WORKS / 01 PRIVATE EVALUATION

DIARYBENCH

DiaryBench is a private benchmark for one question: how accurately can a model perform fine-grained emotional analysis over a long sequence of diary entries? It is designed to test not only whether a model can identify meaningful patterns, but whether it can do so without under-interpreting or over-interpreting the evidence. Most LLM benchmarks focus on coding or agentic ability, not on the nuanced interpretation required to track how emotion shifts across time. DiaryBench is meant to fill that gap.

TRACK 01 / SET 50

Model performance board

27 models · higher is better

full board · 50 questions · visible scale 30–70

FAMILY
RIG
SORT
COMPARE
EVIDENCE
TRAPS

These filters show how much context from my diary a model needs to use. Evidence ranges from one passage, to the same period, to several years, and finally to a later retrospective. Traps marks questions where the wrong options sound psychologically insightful—defense, self-protection, and so on—but do not match what I was actually feeling.

    OpenAI Google Anthropic Open source

    Aggregate scores only. GLM-5.3 helped draft the questions; the private answer key never left the author.

    API / web · unlabelled entries use the highest effort tier.

    WHAT THIS MEASURES

    Not positive or negative.
    More exact than that.

    The source stays private; I am the author and the only ground truth. Each question presents several plausible readings of a moment, but the relevant evidence may span one passage, a period, or years of diary entries. The fields below define the test format, the answer key, and the scoring rule.

    1. 01 Long-context reconstruction

      Connect clues across one passage, a period, or years of diary entries.

    2. 02 Fine-grained discrimination

      Separate adjacent, plausible accounts of the same feeling.

    3. 03 Chinese register and performance

      Read internet-native and performative emotional language in context.

    4. 04 Resist over-interpretation

      Do not mistake a depth-sounding mechanism—or the narrator’s own explanation—for what the text supports.

    format 50 multi-select questions about my state of mind at specific moments. Each has 1–3 correct options.
    ground truth I decide what was true. The answer key stays private; web search or training data cannot retrieve it.
    scoring Jaccard overlap |S ∩ C| / |S ∪ C|

    Across 50 questions. S = selected options; C = correct options. Partial credit is built in; extra choices reduce the score.