Equation 9 · The Benchmarks That Don't Need the Image
What does this equation mean?
Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
Read it piece by piece
Symbol N
N is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
How to interpret it
Read this expression with the definitions, units, and assumptions supplied by the article.
What the article says around this equation
MMBench introduced an evaluation protocol called CircularEval specifically to close this loophole. A multiple-choice question is not scored from a single pass; it is put to the model N times, once for each of its N answer options, with the options cyclically shifted into a new order on each pass, and the item counts as solved only if the model answers correctly on every single rotation [ 8 ] . The effect of switching from a single ungated pass to this all-or-nothing protocol was substantial: scores dropped roughly ten to twenty-four percentage points across most evaluated models, and for one open model, OpenFlamingo v2, the collapse was dramatic — from 36.7% under a single pass to 2.6% under…
Read the full surrounding passage
MMBench introduced an evaluation protocol called CircularEval specifically to close this loophole. A multiple-choice question is not scored from a single pass; it is put to the model N times, once for each of its N answer options, with the options cyclically shifted into a new order on each pass, and the item counts as solved only if the model answers correctly on every single rotation [ 8 ] . The effect of switching from a single ungated pass to this all-or-nothing protocol was substantial: scores dropped roughly ten to twenty-four percentage points across most evaluated models, and for one open model, OpenFlamingo v2, the collapse was dramatic — from 36.7% under a single pass to 2.6% under CircularEval [ 8 ] . A model that had learned to favor a particular letter or position regardless of content will pass a single-pass evaluation whenever that position happens to hold the right answer, and fail almost every rotation once it does not.
Sources cited in the surrounding passage
These citations give research context. Read each source to check which claims it supports.