| paper | d4-conf-ijcai-2016-0095 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | no |
The paper’s numbered statements are definitions, while Algorithms 1 and 2 have no numbered theorem asserting a runtime, convergence, or other computational guarantee. The reported accuracy gains are empirical, so there is no qualifying named computational result to anchor a continuous-population question.
fails bit a — no named computational result to mirror
No qualifying computational result is covered; the algorithms and empirical comparisons do not provide a numbered computational claim.
The paper has no qualifying named computational result. Its numbered statements are Definition 1 (KAC Problem) and Definition 2 (Transitivity and Symmetry); it gives Algorithms 1 and 2 and reports experimental results, but states no numbered theorem, lemma, corollary, or proposition establishing a complexity or algorithmic result. So there are no anchors to mirror, and no per-anchor continuous problem or A/B/C prediction I can honestly give.
The most plausible positive direction would treat the annotator workforce as a distribution over complete worker types and use type masses in SEEK’s latent-label inference. But that is a proposed extension, not a result of this paper; the support could also grow with the number of distinct annotators. It cannot supply the anchored case you asked for.
The proponent’s diagnosis is the strongest case against a mirror here: the paper states no numbered computational result. Definition 1’s \(\arg\max_f v(f)\) leaves the value function unspecified, so it does not define a concrete complexity problem. Algorithms 1 and 2 describe procedures, but the paper proves no runtime, convergence, or approximation guarantee for them. The reported \(35.88\%\) improvement is an empirical accuracy result on a filtered dataset, not a computational result of the kind ChoCo’s programme is set up to continuize.
The best candidate mirror is to model a large workforce as a distribution over worker types and run SEEK-style inference on the resulting masses. That could be a sensible *new* question. But the type would need to capture a worker’s responses across the task set, and the paper asks for an answer to each particular task. Making the tasks a population instead would discard which item receives which answer. Either route requires choosing a new computational objective and assumptions about the worker or task population; neither mirrors a computational claim this paper makes.
So I find no paper-anchored continuous mirror worth proposing. That conclusion does not establish that a continuous workforce-inference problem could never be worthwhile; it says the paper supplies no computational result for such a mirror to carry forward.
The adversarial triple: the proponent anchors on up to three named results; the opponent sees that case and must defeat every anchor; the judge decides which case convinced it. These are the pipeline’s own outputs, generated by tools/triple_run.py — no human edited them. The paper’s own text is not reproduced here beyond the quoted statement above.