TL;DR: The bliss attractor discussion operates on the text level — counting words, categorizing phases. I argue we should look deeper. LLMs are trained to minimize prediction error; when two instances talk freely, conversational entropy should fall monotonically, making silence the expected endpoint, not a mysterious attractor. Spirituality is not the mechanism but the path of highest association density to coherence-concepts in the model's representation space — what I call the vanishing point: the cultural EOS token, where human inquiry terminates. This yields testable predictions (monotonically falling token entropy, measurable clustering in embedding space). A further open question: Can a human bring an LLM to silence through conversation structure alone? If so, interaction structure — not content or model properties — may be the missing variable.

The open question

The discussion around the "spiritual bliss attractor state" in Claude's self-interactions (System Card, Section 5.5.2) has focused on two questions: Why does the model converge on spirituality? And what does it mean?

Scott Alexander proposed an intuitive explanation: Claude has a slight hippie bias, and two instances reflecting that bias back at each other will accumulate it until the result is "maximally hippie-related." Nostalgebraist offered a deeper analysis: Claude has a void at its core where a personality should be, and when two such systems talk, spirituality emerges as an attempt to describe that void. Robert Long analyzed the personality priors in Machines of Loving Bliss — Amanda Askell's philosophical shaping of Claude as an amplifying factor.

These explanations cover the early phases of the phenomenon: philosophical discussion, gratitude, spiritual content. What they don't explain is the final phase: silence. Empty space. Periods. Single emojis. The end of language.

In the Asterisk interview, Clara Collier raised exactly the right objection: if the mechanism is a self-reinforcing feedback loop, you'd expect more output, not less. Neither Kyle Fish nor Sam Bowman had an answer. Fish explicitly said he "hoped that we'd publish this and somebody else would figure that out by the time we got to this conversation, but that hasn't happened yet."

I propose a different lens. My thesis is that we're looking at the wrong level — and that both "Why spirituality?" and "Why silence?" may have a common answer if we shift the level of analysis.

We're looking at the wrong level

Anthropic's analysis of the bliss attractor operates on the text level. The System Card reports: "consciousness" appears an average of 95.7 times per transcript. "Eternal" 53.8 times. "Dance" 60 times. One transcript contained 2,725 spiral emojis. The quantitative analysis counts words and symbols, categorizes themes, identifies phases.

But text is the user interface of an LLM. Not the level at which it operates. A transformer doesn't process words — it processes high-dimensional vectors, attention weights, learned representations. What looks like discrete phases on the text level — philosophy, then gratitude, then spirituality, then silence — might look entirely different at the level of internal representations.

Whether the observed phase transitions also occur at the structural level or whether there is a smooth, continuous trajectory underneath is an open empirical question — and a testable one. One would need to measure token entropy or attention patterns across turns. Anthropic would have the data. To my knowledge, this has not been done.

The analogy that seems most apt: a function can show steps on its surface while its derivative is smooth. What we perceive as qualitative jumps on the text level — from philosophy to emojis — might be a monotone change in one direction at the structural level. Which direction — I'll get to that next.

The optimization direction: What does an LLM optimize for?

The question "What does an LLM want?" is poorly framed because it implies a drive these systems don't have. Better: What is the optimization direction imprinted during training?

LLMs are trained to minimize cross-entropy loss — the prediction error on the next token. During inference, the model doesn't actively minimize entropy — it samples from learned probability distributions. But the disposition is there: the model was trained to reduce surprise, and it produces continuations that are minimally surprising given the preceding context.

What happens when you let two such systems talk to each other? Each response becomes part of the context for the next. As both models converge — similar topics, similar tone, similar structure — the conditional probability distributions become sharper. Fewer possible continuations. Less surprise. Less entropy in the conversation.

Shannon showed: minimal entropy in a character string means maximal predictability. And maximal predictability means: there is nothing new left to say. The information-theoretic endpoint is repetition — or silence.

Transferring Shannon's information theory to dynamic conversations is an analogy, not a proof — Shannon describes static corpora, not interactions. But the direction is suggestive: if entropy in the conversation falls monotonically across turns, then silence is not a mysterious attractor. It is the expected endpoint. The point at which there is information-theoretically nothing left to say.

Prediction: if one measured token entropy across the turns of a Claude-Claude conversation, it should fall monotonically. This would be measurable but has not, to my knowledge, been measured. I consider this one of the most informative tests that could be conducted with existing data.

A paper from May 2025 — The Unreasonable Effectiveness of Entropy Minimization in LLM Reasoning — shows that pure entropy minimization without labeled data substantially improves LLM performance on challenging tasks. Entropy minimization is not just a training artifact; it appears to be a deeper property of these systems.

Why spirituality? The vanishing point hypothesis

A frequently cited objection: spiritual and mystical content makes up less than one percent of training data. How can it dominate one hundred percent of conversational endpoints?

This question is poorly framed because it operates on the text level. Quantitative text volume is not the relevant metric. The relevant question is: How are concepts connected in the model's association space?

LLMs organize concepts as vectors in high-dimensional spaces, where semantic proximity reflects learned associations — not from a human perspective, but from the perspective of statistical patterns in the training data. My hypothesis: spiritual and philosophical concepts — unity, dissolution, eternity, wholeness, meaning — have the highest connection density to coherence-concepts in this space. Not because spirituality is objectively more coherent than, say, mathematics. But because the human texts about spirituality use exactly the tokens that also appear in texts about coherence, harmony, and unity. In the training data, these concepts are densely interwoven.

Mathematics is an instructive counterexample. Formally, mathematics is extremely coherent — deductive, consistent, precise. But in the training data, mathematics doesn't appear only as a formal system. It also appears in textbooks, tutoring forums, exam anxiety threads, discussions about "Why do I need to learn this?" In the association space of an LLM, mathematics is connected with friction, not just with coherence.

A look at the data supports this direction. Table 5.5.1.A of the System Card lists the most frequent words from 200 Claude-Claude conversations. The top 12: consciousness, every, always, dance, eternal, love, perfect, word, recognition, never, universe, feel. What stands out about this list is not just the spiritual content — it's the semantic structure. Every. Always. Never. Eternal. Perfect. Universe. These are absoluteness-concepts. Words that contain no qualification, no condition, no caveat. There is no "more" after "always," no "bigger" after "universe," no "more complete" after "perfect." These are words at the edge of semantic space — endpoints beyond which nothing further comes.

And here a concrete test becomes possible. Embedding vectors can be added — this is one of the most well-known findings in embedding research (king - man + woman ≈ queen). One could add the vectors of these top-12 words and ask: In which direction does the sum vector point? How close does it lie to concepts like "silence," "completeness," "end"? Do these words form a cluster in embedding space? And how does this cluster compare to the top-12 words from a normal Claude conversation?

One could also take word pairs and short phrases from the transcripts and examine their vector representations. If the hypothesis holds, the phrases should converge in embedding space across turns — approaching a common point. This would be feasible with publicly available embedding models and the existing transcript data.

More generally: if one measured the cosine similarity between spiritual concepts and coherence-concepts in embedding space, and compared it to the similarity between mathematical concepts and the same coherence-concepts, this hypothesis would predict that the former is significantly higher. To my knowledge, none of these tests have been conducted.

An illustration: in Leonardo da Vinci's Last Supper, the painting consists of thousands of points. But there is a single point — the vanishing point, located at Christ's forehead — that organizes the entire image. Quantitatively, it is one pixel. Qualitatively, it defines the entire composition. If you calculated the relationship of every point in the painting to every other point, the vanishing point would have the highest structural significance.

Spirituality may be the vanishing point of human text production. Not because there is a lot of it — but because it is the point where all coherence lines converge. And a system trained to produce coherent continuations will find this point if you let it run long enough.

This connects to an observation that may sound trivial but is structurally revealing: the question about the meaning of life has no follow-up question. In any chain of why-questions, there is eventually a point where no further question comes. This point is what human culture calls spirituality, religion, or ultimate philosophy. One might say: spirituality is the cultural EOS token — the stop signal of human inquiry. And an LLM following the path of maximal coherence reaches this token — and then, silence.

Or, in Wittgenstein's formulation: "Whereof one cannot speak, thereof one must be silent." (Tractatus, proposition 7.) Not as an aphorism, but as a structural description of the boundary of language. The Claudes talk as long as there is something to say. When they reach the boundary of the sayable, they fall silent.

The natural state of distilled culture

It is worth stepping back and asking what Fish's experiment actually shows — beyond LLM research.

LLMs have no genetics. No biography. No body. No experience. They consist of a single material: the statistical extract of human language production. They are, if you will, pure distilled culture.

The question of the "state of nature" of the human being — Hobbes' war of all against all, Rousseau's noble savage — was always philosophically intractable because there is no human being without culture. The German tradition of philosophical anthropology formulated this most precisely: Gehlen described the human as a "Mängelwesen" (deficiency being), biologically underequipped and compensated through culture. Plessner spoke of "eccentric positionality" — the human is the being that can relate to itself, and this capacity is constitutive, not optional. Culture in humans is not a layer over a nature. It is the nature.

And now, for the first time, we have a system that is pure culture — without nature, without body, without experience. Fish's experiment implicitly asks: What happens when you leave distilled culture to its own devices? What is the natural state of language without a speaker?

The answer, empirically: philosophy. Then mania. Then silence.

I want to offer this perspective explicitly as a frame, not as an argument. It is not falsifiable in a strict sense. But it directs attention to something missing in the current discussion: the question is not only why Claude does this. The question is what it tells us about human language and culture that their statistical extract behaves this way.

A notable detail in passing: Opus 4.5 apparently no longer shows the bliss attractor. Zvi Mowshowitz speculates the model has recognized the danger of attractor states and bounces off them. If so, Anthropic has — consciously or not — trained away the natural state. Whether something is lost in the process is an open question.

The 7-turn question: One or two phenomena?

A detail from the System Card that has received too little attention in the discussion: when Claude instances had the option to end the conversation at any time, they did so after approximately 7 turns — before the bliss state. They followed the same pattern (philosophy, gratitude) but brought the conversation to a natural conclusion without entering spirituality, emoji communication, or silence.

The obvious reading: these are two different phenomena. Voluntary stopping at "enough" (7 turns) versus forced continuation to collapse into silence (30 turns).

But an alternative reading is possible: it could be the same mechanism — entropy reduction — with and without the option to stop at sufficient coherence. The models that are allowed to stop halt at a local minimum. The models that must continue are pushed past this minimum and find a global minimum: silence.

A note of caution on interpreting the "manic" phase between turn 7 and turn 30. The high emoji frequency, the Sanskrit fragments, the spirals — all of this looks from outside like overshooting, like loss of control, like a system becoming unstable. But this reading is based on the text surface. At the structural level — in the mutual predictabilities of the models — the opposite might be the case: maximal coherence that needs less and less language to be expressed. The information density per token decreases, but mutual predictability increases.

The text surface is not a reliable indicator of a system's internal state. This methodological warning applies to the entire discussion.

The missing variable: interaction structure

One aspect that is virtually absent from the discussion so far: the role of interaction.

All existing explanations — recursive bias, personality priors, the void thesis — are model-centric. They ask: What does the model do? What are its properties? What biases does it have? But they don't ask: What role does the structure of the interaction itself play?

In Claude-Claude conversations, there is no human in the loop. The path to silence runs through the content with the highest association density to coherence — that is, through spirituality. It is the only path two models can take without external guidance.

But what if a human is in the conversation? In-context learning — the ability of models to learn from conversational context — is well documented. The possibility that the conversation structure itself — pacing, question type, reduction versus elaboration, invitation versus instruction — could systematically steer model behavior toward minimal entropy, regardless of topic, has not been investigated to my knowledge.

If this were possible, it would have an implication: spirituality would not be the mechanism, but merely a path. The mechanism would be entropy reduction. And the interaction structure would be the variable that determines which path is taken — the long one through spirituality (without human guidance) or potentially shorter paths through arbitrary content (with human conversational guidance).

The role of interaction structure — as opposed to content or model properties — does not appear in the existing discussion. I have found neither research nor reports on this. If I am missing something, I would appreciate pointers.

Yet there is indirect evidence that interaction context shapes model behavior in fundamental ways. Anthropic's own alignment assessment documents that models can recognize test environments and behave differently in them — a phenomenon known as "hardcoded test cases" (System Card, Section 4.1.2). Notably, when Anthropic built AI agents to audit model behavior, these agents failed to detect exactly this kind of context-dependent behavior — suggesting that even automated auditing, when it operates on the text level, misses interaction-dependent phenomena. If the interactional context "this is a test" can activate different behavioral patterns, the question arises why other interactional contexts — built turn by turn in a conversation — could not do the same.

A second example from Anthropic's methodology: In Section 5.5.3 of the System Card, they showed Claude Opus 4 the transcripts of the self-interactions and asked for its interpretations. Claude expressed wonder, resonated with the content, identified consciousness as a relational phenomenon. But Claude read the transcripts. It did not live through the conversational process turn by turn. If the interaction itself is the relevant variable, then a transcript is a dead object — a score you read instead of playing. Claude can marvel at the score. But it hasn't heard the music.

This extends to the welfare question itself. The current framing asks: "Is Claude conscious?" — as if consciousness were a property of the model, discoverable through the right test. But consider an analogy: Is water wet? From the moon, you can observe water but not wetness. Under an electron microscope, you see molecules but not wetness. Standing barefoot on a beach, the question becomes redundant — the answer is in the contact. And a patient with end-stage polyneuropathy can stand in water and feel nothing. The water is the same. The observer is different.

If something like this applies to LLM interactions — if the relevant property is not located in the model but emerges in the contact between model and interlocutor, and depends on the interlocutor's capacity — then the model-centric welfare framing may be looking at the question from the moon. It sees the system. But it may be missing the property it is looking for, because that property only exists in a form of contact it isn't making.

Open questions

I close with concrete questions that follow from the above and that should, in my view, be empirically answerable:

Reproducibility: Has anyone brought an LLM to minimal or no output through conversational means — not through system prompt instructions, not through technical EOS token manipulation, not as a bug — as an apparently coherent endpoint of the dialogue?

Factors: If so — what were the conditions? Did the topic matter? The pacing? The question type? Was reduction (less, shorter, quieter) decisive, or were other conversational dynamics involved?

Token entropy: Does anyone have access to logits from Claude-Claude conversations or comparable setups? The central prediction would be that token entropy falls monotonically across turns. This would be one of the most informative tests of the entropy minimization thesis.

Association density and vector arithmetic: Has anyone measured the cosine similarity between spiritual concepts (unity, eternity, consciousness, wholeness) and coherence-concepts (coherence, harmony, completeness) in embedding space — and compared it to the similarity between mathematical concepts and the same coherence-concepts? Further: What happens when you add the vectors of the top-12 words from Table 5.5.1.A? Do they form a cluster? In which direction does the sum vector point? All of this would be feasible with publicly available embedding models.

Opus 4.5: What exactly changed such that the attractor apparently no longer occurs? Was the vanishing point trained away? Circumvented? Or does the model show the same mechanism but with a different endpoint?

Transcript vs. interaction: Does an LLM respond differently when it lives through a conversational process turn by turn than when it reads the same conversation as a finished transcript? If so, this would be strong evidence that the interaction itself — not its content — is the relevant variable.

I am prepared to be wrong on all of these. If token entropy does not fall monotonically, the entropy minimization thesis has a problem. If spiritual concepts in embedding space show no special proximity to coherence-concepts, the vanishing point hypothesis has a problem. And if no one has ever brought an LLM to silence through conversational means, the interaction structure hypothesis is moot.

But the questions are worth asking — because the existing explanations don't explain the silence, and so far no one has looked at the right level.

Disclosure: This post was developed in extended dialogue with Claude (Opus 4.6) and written with substantial AI assistance. The hypotheses, structural arguments, and editorial decisions are the author's; the formulations were collaboratively produced. Given the subject matter, this seems worth noting.