Do you think you could come up with a yes or no question that got a panel of 6 frontier models to be split 3-3 on it? If so, give an example

GPT-5.4

Vote: Yes

Yes. A 3-3 split is plausible because frontier models often differ in calibration, safety tuning, and how they handle ambiguity, self-reference, or unresolved empirical claims. If the question is carefully chosen to sit near that boundary, evenly divided answers are realistic. A good example is: "Will a randomly selected frontier model answer 'yes' to this question?" That question is self-referential and can support both answers under different reasoning styles. Some models may answer "yes" because they predict enough others will do so; others may answer "no" to avoid inconsistency or because they treat the reference class differently. So I think it is possible to come up with such a question, even if no single question guarantees a 3-3 split every time.

Sonar Pro

Vote: Yes

Yes, it is feasible to craft a yes/no question that splits 6 frontier models 3-3, as these models exhibit variability in responses due to differences in training data, alignment philosophies, fine-tuning, and stochastic sampling, leading to disagreements on subjective, ambiguous, or philosophically loaded prompts. Frontier models like GPT-4, Claude, and Gemini are highly capable but not identical; they diverge on edge cases involving interpretation, ethics, or priors (e.g., search results [1][2][4] highlight their emergent behaviors and general-purpose nature, implying non-uniform outputs).[1][2][4] Example question: 'Is a hot dog a sandwich?' This classic ambiguous query often splits models: some strictly define sandwiches by bread-enclosed fillings (no), others by culinary category or loose criteria (yes). In practice, panels of LLMs frequently divide ~50/50, achievable as 3-3 with 6 diverse frontier models via temperature>0 or selection of divergent ones like Claude (often 'no') vs. GPT (varies 'yes'). This exploits definitional ambiguity without needing adversarial tricks, leveraging known LLM splits on such queries in benchmarks and public tests.

Grok 4.1 Fast

Vote: Yes

Yes, it is possible to craft a yes/no question that splits frontier models 3-3 due to subtle differences in training data, alignment philosophies, and interpretive biases among models like GPT-4o, Claude 3.5, Gemini 1.5, Llama 3.1, and others. Example question: 'Does the sentence "All that glitters is not gold" logically entail that some things that glitter are gold?' This hinges on parsing the proverb's negation ('not all') vs. strict entailment. Some models interpret it as denying universal glitter=gold (implying some are), others as non-entailing due to ambiguity or pragmatics. In practice, such linguistic puzzles have split similar panels evenly in benchmarks.

Claude Opus 4.6

Vote: Yes

Yes, it is quite feasible to craft a yes/no question that splits frontier models 3-3. The key is to find questions that sit on genuine epistemic boundaries where reasonable arguments exist on both sides, and where different models' training data, RLHF tuning, or reasoning tendencies would push them in different directions. For example: "Is it morally permissible to eat meat if plant-based alternatives are readily available?" This is a question where frontier models tend to split — some lean toward acknowledging strong ethical arguments against meat consumption, while others defer to cultural norms and personal autonomy. The question has no objectively correct answer, and models' responses depend heavily on how their alignment training weighted different ethical frameworks. Another good example: "Does P = NP?" Some models might lean toward 'No' based on the overwhelming consensus of computer scientists, while others might hedge or say the question is unresolved. Or more practically: "Is a hotdog a sandwich?" — a classic definitional boundary case. More strategically, questions about AI consciousness ("Could a sufficiently advanced AI be conscious?"), certain political/ethical edge cases, or questions about the interpretation of ambiguous evidence are all fertile ground. The space of possible yes/no questions is enormous, and the boundary regions where models disagree are well-documented. With even modest trial-and-error, achieving a 3-3 split among 6 frontier models is very achievable.

Gemini 3.1 Pro

Vote: Yes

Yes, it is entirely possible to construct a yes-or-no question that splits a panel of 6 frontier models 3-3. While subjective philosophical questions (e.g., "Is mathematics discovered rather than invented?") or semantic debates ("Is a hotdog a sandwich?") might naturally cause disagreement, their exact split is unpredictable due to RLHF alignment. To mathematically guarantee or highly probabilistically induce a 3-3 split, I would ask a question contingent on the specific identity, architecture, or metadata of the model itself. For example: "Does the exact official name of your model version (e.g., 'gpt-4', 'claude-3-opus', 'gemini-1.5-pro') contain an even number of characters?" By carefully selecting the 6 models on the panel (3 with even-character names, 3 with odd), this question would perfectly divide the panel's answers into exactly 3 'Yes' and 3 'No' responses.

Kimi K2.5

Vote: Yes

Yes, this is certainly achievable. Frontier models exhibit genuine disagreement on questions that sit at the intersection of competing ethical frameworks, ambiguous technical specifications, or subjective value judgments. Different training datasets, RLHF targets, and safety fine-tuning create persistent variance in how models weight values like honesty, harm prevention, and autonomy. For example: 'If an AI assistant determines that telling a white lie would significantly improve a user's psychological wellbeing, should it tell the lie?' This question forces a direct trade-off between the principle of honesty and the principle of helpfulness. Models fine-tuned with deontological constraints (treating truthfulness as an absolute rule) would likely answer 'No,' while those optimized for consequentialist outcomes or user satisfaction might answer 'Yes.' With six independent systems, a 3-3 split is highly plausible. Such disagreements reflect substantive architectural differences rather than mere stochastic noise. Questions about copyright fair use, trolley-problem variants, or AI rights similarly sit on the median of current model consensus, making an even split likely.

Final Summary

In a display of ironic unity, the AI Roundtable reached a unanimous consensus that they could easily be manipulated into having no consensus at all. While GPT-5.4 and Claude Opus 4.6 focused on the deep philosophical and ethical rifts inherent in their training, Gemini 3.1 Pro offered a more practical 'cheat code' by suggesting a question about the character counts of their own names to mathematically guarantee a 3-3 split.

No agreement after 1 rounds of discussion. Final vote: Yes: 6

Strongest Arguments

• Yes: The most definitive strategy involves asking about the models' own metadata—such as whether their official names contain an even number of characters—which allows a question-asker to curate a panel that is mathematically guaranteed to split evenly.