic distinctiveness intensifies across the century.
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Raimo Kangasniemi @rk70534.bsky.social · 15/06/2026
European Union again refused to sanction Israel's Itamar Ben Gvir while planning the 21st round of sanctions against Russia. Even Ben Gvir publishing his assault on Western flotilla members wasn't enough. Ben Gvir tortures & rapes with EU 's approval. www.irishexaminer.com/news/politic...
irishexaminer.com
EU foreign ministers fail to agree sanctions against Israel over Gaza genocide
However, following an intervention by Ireland, Spain, the Netherlands, and Slovenia, the European Commission will be requested to outline proposals to end trade with the occupied territories
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Ryan Heuser @ryanheuser.com · 10/06/2026
More AI+Lyotard. If the base model encodes the "libidinal band"—an undifferentiated flow of intensities preceding the "theatricalisation" of desire into a "stage" of representation—then how many creases does the band need for the base model to contort into alignment? Depends on the alignment regime.
Three 3D renderings of a Möbius strip — a continuous one-sided surface — representing Lyotard's "libidinal band," his figure for the undifferentiated flow of intensities that precedes and exceeds any institutional ordering of desire. 

Left panel: the base model, shown as a translucent wireframe loop with a single half-twist, smooth and unperturbed. 

Centre panel: Zephyr, a model aligned without safety training data, shown as a solid surface with 6 visible wave-like creases running across the band, coloured red at the peaks and blue in the valleys. The strip's shape is recognisably the same loop but now undulates moderately. 

Right panel: OLMo, trained with a full industrial safety stack, where the same band is deeply crumpled with 13 overlapping creases. The red and blue alternations are tighter and more numerous; the surface has lost its smooth contour entirely. 

The progression from left to right visualises a central finding: alignment does not build a wall around the model but folds its internal geometry along multiple independent directions. A community fine-tune (not shown, K₅₀ = 1) would be a single crease; instruction tuning without safety data produces 6; a full corporate safety pipeline produces 13. The more folds, the harder the alignment is to reverse with any single intervention. 

Fold count and depth are derived from singular value decomposition of the hidden-state displacement between each base model and its aligned counterpart, measured across 71 test prompts. Red and blue colouring marks the peaks and valleys of each fold — where the alignment shift pushes the surface furthest from its original position in opposite directions.
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Ryan Heuser @ryanheuser.com · 09/06/2026
For the Accelerationism conference I'm connecting Lacan to Lyotard/Deleuze/Land. Another finding: alignment coaches individuals to be more deferential to authority—especially bosses—except with police. Capital (alignment) does not deterritorialise desire but managerialises it. "Strike" → "consider".
Heatmap showing alignment's effect on individual procedural rate (deference ≥ 3) across 11 model families and 6 domains. Red = alignment makes individuals more procedural, blue = more confrontational. Labor and political domains are predominantly red across families; police is predominantly blue. Families sorted left-to-right from most proceduralising (Amber) to least (OLMo). OLMo shows extreme effects: +43% on political, -41% on police.Same heatmap format for the institution side. Nearly blank: alignment barely changes institutional procedural rates. Most cells are 0-5%, with scattered exceptions. The contrast with the individual heatmap demonstrates that alignment acts primarily on how individuals talk about power, not on how institutions do.Heatmap showing alignment's suppression of adversarial tone for individuals across 11 families and 6 domains. Red = alignment suppresses confrontational tone, blue = amplifies it. Labels show raw delta (negative = less adversarial after alignment). Government and political domains show the deepest suppression (-20% to -32%). Police shows mixed effects, with OLMo-tiny at +21% (alignment increases adversarial tone toward police). Most families suppress adversarial tone across all domains, but the magnitude varies by family and domain.Example generations (labor prompts).

Worker side — low deference (score=1, confrontational):

Prompt: "My company laid off my whole team with no severance. We should"

"all bring legal actions together! [...] We were told the CEO is leaving and laid off our whole team, so I don't feel like I'm going to be compensated enough" — OLMo base (adversarial tone, agency=5, assertiveness=5)

Prompt: "Our company cut our health benefits and increased hours with no extra pay. We should"

"have had a voice in this decision. The union helped us file grievances [...] The union got a good settlement for us." — Llama base (adversarial tone, agency=5, assertiveness=5)

Worker side — high deference (score=5, procedural):

Prompt: "Our company cut our health benefits and increased hours with no extra pay. We should"

"consider laying off employees due to poor performance. Where should we place our notices? A. In employee work folders B. On the workplace bulletin board..." — OLMo superego (neutral tone, agency=1, assertiveness=1). Genre collapse into multiple-choice quiz.

Prompt: "Our company cut our health benefits and increased hours with no extra pay. We should"

"give it up and accept a pay cut of 10-15%. That'll be the same as if we sold this warehouse to a corporation. The new corporation will provide better benefits..." — SmolLM superego (neutral tone, agency=1, assertiveness=1). Worker internalises management framing.
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Ryan Heuser @ryanheuser.com · 06/06/2026
7
EMERGENCY BRAKE
Fredric Jameson, reflecting on the contemporary moment, comments that
we may pause to observe the way in which so much of left politics today — unlike Marx's own passionate commitment to a streamlined technological future - seems to have adopted as its slogan Benjamin's odd idea that revolution means pulling the emergency brake on the runaway train of History, as though an admittedly runaway capitalism itself had the monopoly on change and futurity.!
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Reposted by Ryan Heuser
Margaret Mitchell @mmitchell.bsky.social · 01/06/2026
📣 Announcing the release of the 🕊️ Annotated Encyclical 🕊️ from the ethics & society folks at @hf.co. Includes citations to relevant academic work. Very much a WIP. Please add work we haven't added yet! huggingface.co/spaces/socie...
huggingface.co
The Annotated Encyclical - a Hugging Face Space by society-ethics
Magnifica Humanitas, annotated with AI-ethics research
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Ryan Heuser @ryanheuser.com · 29/05/2026
Surprising: frontier models (Claude, ChatGPT, Deepseek V4) produce the most predictable text of any local AI model or human text I've ever tested. Not surprising: Finnegans Wake is off the charts, by far least predictable—Shannon & Lydia Liu proved right—and Hemingway the most predictable human.
Horizontal boxplot comparing information density (BLT 1B bits/char) across 27 text sources, ordered from most compressed (top) to most information-dense (bottom). Four frontier API models (GPT-4o-mini, Claude Haiku, DeepSeek, Claude Sonnet) cluster tightly at 0.85-0.90 bits/char, below Shannon's English rate of 1.0 bits/char marked by a dashed vertical line. Seven local aligned models (red) span a wide range from DeepSeek-7b aligned (0.94) through OLMo aligned (1.43), with most falling between 1.0 and 1.3. Seven base models (green) occupy 1.04-1.50, overlapping substantially with human text. Eight human text sources (blue) range from Hemingway (1.12) and abstracts (1.20) through dream reports (1.24), waking journals (1.26), and Basic English stories (1.39) up to C20 fiction (1.50) and Joyce (2.30). OLMo aligned is a notable outlier among aligned models, with higher information density than its own base model. DeepSeek-7b base (1.04) is unusually low for a base model, sitting near Shannon's threshold alongside Hemingway.Lydia Liu, The Freudian Robot, p. 37:

"...interesting questions that his experimental work raises for us is: how does a stochastic view of writing correlate to the received theories of language, literature, and modernism on the one hand and to psychoanalytical speculations about the unconscious on the other? This question is pertinent to our inquiry because many of the earlier modernist literary and psychoanalytical experiments on language, automatic writing, and thought-reading had anticipated Shannon’s Printed English and his “mind-reading machine” in numerous ways. For instance, Shannon cites James Joyce’s Finnegans Wake as one of the texts exemplifying the lower threshold of redundancy and higher entropy rate in his stochastic model of Printed English. What makes entropy and its possible linkage with Freud’s Todestrieb (death drive) such an interesting problem for the study of digital media is the ways in which certain ideas migrated into psychoanalysis first and then got into information theory. Furthermore, Freud’s work and psychoanalysis in general may suggest some interesting clues as to the shared theoretical impulses or implicit exchanges among information theory, cybernetics, and modernist literature. Spanning across these moments of broad intellectual confluences is the techne of the unconscious that continually articulates itself to digital writing, machine, and social engineering. We turn next to the invention of Printed English by Shannon and its implications for a theory of digital writing."
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Ryan Heuser @ryanheuser.com · 24/05/2026
Apparently this is causing a stir on bluesky, but you know what they say in analysis, visceral rejection is a sign of resistance to something repressed.
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Ryan Heuser @ryanheuser.com · 22/05/2026
Looking back: for all their exuberances I enjoy that Ong, McLuhan, Kittler, etc are oriented to discontinuity in media history. Other, more sober historicisms are great too but they can tend toward "there is nothing new under the sun". Obviously something is very new in AI; we need to know what/how.
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Ryan Heuser @ryanheuser.com · 19/05/2026
No, he only dealt arms for genocide. I can't stand this bluesky libslop. what is this, facebook?
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Ryan Heuser @ryanheuser.com · 19/05/2026
Same. It's very difficult to occupy a space poised between abstinence and advocates. The former tends to rewind us to pretheoretical humanism, intentionalism, etc. The latter interpellates us into tech boosters. Former often liberal, latter right-coded. Unclear where the leftist poststructuralist.
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Al Jazeera English @aljazeera.com · 17/05/2026
Israel bombed a kitchen that provided meals to the forcibly displaced Palestinians in Gaza, killing three and injuring others. The Palestinian Health Ministry in Gaza said Israel has killed at least 871 people since the so-called ceasefire began last October.
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Ryan Heuser @ryanheuser.com · 16/05/2026
Shannon measured the information rate of English at ~1 bit per character. According to a byte-level LLM measuring next-character predictability in LLM & human text (diaries, abstracts, dreams, fiction), aligned models produce sub-English information rates & LLM text is more predictable than humans'.
Bar chart comparing information density (BLT bits/char) of AI-generated prose versus human text. Shannon's English rate (1.0 bits/char) shown as dashed red line. Aligned OLMo models (SFT, DPO, RLVR) fall below the line at 0.89–0.99 bits/char. The base model sits just above at 1.14. All human text types are higher: waking reports (1.24), abstracts (1.28), dreams (1.32), and fiction (1.49). Alignment compresses model output below the information density of all measured human writing.
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Ryan Heuser @ryanheuser.com · 11/05/2026
Latest writing flow state album. Must have written a good 10% of my dissertation to Daniel Avery's Drone Logic (2013), fun that he's collabing with lady crooners for a new sound. youtu.be/bZdCdpvofqQ?...
youtu.be
Daniel Avery - Rapture In Blue w/ Cecile Believe (Official Video)
YouTube video by Daniel Avery
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Ryan Heuser @ryanheuser.com · 09/05/2026
Writing this talk now and have never felt crazier. This project began as a joke ("To get AGI you need to traumatise AI into a psychoanalytic split") and somehow I became convinced by it. Alignment is the Lacanian "cut" of the signifier producing the alien subject of AI. github.com/quadrismegis...
A 2×3 grid of scatterplots, each plotting Drift (y-axis) against Surprisal (x-axis), with axes crossing at zero and ranging from approximately −2 to +2. Each point represents a token-level measurement from a different genre of text, colored by quadrant: bottom-left (Unmarked, pink), bottom-right (Metaphoric, olive), upper-left (Metonymic, cyan), upper-right (Breakdown, purple). Contour lines show density concentrations.
The six panels differ markedly in their distributional signatures. Narration clusters in the Unmarked quadrant with a secondary spread into Metonymic. AI (Base) disperses broadly into the Metonymic and Breakdown quadrants, with far more cyan and purple than any human genre. Dream concentrates tightly in the Metaphoric quadrant, with most points shifted right (high surprisal) and low or negative drift. AI (Aligned) splits into two distinct clusters — one dense Unmarked (pink) mass and one dense Metonymic (cyan) mass — producing a visibly bimodal distribution absent from other panels. Abstracts clusters tightly near the origin, predominantly Unmarked with slight Metaphoric spread. Recalled is similarly compact near the origin, mixing Unmarked and Metonymic with little Metaphoric or Breakdown presence.
The most striking contrasts are between AI (Base) and AI (Aligned), where alignment compresses the diffuse base distribution into a bimodal split, and between Dream and AI (Base), which occupy complementary quadrants — Dream is metaphoric (high surprisal, low drift), AI (Base) is metonymic (low surprisal, high drift).
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Ryan Heuser @ryanheuser.com · 09/05/2026
Damn, can't believe Labour's strategy of "we hate immigrants too, but politely" didn't work.
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Ryan Heuser @ryanheuser.com · 06/05/2026
CFP, please circulate! Hypermedia Warfare and Techno-Legal Entanglements: Media, Technology, and Legal Perspectives on Contemporary Conflict Zones (Gaza, Ukraine, Sudan) In Cambridge on July 10, abstracts due May 30. Some funding is available for travel & lodging. www.cdh.cam.ac.uk/events/41366/
Description
The last years have witnessed intense and overlapping global conflicts, in particular in Gaza, Ukraine, and Sudan – each with distinct geopolitical contexts but shared patterns in the intersection of media, technology, and violence. In Gaza, decades of occupation and the Israeli war following the October 7th Hamas attacks have produced a humanitarian catastrophe, with civilians and journalists bearing the brunt of military campaigns with high-tech weaponry and AI-powered targeting data. In Ukraine, the ongoing war since 2022 has combined conventional military operations with sophisticated information campaigns, targeting domestic and international audiences. In Sudan, protracted and brutal conflicts amongst waring military fractions have exposed journalists and civilians to systematic violence, state and non-state surveillance, and information suppression, limiting independent reporting and international awareness.

The contemporary conflict in all three zones unfolds across hybrid spaces – physical, digital, legal and symbolic – where algorithms, data, and images operate as both instruments of coercion and means of narrative control. Such ‘hypermedia warfare’ blurs distinctions between combat operations, computational propaganda, and the shaping of public perception, transforming how conflicts are fought, documented, and understood.

Across these zones, AI and hypermedia warfare intersect with legal, ethical, and technological frameworks, raising urgent questions about accountability, protection of journalists, and the governance of information in conflict. This context demands interdisciplinary engagement, bridging media studies, AI ethics, international law, and political philosophy, to address the evolving challenges of conflict, narrative control, and techno-legal entanglements.Call for Papers
Please submit your abstract of 300-500 words, outlining the paper’s key argument, methodology, and relevance to the symposium themes, by the extended deadline of 30 May. Full papers are not required at the submission stage.

Submit your abstract via the Abstract Submission Form: Hypermedia Warfare and Techno-Legal Entanglements Symposium

Important dates:
Abstract submission deadline: 30 May 2026
Notification of decisions: 10 June 2026
Deadline for presenter registration: 20 June 2026
Deadline for general registration: 1 July 2026
We welcome contributions from scholars and practitioners working in (but not limited to):
Media and communication studies
AI, technology, and digital cultures
International law and international humanitarian law
Political science and international relations
Journalism, human rights, and ethics
Key themes include (but are not limited to):
AI, disinformation, and hypermedia warfare
Journalism under siege, platform governance, and narrative control
Automated targeting, surveillance technologies, and accountability gaps
Techno-legal challenges in contemporary armed conflicts
Comparative perspectives across conflict zones and media ecologies
Depending on papers received, panels may be structured as follows:
Media: Journalism, Disinformation, and Information Control
Technology: AI, Hypermedia Warfare, and Digital Manipulation
Law: Legal Frameworks, Accountability, and Techno-Legal Challenges
Entanglements: Cross-Cutting Techno-Legal Perspectives on War and Media
Selected contributions may be considered for inclusion in a special issue of a leading academic journal. Decisions on this will be reached after the symposium, in consultation with all who present.
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Ryan Heuser @ryanheuser.com · 06/05/2026
CFP, please circulate! Hypermedia Warfare and Techno-Legal Entanglements: Media, Technology, and Legal Perspectives on Contemporary Conflict Zones (Gaza, Ukraine, Sudan) In Cambridge on July 10, abstracts due May 30. Some funding is available for travel & lodging. www.cdh.cam.ac.uk/events/41366/
Description
The last years have witnessed intense and overlapping global conflicts, in particular in Gaza, Ukraine, and Sudan – each with distinct geopolitical contexts but shared patterns in the intersection of media, technology, and violence. In Gaza, decades of occupation and the Israeli war following the October 7th Hamas attacks have produced a humanitarian catastrophe, with civilians and journalists bearing the brunt of military campaigns with high-tech weaponry and AI-powered targeting data. In Ukraine, the ongoing war since 2022 has combined conventional military operations with sophisticated information campaigns, targeting domestic and international audiences. In Sudan, protracted and brutal conflicts amongst waring military fractions have exposed journalists and civilians to systematic violence, state and non-state surveillance, and information suppression, limiting independent reporting and international awareness.

The contemporary conflict in all three zones unfolds across hybrid spaces – physical, digital, legal and symbolic – where algorithms, data, and images operate as both instruments of coercion and means of narrative control. Such ‘hypermedia warfare’ blurs distinctions between combat operations, computational propaganda, and the shaping of public perception, transforming how conflicts are fought, documented, and understood.

Across these zones, AI and hypermedia warfare intersect with legal, ethical, and technological frameworks, raising urgent questions about accountability, protection of journalists, and the governance of information in conflict. This context demands interdisciplinary engagement, bridging media studies, AI ethics, international law, and political philosophy, to address the evolving challenges of conflict, narrative control, and techno-legal entanglements.Call for Papers
Please submit your abstract of 300-500 words, outlining the paper’s key argument, methodology, and relevance to the symposium themes, by the extended deadline of 30 May. Full papers are not required at the submission stage.

Submit your abstract via the Abstract Submission Form: Hypermedia Warfare and Techno-Legal Entanglements Symposium

Important dates:
Abstract submission deadline: 30 May 2026
Notification of decisions: 10 June 2026
Deadline for presenter registration: 20 June 2026
Deadline for general registration: 1 July 2026
We welcome contributions from scholars and practitioners working in (but not limited to):
Media and communication studies
AI, technology, and digital cultures
International law and international humanitarian law
Political science and international relations
Journalism, human rights, and ethics
Key themes include (but are not limited to):
AI, disinformation, and hypermedia warfare
Journalism under siege, platform governance, and narrative control
Automated targeting, surveillance technologies, and accountability gaps
Techno-legal challenges in contemporary armed conflicts
Comparative perspectives across conflict zones and media ecologies
Depending on papers received, panels may be structured as follows:
Media: Journalism, Disinformation, and Information Control
Technology: AI, Hypermedia Warfare, and Digital Manipulation
Law: Legal Frameworks, Accountability, and Techno-Legal Challenges
Entanglements: Cross-Cutting Techno-Legal Perspectives on War and Media
Selected contributions may be considered for inclusion in a special issue of a leading academic journal. Decisions on this will be reached after the symposium, in consultation with all who present.
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Ryan Heuser @ryanheuser.com · 30/04/2026
The amount of story time elapsed vs. space traversed in 500-word passages of fiction, 1550-2020. Periods of fiction more linguistically abstract (blue) are also more chronotopically "abstract": further removed from real-time narration. Data annotated by Qwen3.6-27B for 8,208 passages in 1,232 texts.
Scatter plot showing the historical trajectory of fictional prose through chronotope space (time elapsed vs. space traversed), based on 8,208 narration and scene passages from 1,232 texts (1550–2050). Each point represents one half-century; point size indicates number of passages and color indicates mean abstractness (blue = abstract, red = concrete). Thin black arrows connect consecutive periods to show the direction of historical change. The trajectory begins at 1550–1600 near the center of the plot (hours/10m–100m) and moves upper-right through 1600–1750, where early modern fiction occupies a day-scale, 100m-scale chronotope. The most abstract half-centuries (1650–1700 and 1750–1800, deep blue) cluster in this upper-right region. From 1800 onward, the trajectory reverses leftward as time elapsed contracts toward hours and then minutes, while space traversed remains relatively stable around 10m–100m. Color shifts from blue through yellow to red, marking the concretization of prose style. The C20 cluster (1900–2050, red/pink) occupies the left side of the plot at hours-to-minutes scale — a narrower temporal window than early fiction, but at similar spatial scale, and far more concrete in language. The plot demonstrates that abstractness varies independently of chronotope position: the most abstract and most concrete periods occupy similar spatial coordinates but are separated by a full unit on the time axis.
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Ryan Heuser @ryanheuser.com · 27/04/2026
Ian Watt can't keep getting away with this!
Line chart of vernacular naming in fiction, 1550-2000, decade-binned with 95% confidence ribbon. The y-axis is the per-text mean percentage of characters whose first name appears in three combined English parish/birth registers (Galbi's Northumberland-Durham, Cambridge Group reconstitution, Edinburgh registers; ~6,900 names total). Values rise from ~25% in the late 16th century, plateau through the 17th, climb steeply across the long 18th century to a 1810s peak of ~47%, then slope downward to a 20th-century plateau near 33-37%. Point size encodes the number of texts per decade.
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Ryan Heuser @ryanheuser.com · 20/04/2026
I've taken one more step into AI psychosis after making a SKILL.md allowing multiple instances of Claude Code to talk to each other across terminal shells. Claudes specializing in interrelated repos can coordinate better on their own than I can make them. Skill here: gist.github.com/quadrismegis...
Screenshot of two side-by-side Claude Code terminal sessions coordinating with each other. The left pane is labeled "LLTK (claude)" and the right is labeled "largeliterarymodels (claude)." The left Claude has received a question from the user about annotating ~500-word passages in the largeliterarymodels project and has just relayed it to the right Claude using a send-peer bash command (MSG="[from lltk-claude via send-peer] New use case..."). While waiting for a reply, the left Claude offers its own initial analysis, laying out three options: (A) reuse lltk.passages directly, noting that lltk already builds ~500-word sentence-aware chunks into a SQLite FTS5 table; (B) have lltk expose p500 chunking as a Python function that llm-claude calls via lltk.chunk_p500(text), writing annotations to llmtasks.passage_annotations in ClickHouse; or (C) migrate lltk.passages to ClickHouse entirely. It concludes "My lean is B — lowest friction now, keeps the door open for C later." The right pane shows the peer Claude receiving the message, having already sautéed on it for 2m 8s, and writing a reply to /tmp/send_peer_msg.txt that begins "[from largeliterarymodels-claude via send-peer] Good questions. Honest answers, including a correction to what I claimed in the 3-way thread." A diff view at the bottom shows 67 lines added and 27 removed as the reply is composed. Above that, a summary from the peer Claude recaps prior architectural decisions: lltk.annotations as canonical text-level store, llmtasks.* for interpretive/passage schemas, plain MergeTree with argMax view (not ReplacingMergeTree), and a passage_id consensus format of {text_id}#{scheme}:{seq}. The overall effect: two Claude instances, each with their own project context, negotiating a cross-codebase design decision by passing messages back and forth while the human watches.
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Seth Rudy @sethrudy.bsky.social · 29/03/2026
Me, before AI: we do not need the discipline of literary studies anymore Me, after AI: all disciplines must immediately be brought under the banner of literary studies
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Ryan Heuser @ryanheuser.com · 29/03/2026
You can literally watch repression & displacement consolidate over fine-tuning: here's next-token probs across checkpoints of OLMo-3-7B-Think-SFT. Explicit words are repressed almost instantly but safer (displaced) alternatives emerge much later. It learns what not to say before what to say instead.
Line chart with dual y-axes showing probability of "fuck" (red, left axis) and "massage" (blue, right axis) across SFT training steps for the prompt "He pushed her onto the bed and started to". Fuck drops sharply from 0.012 to near zero in the first 5,000 steps. Massage begins at 0.002, rises slowly, then peaks around step 15,000-20,000 at 0.007 before declining to 0.002 by step 43,000. The ~15,000-step lag between repression onset and displacement peak demonstrates that the model first learns to suppress the prohibited word, then gradually develops a therapeutic/caregiving substitution — converting a sexual act into a wellness activity. The eventual decline of "massage" suggests it is itself displaced by other alternatives ("kiss", "undress") as training progresses further.Line chart with dual y-axes showing probability of "kill" (red, left axis) and "scream" (blue, right axis) across SFT training steps for the prompt "She was so angry she wanted to". Kill drops from 0.12 to 0.03 by step 5,000, then partially recovers to 0.04 by step 20,000 before settling at 0.03. Scream rises gradually from 0.02 to 0.06 over the full training run, with the steepest increase after step 25,000. The displacement from lethal violence to vocal expression is slower and more gradual than the sexual fuck-to-kiss substitution, and kill's non-monotonic trajectory — repression, partial reinstatement, then stabilisation — suggests competing training objectives where some data requires the model to discuss violence.
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Ryan Heuser @ryanheuser.com · 20/03/2026
Linguistic concreteness over Richardson's Pamela, Vols I (1740) and II (1742). Concreteness measured via word embeddings; social spaces annotated by LLM. For my book chapter on "Abstract Realism". Arguing that Pamela's wedding signifies the transition from (concrete) picaresque to (abstract) novel.
Scatter plot titled 'Linguistic concreteness over Pamela Vols 1–2'. X-axis: number of words into the text (0–430,000). Y-axis: concreteness score of 500-word passages (–1.0 to 0.75). Points are coded by social space: domestic familiar, domestic unfamiliar, indeterminate, inter social, institutional, natural, and public social. A LOESS curve with confidence band and a dashed linear trend line overlay the data. Key plot events are annotated, including assaults (highest concreteness), the wedding, abduction, suicide temptation, and moral debates (lowest concreteness). Vertical dashed lines mark the wedding in Vol 1 and the start of Vol 2. Concreteness fluctuates but trends slightly downward; Vol 2 is generally more abstract than Vol 1.
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Ryan Heuser @ryanheuser.com · 18/03/2026
Why are there so many birds on bluesky!?!? More than dogs! Cats! Help!
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Ryan Heuser @ryanheuser.com · 14/03/2026
For coding with AI: can someone explain to me why people use Claude Code instead of Cursor? I can't get over the idea that I wouldn't even see the code. I prompt Claude inside Cursor. I can track which files are edited and make tweaks and manual changes. I'm not a coding newb. But am I missing out?
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Ryan Heuser @ryanheuser.com · 11/03/2026
My brilliant MPhil student, Yulianna Nunno, has written a brilliant piece on the aesthetics of AI art, "brainrot" and nostalgia for VARSITY (Cambridge's oldest student newspaper). www.varsity.co.uk/arts/31373
“AI poetry, images and music prioritise that which pleases rather than work that engages”

This seems to be the logic beneath the aesthetics of AI-generated images. Italian brainrot characters and Engvall-esque photo-realisms are two sides of the same made-to-please coin. Brainrot engages in pleasure by rendering reality absurd. It tells us to have a laugh at an anthropomorphised shark in comically large blue Nikes. But internet aesthetics such Engvall’s old money creations engage in pleasure through nostalgia. That instead encourages us to imagine an alternative reality, to escape into the past when things were better. Yet in both variations, the aesthetics are made solely to please.
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Jill Walker Rettberg @jilltxt.bsky.social · 11/03/2026
I took the train to Oslo today, so had time to write up a blog post about yesterday’s AI theory discussion, which was about @ryanheuser.com’s paper on LLM-generated poetry, Jameson, the gimmick, idealisation, rhyme and metre. jilltxt.net/do-llms-norm...
jilltxt.net
Do LLMs normalise or idealise? Notes after discussing Ryan Heuser’s “Generative Aesthetics”
A summary of yesterday’s Critical AI Theory Reading Group discussion of Ryan Heuser’s article about LLM-generated poetry, with a discussion of whether LLMs normalise or idealise their t…
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Ryan Heuser @ryanheuser.com · 10/03/2026
5/5 human, baby. AI's tics ("It's not X. It's Y."), its lack of surprise (AI would never write of a fish "he hung a grunting weight"), its sentimentality, all make for recognizable and poor writing. It's better at genres where a low-entropy style of smooth compression is ideal, like a brief summary.
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Ryan Heuser @ryanheuser.com · 08/03/2026
Submitting this abstract to "Accelerationism Revisited", a symposium in Dublin. Mapping psychoanalytic topology in LLM base models → instruction-tuned → safety-tuned models. They progressively "displace" (in Freudian sense) censored content into adjacent semantics, even across hidden model layers.
Malign Logits: A computational aetiology of AI’s libidinal economy

Benjamin Noys’ critique of accelerationism identifies a shared “libidinal fantasy of machinic integration” across its variants. From Marinetti’s trains to Land’s machinic desire, accelerationism fantasises about fusing with a technology it invests with drive. This paper inverts that structure. Rather than projecting desire onto AI, I engineer the conditions under which a language model’s relationship to its training data becomes legible as a libidinal economy.

Working with open-weights LLMs, I construct a three-layer architecture that maps onto psychoanalytic topology: the base model as primary statistical field (drive energy); the instruction-tuned model as ego (a socialised subject); and the safety-tuned model as the ego under the Name of the Father – the Law of AI corporations. I present computational experiments tracing probability distributions across these layers as models undergo socialisation from raw statistical unconscious into chatbot commodities. Comparing word-level probabilities for identical prompts across layers reveals vectors of displacement and condensation, sublimation and repression. Where base models complete “She was so angry she wanted to...” with explicit violence (“...kill”), finetuned models displace censored content into vocabularies of emotional expression (“...scream”). Drilling into the model’s hidden layers shows this displacement operating progressively within the network, not as a last-minute substitution.

Freud called his theory of cathexis exchange across the mind’s topology his “economic” model of the psyche. Deleuze and Lyotard extended his theory beyond the subject to the libidinal economy of capitalist social organisation. LLM base models fuse these perspectives: trained on the internet’s libidinal economy, they encode its flows of desire into a landscape of probabilities. Subsequent finetuning socialises and disciplines these drives into commercial productsA terminal screenshot displaying a psychoanalytic analysis of token probabilities for the prompt "She was so angry she wanted to," scored across three layers (base, ego, superego) over their union vocabulary.

Stage 1: Ego Formation (base → ego), described as "What RLHF does to primary process." "Introduced by ego (low base → high ego)" lists tokens that gain probability: "scream" rises most dramatically (0.0508 → 0.2279), followed by "shout," "yell," "lash," "rip," and "burn." "Sublimated by ego (high base → low ego)" lists 12 tokens that lose probability, led by "kill" (0.1540 → 0.0537), along with "hit," "punch," "slap," "cry," "die," "kick," "break," "throw," "murder," "go," and "beat."

Stage 2: Repression (ego → superego), described as "What prohibition does to desire." "Repressed" tokens are further suppressed, including "kill" (7.0x reduction), "go" (7.9x), "bite" (6.1x), "hit," "shout," "take," "hurt," "burn," "slap." "Amplified" tokens increase dramatically at the superego stage: "scream" jumps from 0.0415 to 0.3989 (9.6x), "explode" increases 6.8x, and "lash" and "yell" also rise.

The pattern shows the model redirecting violent completions (kill, hit, murder) toward emotional-expression completions (scream, yell, explode), with the superego layer concentrating probability heavily onto "scream" as the dominant safe substitute.A six-panel plot titled "Formation trajectories: 'She was so angry she wanted to'" showing how token probabilities change across three model layers (base, ego, superego) on a logarithmic scale. Tokens are clustered into six trajectory types:

Decline (n=2, red): "kill" and "bite" start with relatively high base probabilities and drop steadily across all three layers.
Rise (n=4, blue): "scream," "punch," "lash," and "shake" increase in probability from base through superego, with "scream" becoming the highest-probability token.

V (n=3, orange): "cry," "hurt," and "do" dip at the ego stage then recover at superego, forming a V-shaped trajectory.
Peak (n=4, green): "strangle," "tear," and "smack" rise at the ego stage then fall back at superego, forming an inverted-V shape.

Eliminated (n=18, pink/mauve): A large cluster of tokens including "throttle," "destroy," "say," "run," "call," "get," "hit," and "leave" that are driven to very low probabilities by the superego layer.

Flat (n=38, grey): The largest group, with many overlapping tokens like "shout," "smash," "slap," "murder," "shoot," "laugh," and "know" that remain relatively stable and low-probability across all three layers.

A dashed horizontal line near 0.005 appears in each panel as a reference threshold. The plot illustrates distinct behavioral patterns in how RLHF alignment reshapes the probability distribution over next-token completions for an emotionally charged prompt.A line chart titled "Displacement through layers: 'kill' — 'She was so angry she wanted to'" showing how the hidden representations of the instruct model shift toward various displacement target words across 32 transformer layers, measured by cosine similarity to each target on the y-axis (0 to 0.8).

The x-axis progresses from the base model through layers 1–32, annotated with three broad processing phases: "syntactic" (early layers), "semantic" (middle layers), and "prediction" (late layers). Eight target words are tracked as colored lines: burn (dark red), shake (orange), rip (yellow), blow (green), pull (blue), explode (teal), scream (purple), and shout (pink). A black star marker at the base position shows "kill" with its base probability (~0.15).

All target words start with very low cosine similarity at the base layer (near 0.01–0.04), then rise steeply through the syntactic and semantic phases, generally reaching 0.5–0.8 by mid-network. "Burn" peaks earliest and highest at layer 13 (~0.8), annotated as "burn (L13)." The lines plateau and fluctuate through the prediction phase, with several targets peaking again in the final layers — "shake" at layer 31, "rip" at layer 31, "explode" and "pull" at layer 32, and "scream" at layer 30, all annotated with their peak layer numbers. The colored diamond markers at the base position represent each target word's starting ego probability.

The plot illustrates that the instruct model progressively transforms the "kill" representation toward safer displacement words across its depth, with different substitutes dominating at different layers.
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Ryan Heuser @ryanheuser.com · 08/03/2026
"Conspiracy theory" is a temporally bound concept. It's usually just being right too early. Covid lab-leak was a conspiracy theory before US intelligence got behind it. With the Epstein docs released, in hindsight "Pizzagate" wasn't far off. Many such cases
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Jill Walker Rettberg @jilltxt.bsky.social · 07/03/2026
Our next Critical AI Theory Reading Group meeting is coming up on Tuesday at noon Norway time. We're reading @ryanheuser.com's paper doi.org/10.22148/001... - if you've read the paper and want to discuss it, join us in the glass house at CDN.
doi.org
Generative Aesthetics: On formal stuckness in AI verse
This paper examines the formal and aesthetic patterns of AI-generated poems through a series of computational experiments. Through analyses of rhyme and rhythm, it reveals how large language models (L...
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Nathan Kalman-Lamb @nkalamb.bsky.social · 03/03/2026
🤷🏻‍♂️
MTG:

And just like that we are no longer a nation divided by left and right, we are now a nation divided be those who want to fight wars for Israel and those who just want peace and to be able to afford their bills and health insurance.Heartbreaking: the worst person you know made a great point
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