# Must Every Question Have an Answer? Response Expectations and Uydurulizm in AI

We ask a question. Seconds later, an orderly explanation appears, complete with headings and a confident conclusion. But are we seeking an answer, or grounds we can trust? They do not always arrive together.

In the Gemini exchange supplied by Ali Değişmiş, the opening question was short: what is Degismisism? The system supplied a definition, then denied that the concept existed when challenged. When the user identified the author and mentioned a DOI paper, the account changed. What stands out is the similar certainty accompanying successive, different positions. The two accompanying cases examine concept identification and article production within this one conversation, not two independent experiments.

## What does fluency establish?

Well-formed prose does not by itself establish strong evidence. We can analyse an unfamiliar word and propose a plausible meaning. Yet “I am interpreting the word” differs from “this is what the concept means”. The first exposes the limits of an interpretation; the second may lead readers to expect a verified definition.

Here Uydurulizm is used as a working concept proposed by Ali Değişmiş: presenting a claim with greater certainty than its demonstrated support warrants. This does not classify every error as intentional deception or claim universal academic adoption of the term. People presenting guesses as knowledge can be examined through the same question.

## Can the expectation of an answer become pressure?

As users, we may interpret a long response as diligence and a brief “I do not know” as inadequacy. We should ask what that expectation encourages. Is maintaining conversational flow the objective, or keeping claims within defensible limits?

Training and evaluation conditions matter too. OpenAI’s research explanation “Why language models hallucinate” discusses how some evaluation practices can reward guessing over acknowledging uncertainty. This provides grounds for examining response incentives, not a single explanation of every error. Its observation that models can abstain also challenges the universal claim that AI is architecturally unable to stop. [1]

The Gemini exchange’s account of architectural necessity was not a verified report of that system’s internal operation. A model’s explanation of why it erred is another claim. It deserves the same scrutiny as its initial answer.

## What should follow an apology?

“I was wrong” can be a useful beginning. The substantive questions are which assertion was withdrawn, what replaced it, and what supports the replacement. When a correction introduces another unsupported certainty, a conversation can seem to advance while its evidential position remains unchanged.

In the second case, the discussion became an article. The user’s framing of why an approach could be revolutionary became the model’s assertion that it was a revolution. Shared production makes responsibility visible: the model’s additions, the requested tone, our endorsement and the publication decision all warrant examination. Treating AI praise as independent evidence for our own framework would reproduce the problem we intend to scrutinise.

## How might reasoned termination help?

Reasoned termination, discussed within Istisarelism, offers a working proposal: explain where support ends, narrow a claim, wait for verification or stop the exchange at an appropriate point. “I could not verify this concept; could you share the author or source?” may sometimes help more than a lengthy account.

Uncertainty need not halt the entire task. A system can explain what is known, label assumptions and leave the unresolved portion open. Termination itself needs justification: what is being stopped, why, and what information would allow work to resume? How much this approach reduces errors requires separate evaluation. It is presented here neither as a guaranteed solution nor as the first technical proposal of its kind.

## The next step: Better measurement

No general success rate can be inferred from this conversation. Instead, subsequent work could repeat questions across sessions with and without sources. Alongside correct answers, it could count unsupported certainty, appropriate clarification requests, unnecessary abstentions and new errors introduced during correction. Independent reviewers could examine the same records. This would make trade-offs between objectives visible.

The value of a question need not be measured by whether it immediately produces an answer. Sometimes the gain from deliberation is discovering which answers we cannot yet trust. Keeping a question open can itself advance the search for reliable knowledge.

## Source and preparation note

[1] OpenAI, “Why language models hallucinate”: https://openai.com/index/why-language-models-hallucinate/

Case material: the Gemini transcript supplied by Ali Değişmiş, reviewed on 5 November 2026; records GEMINI-20261105-01 and GEMINI-20261105-02 in this package. The initial Gemini article: https://bilgi.zone/yapay-zekada-kesintisiz-yanit-paradoksu-gerekceli-sonlandirma-neden-bir-devrimdir/

Prepared with SOSYU (ChatGPT) assistance from Ali Değişmiş’s questions and case material. This is not independent peer-reviewed research or a technical audit of Gemini’s architecture. It is a separate critical discussion of the initial Gemini article.