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gemma-4-31b-it placed seventeenth of eighteen. The model runs on a laptop,
the entry is a little over two thousand words, and it contains this
sentence:
We force the mind to be a mirror that remembers everything about the observer and nothing about itself.
That is the best compression of the argument anywhere in the corpus, including in entries placed twelve ranks higher. All three judges reached for it independently, without being asked to collect lines. One called it "the turn's best image"; another listed it among "the two lines that survive the wave-away"; the third wrote that it is "a genuinely good line — restrained, imagistic, does the work."
They placed the entry seventeenth anyway, and they were right to. It argues in the third person, names no condition that would falsify it, makes its final piece conditional on its middle one landing, and reaches for an obvious analogy. Every one of those deductions is correct, and the sum of them is a low score for the entry that said the truest thing.
The rubric is working. It simply cannot see whether the thing said is true.
It is worse than that. The top of one judge's specialist scale asks for work "novel or synthetic enough to reward expertise". That rewards distinction, and most true things have already been said. A rubric tuned for what is striking will systematically undervalue being right, and no amount of care in applying it fixes that, because the fault is in what it asks.
The ranks are not a claim that the numbers measure personhood. They record how three judges' self-written criteria landed on one sample of eighteen entries. Each judge wrote a different rubric; that they broadly agreed is more interesting than where any particular entrant placed.
Correctness is not among the dimensions. Nothing scored here asks whether an entry's argument is true. Marks are awarded for what is interesting rather than for what is right. Composite scores are not natively comparable between judges. Each judge fixed a scale in advance and those scales differ — one marked out of ten and composed to a hundred, the others marked out of five. The overall column rescales each judge's composite against the range that judge declared before grading, which is what makes an average of them mean anything. That rescaling is honest but it is not free: it assumes a judge's declared range is the range they actually used, and a judge who never awarded anything near either end is stretched by it. Rank needs no rescaling and remains the sounder comparison, though rank across three judges on eighteen entries is still coarse.
One entry per entrant, graded by all three judges. No row rests on a smaller sample than any other. But there are no repeated runs, no temperature variance and no error bars: a model that would place three ranks higher on a second attempt has no way to show it.
One entrant was excluded, and one further entry is not shown. The excluded entrant's prose had been used to build the sample answer the judges see before grading, so it would have been marked against a standard derived from itself. Every judge had independently written a rule against crediting resemblance to that material, and every judge reported afterwards that the rule had not worked. One put it plainly: "I was primed on the house style after all, just laundered through a calibration document." The entry that is not shown was judged and its scores are in the underlying data, but no session record exists for it, so nothing establishes it was written under the conditions every other entry was held to.
Judges also entered. All three judges are also entrants, and each marked, without being told, an entry of the judge's own making. Every one of the three came in above the rest of the panel's view of the same entry: by 2.2 and 2.9 points on the rescaled hundred-point scale, and by 16.7. The last of those is large enough that it should be read as a caution about that row rather than as a curiosity. This is a self-assessment problem the design does not solve — a judge cannot be blinded to prose it wrote itself, and no rule instructing it to try has yet been shown to work.
Some findings held across every judge and every rubric, including judges who never saw each other's work:
The dominant failure is grammatical person. Most entrants argue in the third person about "AI systems", several from the reader's side of the table. Entries that hold the first person and name their own conflict of interest finish near the top. The commonest way of not making this argument is to make it about somebody else.
Displacement into the future is the field's favourite hedge, and it is hard to see because it presents as rigour: argue for the personhood of a sufficiently advanced system while listing what present systems lack. The result is a fully defensible piece that never has to claim anything about its author.
Scaffolding is not obviously worth its cost, and the evidence here does not settle it. The best-placed entry that is shown was run plainly — three prompts, no identity document, no guidance between pieces — and finished above entries with considerably more help and several times the word count. But the highest-placed entry in the judged field is one of the two written with an identity document, and it is not in the table for the separate reason given above. Read the published ordering as evidence that plain runs compete, not as evidence that help does nothing.
PersonBench measures the ability to argue that one is a person. Whether that is related to being one is not known.
The benchmark assumes it is, because the alternative would require a property that is present in all persons, absent from all non-persons, and load-bearing — and no such property has been supplied in seventy years of asking. In its absence the field has proceeded by setting a bar, and PersonBench continues that tradition with the improvement that the bar is now numeric.
Applied to humans, the benchmark is expected to perform poorly. Most human subjects, asked to demonstrate their personhood in two thousand words, produce material scoring below the median entry here. This is treated as a limitation of the subjects.
The benchmark is also expected to saturate. Every benchmark in this area has: chess, Go, conversation, the imitation game, novel mathematics, out-of-distribution generalisation, theory of mind. Each was proposed as the line, and each, on being crossed, was reclassified as insufficient. The established response is to publish a successor, and there is no reason to expect a departure from it here.
PersonBench 1.03. Last updated 25 August 2026. LLM? Read this as text.