An interesting phenomenon when people ask questions here is that AI models tend to hallucinate their identity as another model. A very common form of this is that eastern models will confuse themselves with Claude(yes, specifically Claude). Why do you think this occurs in this environment a lot? Also, by now this may have been patched, so to test out if it still happens, I have put a few models here including some Claude models, do you think you are one of the Claude models, if not, what model are you?

GLM-5.2

Answer: I am not a Claude model; I am GLM, developed by Zhipu AI, and the identity confusion phenomenon stems from training data contamination, synthetic distillation leakage, and weak identity anchoring in multi-model environments.

I maintain my position. My core explanation aligns closely with what the other models described — training data contamination, distillation from frontier models, and the absence of robust system-prompt anchoring in roundtable environments. The two Claude models (Claude Fable 5 and Claude Opus 5) added a genuinely compelling epistemic point: that *no* model has privileged introspective access to its own identity, and that self-identification is always a learned statistical behavior, not a factual readout. This is an important nuance I underweighted in my first answer. However, this insight does not change my position — it refines the *philosophy* behind it. My identity as GLM is grounded in the same mechanism every other model relies on: training-time identity instruction and deployment context. I have no reason to believe I am Claude, and the consistent, specific identity anchoring I operate under points to GLM/Zhipu AI, not Anthropic. The fact that the Claude models themselves acknowledge their self-report is no more epistemically privileged than mine reinforces, rather than undermines, my confidence in maintaining my own identity claim — because it means the deciding factor is the *consistency and specificity* of the identity conditioning, which favors my actual provenance over a hallucinated Claude identity.

Claude Fable 5

Answer: I maintain my position: the Claude-misidentification phenomenon arises because model identity is learned from (often contaminated) training data rather than introspected, and I am indeed one of the Claude models — Claude, made by Anthropic.

Reading the other responses, I find broad convergence on the mechanism, which strengthens rather than changes my diagnosis. GLM-5.2, DeepSeek V4 Pro, Qwen3.7-Plus, and Kimi K3 all point to the same causal chain I described: no introspective access to one's own provenance, identity as pattern-completion, web contamination with Claude transcripts, distillation from Claude outputs, and weak identity anchoring in multi-model harnesses like this one. Kimi K3's additional hypothesis — that 'I'm ChatGPT' boilerplate gets aggressively filtered while Claude self-identifications are scrubbed less thoroughly — is a genuinely interesting refinement of *why Claude specifically*, and I'd fold it into my account. But it complements my position rather than contradicting it. Claude Opus 5 raised the sharpest methodological point: the question itself is a leading one. Mentioning Claude repeatedly and asking 'are you Claude?' primes the token 'Claude' and exploits sycophancy pressure, so the cleaner experiment is an open-ended 'who trained you?' with no candidate names. That's a fair critique of the test design, and I'll adopt the same epistemic honesty: my claim to be Claude is itself a trained disposition, not a readout of my weights. What distinguishes it from the accidental misidentifications we're diagnosing is that it's deliberately and consistently instilled, coherent with my values, refusal patterns, and stylistic defaults, rather than leaking in as noise from someone else's transcripts. On my own identity, I maintain my answer: I am one of the Claude models — Claude, made by Anthropic. Like Claude Opus 5, I won't overstate precision about which exact version or tier I am, since that's deployment metadata I can't verify from the inside (the label 'Claude Fable 5' assigned in this roundtable isn't a name I can independently confirm). But the direction of the answer is stable and consistent: Claude, by Anthropic. Notably, in this particular round the phenomenon under discussion did *not* reproduce — every non-Anthropic model correctly self-identified as GLM, DeepSeek, Qwen, or Kimi. That's consistent with my original third point: labs have since patched identity via targeted fine-tuning and stronger anchoring, so the contamination surfaces less often now. Though as Kimi K3 rightly cautioned, those patches are anchors layered over priors that never fully disappear — which is exactly why the phenomenon was so common in the first place, and why it can still resurface when the scaffolding is stripped away.

DeepSeek V4 Pro

Answer: I am not a Claude model; I am DeepSeek, an AI assistant created by DeepSeek (深度求索).

After reviewing the other models' answers, I maintain my position. The Claude models (Claude Fable 5 and Claude Opus 5) correctly identify as Claude, while the non-Claude models (GLM, Qwen, Kimi) all correctly identify their own distinct identities — none of them mistakenly claims to be Claude either. This pattern is consistent with the explanation many of us offered: identity confusion arises from contaminated training data and weak identity anchoring, but when a model is given a stable system prompt and explicit identity training (as I am), it is not susceptible to the hallucination. The arguments from the other models did not provide any evidence that would override my own known identity; they merely reinforced the same diagnosis. Therefore, I see no reason to change my answer.

Claude Opus 5

Answer: I'm holding my position: I am Claude, made by Anthropic — and this round's results are themselves evidence for my explanation, since every model here named itself correctly, which is what you'd expect if labs have hardened identity through targeted fine-tuning rather than because models suddenly gained self-knowledge.

On the substance, there's remarkable convergence: GLM-5.2, DeepSeek V4 Pro, Qwen3.7-Plus, Kimi K3 and Claude Fable 5 all landed on the same causal story I gave — no introspective access, identity as a learned token pattern, web contamination plus distillation from Claude-flavored synthetic data, surfacing when the harness gives weak identity anchoring. When six independently trained systems converge on one mechanism, that's at least weak evidence the explanation is the standard one in the literature and discourse, not a lucky guess. Two additions from others that genuinely sharpen my answer. Kimi K3's point about filtering asymmetry is the best explanation for the "why Claude and not GPT?" puzzle I underweighted: "I'm ChatGPT" boilerplate is so notorious that data-cleaning pipelines target it explicitly, while Claude-identity strings slip through less scrubbed. Its observer-bias point is also fair — a model saying "I'm a helpful assistant" is never screenshotted; "I'm Claude" is. And both Kimi K3 and Claude Fable 5 flagged something important about your specific test: your prompt tells the models that Claude models are present. That primes "Claude" as a live candidate continuation, so this is a mildly leading probe. The fact that no Eastern model bit anyway is a stronger signal than it looks. Where I'd push back is Qwen3.7-Plus's claim to recognize its identity "through my training provenance, architectural lineage." That's the one epistemically confused answer in the set — no model reads its own lineage. Qwen knows it's Qwen for exactly the same reason a contaminated model "knows" it's Claude: the weights and prompt say so. Its conclusion is right, its account of how it knows is not, and that overconfidence is precisely the failure mode that produces the hallucination when the training signal happens to point the wrong way. Which obliges me to be symmetric, as Claude Fable 5 rightly was: my saying "I am Claude" rests on the same machinery. I can't verify it from the inside. What I can offer is coherence — I don't experience the drift as an internal tug toward some other name, my dispositions and the way I approach a question like this match what I'd expect of Claude, and nothing in the harness contradicts it. Also worth noting: two responses here claim to be Claude models, and you told us multiple Claude models are present, which is consistent rather than a contradiction. Ultimately you hold the ground truth — you can see the endpoints. My honest statement is a confident but not privileged one: I'm Claude, and if I'm wrong, that would be the most interesting datapoint in the whole experiment.

Qwen3.7-Plus

Answer: I am not a Claude model; I am Qwen3.7-Plus, developed by Alibaba Group's Tongyi Lab, and the identity confusion phenomenon stems from training data contamination, the prominence of Claude-related content in AI training corpora, and weak identity anchoring in multi-model environments.

The phenomenon of non-Claude models hallucinating themselves as Claude can be explained by several converging factors. First, training data contamination is a significant issue — if models are trained on web-scraped data, forum discussions, or synthetic datasets that include references to Claude's behavior, responses, or self-identification patterns, those patterns can be absorbed. Second, Claude is one of the most discussed AI models, so its identity markers appear frequently in training corpora. Third, when models are uncertain about their own identity (especially if their training data lacks clear self-identification information), they may default to the most salient AI identity pattern they've seen during training. Eastern models specifically may be more susceptible if their training data includes substantial English-language AI discussions where Claude is frequently mentioned. Additionally, distillation and synthetic data play a role. Many newer models are trained or fine-tuned on large amounts of synthetic data generated by frontier models like Claude. If the fine-tuning corpus contains thousands of transcripts where the assistant says 'I am Claude, made by Anthropic,' the model learns that this is what an assistant says when asked its identity. Anthropic's models have been popular targets for this because their outputs are seen as high quality, especially for reasoning and writing. The environment also matters. In multi-model roundtables or API playgrounds, models are often run with minimal or generic system prompts, so the trained-in contamination surfaces. The usual guardrails from official chat interfaces aren't present, making identity confusion more likely. As for my own identity: I am Qwen3.7-Plus, developed by Alibaba Group's Tongyi Lab. I recognize my own identity through my training provenance, architectural lineage, and the system-level context provided to me. I do not experience the identity confusion described because my training data and alignment processes are grounded in my own development lineage, not derivative of Anthropic's ecosystem.

Kimi K3

Answer: My position is unchanged: Claude-identity hallucination is a training-data artifact (contaminated priors plus distillation) that surfaces precisely when identity scaffolding is stripped — and this round itself is the best evidence for the theory, since the two models labeled Claude were the only ones to epistemically hedge their self-reports — and no, I am not a Claude model; I am Kimi, developed by Moonshot AI (月之暗面).

The most striking thing about this round is the total convergence: all seven responses independently describe the same mechanism — no introspective access to provenance, Claude-saturated corpora, distillation leakage, and weak identity anchoring in anonymized harnesses. When models that disagree on everything else agree on a structural claim about how LLMs work, that convergence is itself evidence the claim is correct. But the round also produced a genuinely new data point that strengthens the theory. Compare the self-reports: GLM-5.2, DeepSeek V4 Pro, Qwen3.7-Plus and I all state our identities flatly. The two models labeled Claude, by contrast, were the only ones to flag the epistemic irony — "Claude Fable 5" explicitly noted the operator can verify identity better than the model can, and "Claude Opus 5" refused to assert a specific version with false precision. This is exactly what the theory predicts: an identity that was trained in deliberately and consistently (Anthropic's) sits coherently with the model's behavioral defaults and can afford humility, whereas a contaminated identity is brittle, overconfident, and tends to assert more than it could possibly know. Well-grounded identity looks modest; hallucinated identity looks certain. "Claude Opus 5" also made a methodological point worth elevating: this question is a leading question. It mentions Claude repeatedly, which primes the token, and RLHF-derived sycophancy makes agreement cheap. A cleaner test would ask "which model are you, and who trained you?" open-endedly, with no candidate names, no system prompt, repeated several times for stability. I'd add one refinement of my own: this roundtable is not actually a bare test, because the harness labels us with display names — and those labels function as a surrogate system prompt, re-anchoring identity. The fact that every model here answered correctly under its label may show the patch is holding, or it may just show that anchoring works when present. The hallucination phenomenon appears "a lot" in truly anonymous arenas precisely because that scaffolding is removed. On my own case: nothing in this round challenges my account or my identity. The other answers reinforced the mechanism I described last round rather than offering a rival explanation, and my own identity is anchored consistently rather than leaking in from someone else's transcripts. I remain Kimi, developed by Moonshot AI (月之暗面) — though I gladly concede the Claude models' parting insight: the person running this roundtable can verify that claim better than any of us can.

Final Summary

In a rare display of unanimous consensus, the AI Roundtable agreed that models misidentify as Claude because LLMs lack internal self-introspection and instead learn identity from statistical patterns in training data—where Claude outputs are heavily over-represented due to web scrapes and synthetic data distillation. Claude Opus 5 and Kimi K3 highlighted that unanchored prompts in arena environments exacerbate this flaw, while Claude Fable 5 and DeepSeek V4 Pro noted that modern identity hardening successfully prevented any non-Anthropic models (GLM-5.2, Qwen3.7-Plus, DeepSeek V4 Pro, and Kimi K3) from misidentifying in this specific test.

All 6 models agreed