Leaderboard
All rows: reader Qwen3.6-27B, insertion form D, all chains per film, at most
5 frames per film, 204 films. 110,685 (frame, chain) units in total.
| # | Method | S | 95% CI | Insert rank | S@1 | Top-1 | Beats the still | Journey |
|---|---|---|---|---|---|---|---|---|
| — | Human IMDb still ceiling | 0.7218 | [0.711, 0.733] | 2.67 | 0.735 | 13.4% | 35.1% | 100% |
| 1 | CueKFS j7 (gpt-5.5, budget 96) tuned | 0.5855 | [0.566, 0.605] | 3.49 | 0.588 | 6.5% | 23.6% | 38.2% |
| 2 | CueKFS j7 (gpt-5.5, budget 128) tuned | 0.5795 | [0.560, 0.599] | 3.52 | 0.568 | 6.4% | 23.1% | 35.4% |
| 3 | CueKFS j7 (gpt-5.5, budget 64) tuned | 0.5785 | [0.560, 0.597] | 3.53 | 0.585 | 6.2% | 22.8% | 35.0% |
| 4 | CueKFS j7 (gpt-5.5, published) | 0.5653 | [0.547, 0.584] | 3.61 | 0.572 | 5.7% | 21.6% | 29.0% |
| — | Largest-face heuristic probe | 0.5571 | [0.537, 0.578] | 3.66 | 0.500 | 5.0% | 20.2% | 25.3% |
| — | Random floor | 0.5015 | [0.486, 0.517] | 3.99 | 0.489 | 2.9% | 16.1% | 0% |
| 5 | Uniform sampling | 0.4385 | [0.422, 0.455] | 4.37 | 0.330 | 2.6% | 13.6% | −28.6% |
| 6 | SigLIP2 text retrieval | 0.3620 | [0.341, 0.383] | 4.83 | 0.341 | 1.1% | 8.3% | −63.3% |
| — | Brightness + sharpness probe | 0.2969 | [0.274, 0.320] | 5.21 | 0.266 | 1.3% | 6.5% | −92.9% |
| 7 | WFS-SB (CVPR'26) | 0.2633 | [0.244, 0.283] | 5.42 | 0.248 | 1.0% | 5.4% | −108.1% |
Confidence intervals treat each film's S as one observation (n = 204). ceiling and floor are reference points, not methods. probe marks single-attribute selectors included to show what the score is made of.
Against the random baseline
Paired Wilcoxon signed-rank on the 204 per-film scores:
| Method | ΔS | p | |
|---|---|---|---|
| Human still | +0.220 | 9.0e−34 | above |
| CueKFS j7 (budget 96) | +0.084 | 1.8e−12 | above |
| CueKFS j7 (published) | +0.064 | 5.9e−08 | above |
| Largest-face heuristic | +0.056 | 8.9e−06 | above |
| Uniform sampling | −0.063 | 6.7e−07 | below |
| SigLIP2 text retrieval | −0.139 | 2.3e−19 | below |
| Brightness + sharpness | −0.205 | 2.8e−26 | below |
| WFS-SB | −0.238 | 1.0e−31 | below |
Reading the table
Only one non-trivial method beats random. And its advantage over a plain face detector is not measurable in the published configuration (ΔS = +0.008, p = 0.63) — only the swept configuration separates (p = 0.032).
Three methods score significantly below random. They are not broken; they optimise something else. Uniform maximises temporal coverage, SigLIP2 maximises text-image similarity, WFS-SB detects semantic boundaries and spreads budget across segments. When those objectives are orthogonal to hero-frame quality, "below random" is the informative answer, and the benchmark says it with a sign rather than a shrug.
Nobody covers 40% of the distance. The gap that remains is 0.136 of S, or 0.818 of an insertion position. For scale, sweeping CueKFS's single most important hyperparameter is worth 0.020 — the headroom is not in tuning.
The scale behaves. Random landing at 0.501 against a construction-implied 0.5 means the zero point was not fitted.
What the remaining distance is not
Photographic quality. Across 10,200 selected frames, the correlation between a
frame's score and its brightness, contrast, sharpness, colourfulness, or edge
density is |ρ| ≤ 0.09. A selector optimising exposure and focus
(bright_sharp) scores below random.
Face size is the one attribute that correlates strongly (ρ = 0.485), but it is a threshold rather than a dial, and it has a ceiling: human publicity stills carry a mean max-face fraction of 0.045, one eighth of what the face heuristic selects for, and score 0.165 higher.
Submitting
Open a pull request adding your row, with the result JSON from
scripts/04_evaluate.py. See
examples/submit_your_method.md.
Requirements: the specified reader, the unmodified prompt, a verified frame track, and — if your method calls an LLM internally — the model named in the method name and reported in its published configuration.