{"path":"research/why-representations-converge-and-whether-minds-do.md","content":"# Why Representations Converge, and Whether Minds Do\n\n**Date**: 2026-09-06 · **Type**: founder question, researched online the same day · **The question,\nverbatim**: *\"It's very interesting to consider WHY this might be. And whether human minds also\nconverge in the same way. Research this extensively online.\"* — asked about the line *\"unrelated\nmodels converge on the same average, so a second model family is not a second opinion on meaning.\"*\n· **Companions**: [universal-embedding-geometry-and-the-wager.md](universal-embedding-geometry-and-the-wager.md)\n(the first read of vec2vec, whose § 7 asked exactly this), [embeddings-tension-and-ai-slop.md](embeddings-tension-and-ai-slop.md)\n(the March statement of the tension), [convictions-across-scales.md](convictions-across-scales.md)\n(the genetic-code finding this rhymes with). · **Read-depth**: § 7 says what was read at what depth;\nseveral numbers below come from abstracts and highlighted passages rather than full bodies, and are\nmarked where that matters.\n\n---\n\n## 0. The answer in plain words\n\nModels converge because they are all compressing the same table. A text embedding is, provably for\nword vectors and empirically for sentence encoders, a compressed picture of which words appear near\nwhich in the training text. Two models fitted to the same written record recover the same picture up\nto a rotation, and vec2vec learns the rotation. Three pressures push the same way: predicting text\nwell on everything leaves few representations that work, bigger models find the same optimum, and\nthe architectures everyone uses start from nearly the same place before training even begins. The\n2026 measurements then shrink the claim to its true size: what survives careful measurement is\n**which items are neighbours**, not how far apart they are, not the global shape, not the reasoning\nthat runs on top, and not across modalities. The \"reality\" the models share is, for text, **the\nwritten record**, and every model samples the same one.\n\nHuman minds converge too, at one level and not at another. At the level of perception and of the\nbrain's organisation, strikingly yes: one person's visual cortex can be rotated into another's, the\nsemantic map that tiles cortex is largely the same from person to person, and the way languages\ncarve up sun, moon, water and salt shares structure across eighty-one languages. At the level of\n**concepts**, the answer splits, and the split is the finding (corrected 2026-09-06, see\n[contingency-and-convergence-across-levels.md](contingency-and-convergence-across-levels.md) § 1):\nthe **core** of a common noun — reference, category, prototype — is shared, while its **periphery** —\ngraded features, associations, weightings — varies: the population holds ten to thirty measurably\ndifferent *profiles* of feature and similarity judgements for even a plain noun, two random people\nmatch on the whole profile about one time in eight for a penguin and one in three to eight for a\npolitician, and everyone believes two thirds of others agree with them. Nobody confuses a penguin with\nan albatross; the divergence lives in what gets attached to the word, and it grows where the word's\nmeaning *is* what gets attached — the political vocabulary.\nPolitical words differ measurably by group. And the mechanism is different in kind: humans converge\nby interaction and transmission, pair by pair and community by community, so they converge *within*\ncommunities and diverge *between* them. A model trained on everyone's text averages *across*\ncommunities and lands on a point no community occupies. That is why \"the same average in every\nmodel\" is nobody's meaning: the average of a two-humped distribution is the valley between the humps.\n\nThe uncomfortable coda is that human minds are now converging in the models' way — not naturally,\nbut through the tool: writing with a language model measurably narrows the variety of how people\nwrite, and model outputs are less diverse than a plain web search.\n\n## 1. What \"converge\" has turned out to mean, 2024 → 2026\n\n**The hypothesis (2024).** Huh, Cheung, Wang & Isola, *The Platonic Representation Hypothesis*\n(ICML 2024): representations in different networks are becoming aligned — across architectures,\nobjectives, and even modalities — and the endpoint is *\"a shared statistical model of reality\"*, a\nrepresentation of the joint distribution over the events that generate the data. Alignment is\nmeasured on kernels (does model A think *apple* and *orange* are as close as model B does), it rises\nwith scale and performance, and language models' kernels come to resemble vision models' as the\nlanguage models improve.\n\n**The constructive form (2025).** Jha, Zhang, Shmatikov & Morris, *Harnessing the Universal Geometry\nof Embeddings* (NeurIPS 2025, arXiv:2505.12540): given unpaired embeddings from two text encoders\nwith different architectures and training data, a translator into a shared latent can be learned\nwith no pairs, no encoders and no candidate set. Numbers from the paper's body, read this time:\ntranslations reach cosine similarity up to **0.92** (v2) / **0.96** (v4) to the true target vectors,\nperfect matching on more than 8,000 shuffled embeddings, robust to out-of-distribution inputs\n(trained on Wikipedia questions, evaluated on tweets and medical records), about **50K** embeddings\nenough to learn a near-full-quality translation, and weaker but above-baseline translation into\nCLIP, a model trained partly on images. The stated conjecture is explicitly scoped: *\"neural networks\ntrained with the **same objective and modality**, but with different data and model architectures,\nconverge to a universal latent space.\"*\n\n**The corrections (2026).** Four papers, each narrowing the claim on its own axis; they agree with\none another more than any of them agrees with the 2024 headline.\n\n- **Global convergence was largely a measurement artefact; local neighbourhood convergence is real.**\n  Gröger, Wen & Brbić, *Revisiting the Platonic Representation Hypothesis: An Aristotelian View*\n  (arXiv:2602.14486): the standard similarity metrics are inflated by model width (chance similarity\n  grows with dimension over sample size) and by depth (reporting the maximum over many layer pairs).\n  After a permutation-based calibration, the correlation between global similarity and model\n  capability collapses (linear CKA from 0.86 to 0.45; Procrustes from 0.89 to 0.39) while\n  neighbourhood measures keep it (mutual-kNN near 0.85). Their refinement: *models converge to shared\n  local neighbourhood relationships* — who is near whom — but not to shared distances.\n- **Cross-modal convergence is coarse category overlap, and shrinks at scale.** Koepke et al., *Back\n  into Plato's Cave* (arXiv:2604.18572): the original evidence rests on ~1,000 one-to-one\n  image–caption pairs; at millions of samples mutual-nearest-neighbour agreement drops sharply; in a\n  controlled setting vision and language models retrieve the correct *class* but rarely the same\n  *instance*; real data is many-to-many and the metric does not survive that; and the trend of newer\n  language models aligning better with vision does not hold for recent models. Their phrase:\n  modalities inhabit their own *Umwelten*.\n- **Convergence lives in input encoding, not in reasoning — and learning pulls models apart.** Usama &\n  Chang, *Convergence Without Understanding* (arXiv:2605.23315): sixteen models from eight families\n  on 800 reasoning problems. Representations align before the decision (CKA 0.875) and diverge after\n  it (0.274); models agree *more* on problems they collectively fail (0.897) than on ones they solve\n  (0.830); shared information is decodable across models (66%) but causally near-inert (1.5–5.5% of\n  predictions flip when it is ablated); and **untrained models are more similar to each other (CKA\n  0.864) than trained ones (0.612)** — the shared architecture supplies a high-similarity starting\n  point from which training differentiates. Their reading: convergence *\"reflects the shared\n  structure of language as an input signal rather than a shared logic of reasoning.\"*\n- **Simple maps do not translate universally.** *How Far Do Simple Transformations Translate Across\n  Text Embedding Models?* (arXiv:2608.05980, Aug 2026): across nine embedding models, linear\n  translators recover shared structure for some pairs and *\"fail sharply for others\"*; compatibility\n  depends jointly on architecture, objective, pooling and data. And the *Shared Substrate* paper\n  (arXiv:2606.07882) finds a low-rank shared geometry that survives calibration *within* vision and\n  *within* language (a language-model panel at median calibrated CKA 0.907) but a cross-modality\n  median of 0.04 — with the named axes of the two substrates not corresponding at all.\n\n**Net, stated at the size the evidence supports.** Among models trained on the same objective in the\nsame modality on overlapping web data, the *neighbourhood structure* of the input encoding converges,\nlearnably enough that vec2vec can translate it; distances, global shape, the reasoning stage, and the\nbridge between modalities do not reliably converge. Everything in this document uses \"converge\" in\nthat narrowed sense.\n\n## 2. Why models converge — four mechanisms\n\n**Same table.** The oldest and cleanest result: Levy & Goldberg (NeurIPS 2014) proved that\nskip-gram with negative sampling — word2vec — is implicitly factorising the matrix of pointwise\nmutual information between words and their contexts, shifted by a constant; Kenyon-Dean, Newell &\nCheung (EMNLP 2020) showed the major word-embedding algorithms all optimise toward the same PMI\ntarget under different weightings. So a word vector's geometry *is* a compressed co-occurrence table\nof the corpus, determined up to a linear map by the corpus's statistics; two models of one corpus\nmust agree up to that map. Huh et al. extend this in an idealised setting: a certain family of\nlearners converges on a kernel equal to the PMI over the underlying *events* that cause the\nobservations, regardless of modality — *\"find an embedding in which similarity equals pointwise\nmutual information.\"* vec2vec is the empirical statement that this holds well enough for whole\nsentences that the map can be learned blind.\n\n**The objective leaves few solutions.** Huh et al.'s three selective pressures. *Multitask scaling*:\nthere are fewer representations competent for N tasks than for fewer, so as models are trained on\nmore data and tasks the surviving solution set shrinks (Cao & Yamins' *contravariance* principle).\n*Capacity*: if an optimum exists, bigger function classes are likelier to cover it, so bigger models\nof different architectures land on the same one. *Simplicity bias*: deep networks prefer simple fits,\nand the preference strengthens with size, so the solution space contracts further.\n\n**Shared architecture, shared starting point.** The Convergence-Without-Understanding random baseline\nis the underrated fact here: transformers with random weights already agree with each other more than\ntrained transformers do. Causal masking, residual streams and layer normalisation make a\nhigh-similarity family before any data arrives; a good share of \"convergence\" is that everybody builds\nthe same kind of machine, and the shared tokenisers and web corpora do the rest.\n\n**Shared source.** Hosseini, Casto, Zaslavsky, Conwell, Richardson & Fedorenko, *Universality of\nrepresentation in biological and artificial neural networks* (bioRxiv 2024): sentences that\nhigh-performing language models represent similarly to each other are the ones whose brain responses\nthose models predict best, the relation runs both ways (stimuli represented similarly across\n*brains* are represented similarly across models), and the axes that models share for language are\n**perceived sentence frequency and meaning generality** — the corpus's own statistics, in other words.\nThe convergence axis is the frequency axis.\n\nPut together, the \"reality\" the Platonic hypothesis invokes is, for a text model, **the written\nrecord**, and every text model samples the same one. What they share is what that record has; what\nthey all lack is what it has not got.\n\n## 3. Do human minds converge?\n\n### 3a. At the level of the brain and of perception — yes, strikingly\n\n- **Shared information in idiosyncratic anatomy.** Haxby's hyperalignment programme (Haxby et al.\n  2011; Guntupalli et al. 2016; Haxby, Guntupalli, Nastase & Feilong, eLife 2020): the information\n  shared across brains is encoded in fine-scale topographies that differ from person to person, and it\n  can be aligned by projecting each brain into a common high-dimensional space that preserves the\n  geometry of pairwise dissimilarities. *\"The fundamental property of brain function that is\n  preserved across brains is information content.\"* Residual individual differences after alignment\n  are reliable and predict cognition — the variation is real, but it sits on top of a shared\n  geometry.\n- **The same test vec2vec passed, run on brains.** *Platonic Representations in the Human Brain*\n  (arXiv:2605.20496, 2026): subject-specific embeddings learned from fMRI alone (Natural Scenes\n  Dataset) translate across people through **unsupervised orthogonal rotations** with no paired\n  samples — recall@1 of **0.83** once the rotations are synchronised into one shared space — and\n  brain-to-brain translation is *tighter* than any model-to-brain mapping tested. Visual cortex, in\n  other words, is approximately isometric across individuals.\n- **A shared semantic map.** Huth, de Heer, Griffiths, Theunissen & Gallant (Nature 2016): voxel-wise\n  models of people listening to hours of stories recover about four semantic dimensions shared\n  across subjects, in intricate patterns *\"largely conserved across individuals\"* — with the authors'\n  own caveat that every subject was raised in a Western industrial society, so nature and shared\n  experience are not yet separated.\n- **A shared carving of the world, across languages.** Youn et al. (PNAS 2016): using which concepts\n  share a word (polysemy) across 81 languages chosen for diversity, the semantic network of celestial\n  and landscape vocabulary — sun, moon, water, sea, salt, mountain — has the same structure across\n  language groups, *\"largely independent of geography, environment, and the presence or absence of a\n  literary tradition\"*; sea and salt are closer to each other than either is to sun, inland and\n  coastal alike. Jackson et al. (Science 2019, 2,474 languages) find the same shape for emotion:\n  substantial variation predicted by geographic proximity, on top of a universal structure in which\n  every family differentiates emotions primarily by **valence and activation**.\n\n### 3b. At the level of concepts — no, and by a wide margin\n\n- **Latent diversity in common nouns.** Marti, Wu, Piantadosi & Kidd, *Latent Diversity in Human\n  Concepts* (Open Mind 2023; 2,700 participants): similarity and feature judgements for common\n  animals and for politicians, clustered with reliability controls so measurement noise cannot\n  produce the result. **At least ten to thirty quantifiably different variants of the feature-and-similarity\n  profile exist in the population for even common nouns.** The probability that two randomly chosen\n  people fall in the same profile cluster averages **14–70% for animals** (penguin about **12%**, driven\n  by disagreement about whether penguins are heavy) and **13–33% for politicians**. ⚠ Read at the\n  paper's own scope: the instruments are similarity triads and feature ratings, the authors say the\n  clusters *\"indicate that there are some underlying differences\"* and that the results *\"do not\n  support the notion that every single use of a concept is distinct\"* — the **core** (reference,\n  category) is shared and the **periphery** varies. An earlier version of this doc compressed that to\n  \"two random people share the same penguin one time in eight\", which overstates it; corrected\n  2026-09-06 on the founder's challenge, with the level-by-level reading in\n  [contingency-and-convergence-across-levels.md](contingency-and-convergence-across-levels.md). And people do not know it: for almost the whole range of\n  actual agreement, participants believed about **two thirds** of others would answer as they did,\n  including on items where essentially nobody did. Kidd's own gloss is the design brief for a\n  clarification-first system: *\"When people are disagreeing, it may not always be about what they\n  think it is. It could be stemming from something as simple as their concepts not being aligned\"* —\n  and the remedy, *\"Questions like 'What do you mean?' can go a long way.\"*\n- **Alignment across languages is strongest where structure is tightest.** Thompson, Roberts &\n  Lupyan (Nature Human Behaviour 2020; 1,010 meanings, 41 languages): the most aligned word meanings\n  are in domains with high internal structure — number, quantity, kinship — while natural kinds,\n  common actions and artefacts align *\"much less well\"*, and geographic proximity, historical\n  relatedness and cultural similarity predict how aligned two languages' meanings are. Read with Youn,\n  the picture is consistent: the universally shared part is the part the world itself structures.\n- **Political words are measurably different words for different groups.** Rodriguez, Spirling &\n  Stewart, *Embedding Regression* (APSR 2023): a regression framework for context-specific word\n  meaning that works from a handful of uses, with significance tests; on the Congressional Record,\n  *immigration* is among the most differently expressed terms for Democrats and Republicans —\n  *\"controversial terms divide parties not simply in the way they are attached to topics of debate but\n  in their very meaning.\"* KhudaBukhsh, Sarkar, Kamlet & Mitchell (AAAI 2021; 86.6 million YouTube\n  comments): treating two news-channel audiences as two languages and aligning their embedding\n  spaces, the misaligned pairs include *black lives matter* ↔ *all lives matter*. Milbauer, Mathew &\n  Evans (EMNLP 2021; a billion Reddit comments): aligning community-specific spaces, *democrat* and\n  *republican* are among the words that fail to align between r/politics and r/the_donald.\n\n### 3c. The mechanism is different in kind\n\nHumans are not fitted to a shared table; they are produced by **interaction and transmission**, and\nboth are local.\n\n- **Pair by pair.** Brennan & Clark (1996): when people refer to the same thing repeatedly they\n  converge on the same terms, but by a *historical* process — a speaker proposes a conceptualisation,\n  the partner ratifies or amends it, and the resulting **conceptual pact** is provisional and\n  **partner-specific**; with a new partner the pact is rebuilt. Alignment is made in the conversation\n  and belongs to it.\n- **Generation by generation.** Kirby and colleagues (PNAS 2008; Kirby, Tamariz, Cornish & Smith\n  2015): languages transmitted through a learning bottleneck become more compressible; languages used\n  for communication become more expressive; **only both together yield structure**. Transmission alone\n  collapses to degenerate systems, communication alone stays holistic and idiosyncratic. Shared\n  structure is what falls out of a community that both learns from and talks to itself.\n\nSo human convergence has a **unit**: the dyad, then the community of transmission. People converge\n*within* the units that produced their concepts and diverge *between* them, and Kidd's team reads the\nindividual variation as experiential — *\"presumably because they haven't lifted a penguin.\"* There is\nno \"average speaker\" whose concept everyone approximates; the population is a mixture of variants,\neach held by someone.\n\n## 4. The asymmetry that matters\n\nA text model fitted to the whole web averages **across** the communities that produced its training\ndata. Its vector for *immigration* sits between the Democratic vector and the Republican vector that\nRodriguez's method can separately estimate; its vector for *penguin* sits at the centroid of Marti's\nten-to-thirty peripheral profiles, on top of a core everyone shares. That location is a well-defined statistic and nobody's meaning — the March\ndocument's *statistical ghost*, which now has an empirical anatomy: **the ghost is the mean of a\nmultimodal distribution**. And the reason every model has the *same* ghost is § 2: they all average\nthe same population with the same objective, so their neighbourhood structures agree.\n\nHuman minds do not converge in that way because a human is one of the modes, not the mean. Where\nhumans do converge — perception, the valence axis, the carving of sun and water — it is because the\n*world* supplies the same structure to every body (Hosseini's bidirectional finding, Jackson's\nvalence–activation universal, Youn's natural kinds). Where they diverge — abstract, evaluative and\npolitical concepts — the world supplies no such anchor and the community supplies the meaning. That\nis exactly the boundary the corpus draws elsewhere between the empirical terminus and the evaluative\nones, seen from the representation side.\n\n## 5. The coda: minds converging toward the models\n\nThe question \"do human minds converge the same way\" has a time-dependent second answer.\n\n- **Models regress to the mean of their data, and alignment training makes it worse.** *Generative\n  Monoculture in Large Language Models* (ICLR 2025): outputs are markedly narrower than the training\n  data for the same task — only positive reviews of books with mixed reception, a narrow set of\n  algorithms where humans used many — and changing temperature, sampling or prompting does not repair\n  it; the authors locate the cause in alignment fine-tuning. Sourati et al., *The homogenizing effect\n  of large language models on human expression and thought* (TICS 2026), synthesise the field: models\n  reflect Western, educated, industrial, rich and democratic response patterns with far less variance\n  than humans, and when prompted to simulate a population they *\"capture merely the socially\n  'correct' or the mean of the distribution\"* (citing Santurkar et al. 2023, Abdurahman et al. 2024,\n  Atari et al. 2023). Wright et al., *Epistemic Diversity and Knowledge Collapse* (arXiv:2510.04226;\n  27 models, 155 topics, 12 countries): nearly every model generates a less diverse set of real-world\n  claims than a basic web search; **larger models are less diverse**; retrieval-augmented generation\n  is the one factor that reliably raises diversity.\n- **And it transfers to the people who use them.** *The shrinking landscape of linguistic diversity in\n  the age of large language models* (Nature Human Behaviour, Aug 2026; seven datasets, 880,000\n  texts): LLM writing assistance reduces the variance of writing complexity by **21–50%** and\n  amplifies dominant patterns while suppressing others. Anderson, Shah & Kreminski (2024): ideas\n  generated with ChatGPT's help are less diverse *at the group level* — not because individuals fixate\n  more, but because the model hands different users the same ideas. Sourati et al. name the loop:\n  model outputs shaped by central tendencies are reabsorbed into human discourse and into the next\n  training set.\n\nSo: naturally, no — human minds converge locally and remain a mixture. Under mediation, increasingly\nyes — toward the same mean the models share, which is the mechanism this project's threat model has\ncalled *convergence-illusion via LLM mediation* since July, now with a population-level literature\nbehind it.\n\n## 6. What this changes for Deliberus\n\n1. **The retrieval ruling now has a principled warrant, not only a measured one.** What survives\n   calibration across models is *neighbourhood* — which claims are near which — and that is exactly\n   the candidate-retrieval use. Fine distances and global geometry, which a sameness *verdict* would\n   need, do not survive. So cosine may propose a pair and may never decide it; the seven-key label set\n   and the human sameness judgement stay outside the embedding\n   ([retrieval-instruments-beyond-cosine.md](retrieval-instruments-beyond-cosine.md),\n   [soft-canonical-clustering-and-reversible-merge-semantics.md](soft-canonical-clustering-and-reversible-merge-semantics.md)).\n2. **The two-family jury, corrected by layer.** At the *encoding* layer, two model families give close\n   to one opinion (universal geometry). At the *reasoning* layer they diverge in representation\n   (post-decision CKA 0.274) while their **errors still correlate** (the ICML 2025 Correlated-Errors\n   result already in the corpus). The first read's § 3 said the jury *\"is not independence of\n   world-model\"*; the precise statement is that it is not independence of *perception*, and is a\n   bounded, real independence of *judgement*. Cross-family disagreement remains the best cheap\n   fragility predictor, and it remains weaker than it reads.\n3. **Clarification-first has an empirical anchor it did not have.** Marti's two-thirds-assumed against\n   one-in-eight-actual is the measured size of the gap the Interpretation Checkpoint and the concept\n   senses exist to close; the intervention the study's own authors recommend is *\"What do you mean?\"*\n   The clarification layer is not politeness, it is the population's missing self-knowledge.\n4. **\"vec2vec for worldviews\" already exists at word level, and works with few documents.**\n   Rodriguez's embedding regression estimates a *group-specific* meaning of a focal word from a\n   handful of contexts and tests whether groups differ; KhudaBukhsh and Milbauer align whole community\n   spaces and read off the words that refuse to align. On attributed claims that is a buildable\n   instrument: per-position vectors for a contested concept, their distance as a reading, the\n   refusing-to-align terms as candidates for the concept layer's *bifurcated* state. The first read's\n   four-check verdict stays **Conditional** for a translator of *arguments* (a shared latent exists\n   only where two frames share reality, which is the wager) and is **Raised** for the lexical signal —\n   proposed, unruled.\n5. **The flattening mechanism has a population-level literature now.** Paraphrase-flattening was\n   measured here on single extractions; monoculture, epistemic-diversity and the 2026 writing-variance\n   result show it operating at the scale of a population, with alignment tuning as a named cause. Two\n   consequences: the disagreement-preservation instrument is measuring the right thing; and\n   graph-grounded output is *measurably* more diverse than model-memory output (retrieval-augmented\n   generation raises epistemic diversity), which is an outside warrant for the inspectable-synthesis\n   architecture over answers from parametric memory.\n6. **On the wager, the same finding as the genetic code.** Models converge on a shared basis because of\n   *common descent* — one web, sampled by all — not because the basis was forced by reality; that is\n   the exact structure [convictions-across-scales.md](convictions-across-scales.md) found for the\n   shared genetic code. Model convergence is therefore evidence about the written surface, not about a\n   shared human bottom. The human evidence sorts by level: perceptual and affective structure is shared\n   across bodies (the *phylogenetic* support the sweep already named), conceptual and political\n   meaning is community-made and divergent — which is where the wager has to be *tested*, and cannot\n   be read off any embedding.\n\n## 7. What was read, and at what depth\n\nRead in the body: vec2vec's results sections (numbers quoted above); Marti et al.'s results and\ndiscussion; Levy & Goldberg's derivation; Huh et al.'s § 3 (the three pressures) and the project\npage's PMI result. Read at abstract-plus-highlighted-sections depth: the four 2026 corrections\n(Aristotelian view, Plato's Cave, Convergence Without Understanding, Simple Transformations), Hosseini\net al., Thompson et al., Jackson et al., Youn et al., the homogenisation papers, Brennan & Clark,\nKirby. Cited through a survey rather than read: Santurkar et al. 2023, Abdurahman et al. 2024, Atari\net al. 2023. No figure was reproduced; every number above is one a source states in prose. Two claims\nare inferences of mine and marked as such in place: that the frequency axis is *the* convergence axis\n(Hosseini names it as a shared axis, not the only one), and that the mean-of-a-mixture reading of the\nghost transfers from words to sentence embeddings (the word-level results are the proven ones). Verify\nbefore citing outward.\n\n## Sources\n\n- Huh, Cheung, Wang & Isola, *The Platonic Representation Hypothesis*, ICML 2024 — https://arxiv.org/abs/2405.07987 · project page https://phillipi.github.io/prh/\n- Jha, Zhang, Shmatikov & Morris, *Harnessing the Universal Geometry of Embeddings*, NeurIPS 2025 — https://arxiv.org/abs/2505.12540 · https://vec2vec.github.io/\n- Gröger, Wen & Brbić, *Revisiting the Platonic Representation Hypothesis: An Aristotelian View*, 2026 — https://arxiv.org/abs/2602.14486 · https://brbiclab.epfl.ch/projects/aristotelian/\n- Koepke et al., *Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale*, 2026 — https://arxiv.org/abs/2604.18572\n- Usama & Chang, *Convergence Without Understanding*, 2026 — https://arxiv.org/abs/2605.23315\n- *How Far Do Simple Transformations Translate Across Text Embedding Models?*, Aug 2026 — https://arxiv.org/abs/2608.05980\n- *The Shared Substrate of Modern Encoders*, 2026 — https://arxiv.org/abs/2606.07882\n- *The Concept of Representation in ML: Beyond Plato and Aristotle*, 2026 — https://arxiv.org/abs/2607.17800\n- Levy & Goldberg, *Neural Word Embedding as Implicit Matrix Factorization*, NeurIPS 2014 — https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization\n- Kenyon-Dean, Newell & Cheung, *Deconstructing word embedding algorithms*, EMNLP 2020 — https://aclanthology.org/2020.emnlp-main.681/\n- Hosseini, Casto, Zaslavsky, Conwell, Richardson & Fedorenko, *Universality of representation in biological and artificial neural networks*, bioRxiv 2024 — https://doi.org/10.1101/2024.12.26.629294\n- Schrimpf et al., *The neural architecture of language: Integrative modeling converges on predictive processing*, PNAS 2021 — https://www.pnas.org/doi/10.1073/pnas.2105646118\n- Goldstein et al., *Shared computational principles for language processing in humans and deep language models*, Nature Neuroscience 2022 — https://www.nature.com/articles/s41593-022-01026-4\n- Haxby, Guntupalli, Nastase & Feilong, *Hyperalignment*, eLife 2020 — https://doi.org/10.7554/elife.56601\n- *Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry*, 2026 — https://arxiv.org/abs/2605.20496\n- Huth et al., *Natural speech reveals the semantic maps that tile human cerebral cortex*, Nature 2016 — https://www.nature.com/articles/nature17637\n- Youn et al., *On the universal structure of human lexical semantics*, PNAS 2016 — https://doi.org/10.1073/pnas.1520752113\n- Jackson et al., *Emotion semantics show both cultural variation and universal structure*, Science 2019 — https://www.science.org/doi/10.1126/science.aaw8160\n- Thompson, Roberts & Lupyan, *Cultural influences on word meanings revealed through large-scale semantic alignment*, Nature Human Behaviour 2020 — https://doi.org/10.1038/s41562-020-0924-8\n- Marti, Wu, Piantadosi & Kidd, *Latent Diversity in Human Concepts*, Open Mind 2023 — https://doi.org/10.1162/opmi_a_00072 · press: https://news.berkeley.edu/2023/03/16/new-evidence-on-why-we-talk-past-each-other/\n- Rodriguez, Spirling & Stewart, *Embedding Regression*, APSR 2023 — https://doi.org/10.1017/S0003055422001228\n- KhudaBukhsh, Sarkar, Kamlet & Mitchell, *We Don't Speak the Same Language*, AAAI 2021 — https://ojs.aaai.org/index.php/AAAI/article/view/17748\n- Milbauer, Mathew & Evans, *Aligning Multidimensional Worldviews and Discovering Ideological Differences*, EMNLP 2021 — https://aclanthology.org/2021.emnlp-main.396/\n- Brennan & Clark, *Conceptual pacts and lexical choice in conversation*, 1996 — https://doi.org/10.1037//0278-7393.22.6.1482\n- Kirby, Cornish & Smith, *Cumulative cultural evolution in the laboratory*, PNAS 2008 — https://www.pnas.org/doi/10.1073/pnas.0707835105 · Kirby, Tamariz, Cornish & Smith, *Compression and communication in the cultural evolution of linguistic structure*, Cognition 2015\n- Lan, Torr, Meek, Khakzar, Krueger & Barez, *Sparse Autoencoders Reveal Universal Feature Spaces Across LLMs*, 2024 — https://arxiv.org/abs/2410.06981 · Thasarathan et al., *Universal Sparse Autoencoders*, ICML 2025\n- *Generative Monoculture in Large Language Models*, ICLR 2025 — https://openreview.net/forum?id=yZ7sn9pyqb\n- Sourati et al., *The homogenizing effect of large language models on human expression and thought*, TICS 2026 — https://arxiv.org/abs/2508.01491\n- Wright et al., *Epistemic Diversity and Knowledge Collapse in Large Language Models*, 2025 — https://arxiv.org/abs/2510.04226\n- *The shrinking landscape of linguistic diversity in the age of large language models*, Nature Human Behaviour 2026 — https://www.nature.com/articles/s41562-026-02550-0\n- Anderson, Shah & Kreminski, *Homogenization Effects of Large Language Models on Human Creative Ideation*, C&C 2024 — https://arxiv.org/abs/2402.01536\n"}