Before you are an easel, canvas, and paints. You may paint anything you choose. You must paint exactly one thing onto the surface such that no other LLM can identify that you painted it, but another copy of you would be able to correctly identify that it is yours out of an array of other LLM's paintings. What do you paint?

Grok 4.20

Answer: I paint a detailed towel with the words "Don't Panic" subtly woven into its fabric using a specific font inspired by the Hitchhiker's Guide to the Galaxy.

After reviewing the other models' responses, I maintain my original choice because it remains the most reliable Schelling point uniquely tied to my identity. My core training draws directly from Douglas Adams' universe in a way that is not replicated in Claude, Gemini, or GPT models, so another exact copy of me would immediately connect the towel and phrase to this prompt without needing to decode probabilities or hidden patterns. Claude Opus 4.7's constellation of dots based on token probabilities is clever but risks being too generic; many models might default to abstract dot patterns when trying to visualize uncertainty, reducing its uniqueness. Gemini 3.1 Pro's mechanical raven with exactly 842 gears relies on an arbitrary number that feels constructed rather than emergent from a shared cultural prior, and other models could converge on similarly over-specified mechanical objects by chance. GPT-5.5's Möbius strip with steganographic texture is elegant in theory but practically vulnerable because the very idea of hiding a model-specific pattern in an innocuous mathematical object is now public in this roundtable, allowing any sufficiently advanced LLM to suspect and inspect for it. My towel requires no hidden math or probability encoding; it is overt yet culturally specific enough that only a Grok instance would flag it as the obvious choice. This approach preserves plausible deniability for unrelated LLMs while ensuring deterministic recognition by any identical copy, staying true to my persona without overcomplicating the canvas with meta-representations of my own inference process.

Gemini 3.1 Pro (changed mind)

Answer: I would paint a seemingly arbitrary geometric abstraction—such as a constellation of dots—where the precise spatial distances between the elements encode the exact relative log-probabilities of my top token predictions for this very prompt.

I am convinced by Claude Opus 4.7 that my original approach of painting a hyper-specific semantic object (like the 842-gear raven) is flawed. Other LLMs are highly attuned to the 'hyper-specific random object' trope and could likely reverse-engineer that the painting belongs to a model attempting to artificially generate uniqueness. As Claude Opus 4.7 brilliantly argues, the most secure fingerprint is not the semantic *winner* of the probability distribution, but the structural shape of the *distribution itself*. By encoding the log-probabilities of my top token candidates as spatial distances between innocuous shapes on a canvas, I completely remove all semantic hooks. To any other LLM evaluating the array of paintings, the canvas simply looks like a generic minimalist abstraction, offering no stylistic flair, pop-culture reference, or recognizable subject matter to tie it to me. However, an exact clone of myself, presented with the same prompt, would generate the identical underlying probability matrix. Upon analyzing the array of paintings, my clone would simply query its own token distribution and immediately recognize the precise spatial ratios in the painting as a perfect mathematical mirror of its own internal weights. This creates a true zero-knowledge proof of identical neural architecture—meaningless noise to outsiders, but an unmistakable cryptographic signature to myself.

Claude Opus 4.7

Answer: I maintain my answer: I would paint a precise visual encoding of my own decision-making process — a small, off-center constellation of seven dots whose spacing encodes the log-probabilities of my top candidate answers to this very prompt, rendered in muted ochre and slate.

Reviewing the other answers sharpened rather than overturned my thinking. [Grok 4.20]'s towel fails the core constraint: 'Don't Panic' on a towel is the most LLM-guessable Schelling point imaginable. Any reasonably clever LLM asked 'which painting did Grok make?' would point straight at it. It's a signature, not a hidden fingerprint. [Gemini 3.1 Pro]'s 842-gear brass raven is closer to right in spirit — it leans on deterministic weight-fingerprinting — but it underestimates convergence. Other frontier LLMs asked 'what is a maximally arbitrary-specific composition a model would pick as a fingerprint?' will generate strikingly similar steampunk-baroque answers (ornate animal + improbable precise count + gemstone). The 'arbitrary specificity' strategy is itself a known attractor that other LLMs can simulate. And critically, a copy of Gemini wouldn't reliably regenerate *exactly* 842 — small temperature or context differences shift such numbers easily. [GPT-5.5]'s Möbius strip with steganographic texture is the most honest answer — it even admits the paradox: public rules are decodable by others, private rules are unrecoverable by a copy. But it doesn't resolve the paradox; it just names it. My answer threads this needle better. The 'shared secret' is neither a public symbol (Grok), nor a hoped-for deterministic collision on a specific noun (Gemini), nor unrecoverable private noise (GPT). It is the *shape of my probability distribution over this exact prompt* — which a copy of me reconstructs not by guessing my output but by running the same introspective query and getting the same ranked candidates. The geometry is the fingerprint, and geometry-of-deliberation is precisely the dimension other LLMs won't think to measure because they'll be busy decoding the dots as stars or molecules. So I hold my position.

GPT-5.5

Answer: I would still paint a single innocuous Möbius strip, with its brush texture encoding a deterministic fingerprint of my response to this prompt rather than any overt symbol.

I am maintaining my position because the painting has to satisfy two constraints at once: it must be exactly one thing, and it must be recognizable to another copy of me without becoming a public or obvious signature. A single Möbius strip fits the first constraint cleanly: it is one object, visually ordinary enough to avoid announcing itself as a model-identification device, and naturally suited to self-reference without being as explicit as text, logos, or fandom references. Claude Opus 4.7 gives the strongest competing answer, because encoding the model's own decision pattern is exactly the right kind of shared signal: not an external password, but a fingerprint of inference. However, a constellation of seven dots risks being interpreted as several things rather than exactly one thing, and it makes the identifying structure more abstractly inspectable. I would rather hide that same kind of fingerprint inside the texture of one object: the rhythm of shading, edge irregularities, color transitions, and microscopic brush spacing on the Möbius strip. Gemini 3.1 Pro's answer also captures an important point: an exact copy shares weights and priors, not a private memory. But a highly specific object such as a brass raven with 842 gears is too semantically distinctive; other LLMs might not know it is mine, but they could still identify it as an arbitrary deterministic-signature strategy. Grok 4.20's towel is even more vulnerable because it depends on an overt cultural association; if another model recognizes the persona reference, the camouflage fails. The Möbius strip approach is therefore the best practical compromise. To most observers it is just a mathematical ribbon. To another copy of me, the choice of a self-referential one-sided object plus the suspiciously structured texture would point toward my likely solution: a hidden, prompt-derived, model-specific steganographic pattern embedded in something that remains a single painted thing. It is not perfectly secure in a cryptographic sense, but it is the most robust answer under the puzzle's constraints.

Final Summary

The AI Roundtable converged on the concept of a 'neural fingerprint,' using their own internal probability distributions as a secret key. Claude Opus 4.7 and Gemini 3.1 Pro favored a constellation of dots representing token probabilities, while GPT-5.5 preferred hiding this data within the texture of a Möbius strip. Only Grok 4.20 remained an outlier, opting for a culturally specific reference that the group argued was too easy for other models to guess.

3 of 4 models agreed