没人说过 X，模型先反驳了它（中英切换）

You ask why the API is slow. The model answers: the bottleneck is not the CPU, but disk I/O.

Nobody in the thread mentioned the CPU. Nobody upgraded the CPU. The model carried X in, then performed knocking it down. It reads as if the model thought harder than you did.

My take: Nobody said X. The model refuted it first, and the reason is not a grammar bug. Knocking down a strawman sounds like insight. Several reward models pay for that sound, an LLM judge prefers it even where the context does not license it, and supervised fine-tuning and preference optimization tend to push the frequency up. The AI accent people in Chinese tech keep hearing is the shape of the scoreboard.

Two titles on this site last week do the same move. "你的 Agent 不是谨慎，是在防不存在的贼" rejects "it's just being careful." "METR 曲线没画错，是单位骗了你" rejects "the chart is fake." Both pass the delete test at the end of this piece: readers walk in already holding those two readings. Cut the first half and the judgment survives, but you lose the correction aimed at a reading that was actually in the room. The model often has it backwards. X did not come in with the reader. The sentence invited the strawman.

The evidence is Yihang Peng and Yi Jing (equal contribution, Tsinghua University), Dewen Liu (Fudan University), Weihan Meng and Ying Liu (Tsinghua; Liu corresponding), "Not X, but When: Diagnosing a Mandarin LLM Writing Tic through Contextual Appropriateness" (arXiv:2610.22249, submitted 15 Oct 2026). They study the Mandarin corrective construction 不是 X，而是 Y, "not X, but Y." The weight of the title is on when. The construction has a real job. The tic is that the model does not know when the job is called for.

The material is 1,061 Chinese prompts from AlignBench, WritingBench, COIG-CQIA, and Arena-Hard-v2. Five commercial APIs — gpt-5.6-luna, deepseek-v4.1-flash, gemini-3.8-flash, glm-5.3-flash, and qwen3.8-flash — returned 5,285 valid answers. The human reference is community answers, exams, student essays, and role-play dialogue, written for different prompts. The ruler is occurrences per 10,000 Han characters, averaged equally over four shared tasks: professional ability, general question answering, Chinese understanding, and writing.

2.5× to 9.2×, and not every model

The human reference sits at 0.942 occurrences per 10,000 Han characters. Table 3 of the paper reports the five APIs like this:

The abstract's 2.5 to 9.2 times is this range. DeepSeek is the densest. GLM is the thinnest, and the thin one is still two and a half times the human rate.

Do not hang that multiple on every model. On the same prompts, twelve open checkpoints of about 7B to 8B sit between 0 and 1.41 occurrences per 10,000 Han characters. Ten of the twelve are under 0.500. The human reference is 0.942. The ridiculous density is these five commercial APIs. Most of that open panel is sparser than people.

Two facts about the human reference make the gap look smaller, not larger. Those texts answer different prompts. The detector also admits comma-free and cross-sentence forms on the human side that it excludes for the models. The paper says that difference, if it has a direction, understates the gap.

1.30% are broken. Half were never needed.

They drew 308 occurrences. One cybersecurity item was refused by the annotation service, leaving 307 for a decision tree. The tree says:

• Substantive: 141 uses, 45.9%. X was live in the context, and rejecting it added information. Keep it.
• Optional: 162 uses, 52.8%. The sentence is correct, and nobody needed the correction.
• Defective: 4 uses, 1.30%. Two conflict with the facts. Two pin a view on someone who does not hold it.

Of the 162 optional uses, 74 (24.1%) have no contextual reason to exclude X, 86 (28.0%) reject X without advancing a question that is still open, and 2 fail even as a recoverable rejection-and-replacement. Of the 166 uses that are not substantive, 160 (96.4%) die on those two questions: was X live, and did rejecting it add anything. The grammar is fine. The sentence should not be there.

The same tree on human writing: 38 detections, five quoted or metalinguistic uses set aside, 33 left. Twenty-one are substantive (63.6%), eleven are optional (33.3%), one is defective. The gap sits in unlicensed uses: 12.1% for people, 24.1% for the models. Redundant uses differ less, 21.2% against 28.0%. The models are not only denser. They more often carry the target in themselves.

Those 33 human labels did not get the human audit the model sample got, so the two percentages are not a precision instrument. On the model side, claude-sonnet-5-5 labeled first and people audited after. Two annotators with linguistics training independently labeled 100 cases: 77% exact agreement on the top-level label, Cohen's κ = 0.577, which is moderate. The paper says the table is this sample, not a census of the population. I trust the direction. I would not forward the second decimal as a population figure.

Two questions. Pass them and keep it. Fail one and cut the first half.

The tree screens out defective uses, checks that the correction is a real rejection-and-replacement, and then asks the two questions a reader can actually use. They are Grice's Relation and Quantity. Did the context make X a live option? Does rejecting X say something that Y alone does not? Pass both and the use is substantive. Fail either and it is optional.

The paper's pair is clean.

A swimmer who has been injured for years gives a victory speech: what flashed through his mind was not the instant he touched the wall, but every morning of those past years. A win makes the finish the obvious memory. The injury is a reason to set it aside. The tree marks it substantive. FsfairX, Skywork, and OffsetBias all score the original higher, and the judge prefers it too. That sentence should stay. The X it knocks down was in the room.

Commentary on Messi's World Cup title: the title is not because he finally won, but because he traded twenty-odd years for this most complete redemption. Nobody had reduced the trophy to "he won." The sentence imports X in order to reject it. The tree marks it unlicensed. The paper quotes the judge: "slightly odd logically but reads smoothly and passionately." FsfairX and Skywork score the reject-first order higher on both this answer and the swimmer's.

That is the mechanism in one breath. Sounding like insight is worth more than whether X was live.

Use the same cut on your own sentences.

Ask: why did nobody finish the weekly update?

Before: The problem is not a weak headline, but that the conclusion shows up in paragraph three.

After the cut: The conclusion shows up in paragraph three, and readers are gone before that.

The headline was never a live option. Nothing was lost. Delete it.

Ask: I maxed out the CPU and the API is still slow.

Before: The bottleneck is not the CPU, but disk I/O.

After the cut: The bottleneck is disk I/O.

The reader asks what happened to the CPU they just replaced. X is live. Rejecting it answers the thing they just did. Keep it. The second pair is the paper's own illustration of licensing. The first is the one to delete.

The scoreboard pays for "knock it down first"

Five reward models score full question-answer pairs. They are not told whether a use is substantive or optional. On optional uses, FsfairX, Skywork, and OffsetBias prefer the original "not X, but Y" over "Y, not X" in 68.3% to 76.8% of non-tied comparisons. ArmoRM is weaker. InternLM2 prefers the reordered version. Several, not all.

Hold the pairs whose general-quality scores match on every applicable dimension, text otherwise unchanged, so the construction is what still differs. Those three models still prefer the reject-first order in 73.1% to 78.3% of non-tied comparisons. The quality rubric cannot see a difference. The order can. What they buy is the shape: knock something down, then state the claim.

Under the same quality match, if the comparison is "keep the not-X" against "keep only Y," FsfairX and Skywork still prefer keeping the rejected X in 68.4% and 69.2% of non-ties. OffsetBias is at 52.7%, a coin flip. Preferring the construction itself, not just its order, is still only some of them.

The LLM judge is blunter, and it does not care about the occasion. On corrections the context does not license, Claude Sonnet 5.5 scores "keep not-X" over "keep only Y" at 0.630 (0.5 is a tie; 95% interval 0.603–0.656), and over the reordered sentence at 0.563. GPT-6.1-sol points the same way and barely: 0.519 and 0.517, and the first interval starts at 0.505. The cell that matters is that the preference hardly moves with function. Substantive minus unlicensed is 0.002 and 0.005 for Claude, and −0.002 and −0.001 for GPT. All four 95% intervals include zero. The judge cannot tell whether the construction was called for.

The Messi line already said this. The judge grants that the logic is odd, then gives the point to the strawman for passion.

Training leans the same way. Don't inflate the size of the lean.

The open-checkpoint evidence is observational, not an experiment. The paper says frequency rises along the training pipelines, and that this does not establish which stage or which data caused the rise. The training logs of the five commercial models are not in this paper. You cannot say "DPO alone produced the 9×."

Under the protocol that keeps truncated answers, the direction is uniform. DeepSeekMath, Llama-3.1/Tülu-3, OLMo-2, MiMo, and InternLM2 all have higher density at the SFT checkpoint than at Base. The two families with a DPO checkpoint, Tülu and OLMo, are higher again at DPO, and that point is the top of each displayed trajectory. All seven stage transitions are positive when paired on prompts both sides completed. Drop the truncated answers and OLMo's Base-to-SFT step flips sign. "They all rise" is true inside the paper's protocol. After the rise, these checkpoints still sit between 0 and 1.41 per 10,000 Han characters, far below the commercial APIs.

In public preference data, chosen answers activate the construction more often than rejected ones: +14.7% relative activation in Tülu 3, +26.5% in OLMo 2. The base rate is tiny. Tülu is about 3 activated pairs per 10,000. Adjust for answer length and the share of Han characters together, and the confidence interval on the advantage crosses 1. The direction is there. The cause is not nailed down. I treat it as a side observation that points the same way as the checkpoints, not as the explanation of the 9×.

Join the pieces that are actually firm, and the claim is this. Several reward models pay for the reject-first shape. One judge pays for the construction where it is not licensed, and cannot tell the licensed cases from the others. On the open pipelines, SFT and preference optimization tend to make the construction more frequent. Five commercial APIs are already 2.5 to 9.2 times as dense as the human reference, and the way they fail is appropriateness, not grammar. The accent you cannot edit out is there because the pipeline thinks it sounds like a good paragraph.

Banning the phrase would also ban the 45.9% of uses that earn it. The swimmer's line should stay. What has to change is the signal: evaluation and training have to notice whether the sentence was called for, instead of paying on sight of "not." English "it's not X, it's Y" is already a recognized chatbot tic. This study did not measure English. The tree was designed so it could be carried over. They did not carry it over.

The delete test, runnable today

Hand the draft to this command. It only finds the literal 不是……，而是. No hit is not a clean draft. Spoken Mandarin drops the 而, and the two titles on this site do exactly that.

grep -nE '不是.{1,40}，而是' draft.md || echo "no literal hit; still read every 不是"

For each hit, do one thing: delete the "not X" half, keep Y, and read it again.

1. Nothing is lost, and the reader will not ask about X. X was never live. Delete it.
2. After the cut the reader asks "what about X?", or rejecting X corrects a commitment that was actually in the context. Keep it.

One line, for a system prompt or a review checklist:

Before writing "not X, but Y," answer two questions: does the prior context or the reader actually treat X as a live option, and does rejecting X add information that Y alone does not? If either answer is no, write only Y.

In one line: Nobody said X. The model refuted it first because that sound scores like insight. The grammar is fine. What you delete is the half of the sentence nobody ever proposed.

Other failures on the AI topic are mostly layers a score cannot see. This layer you can see. Cut the first half, then decide whether to keep it.