| paper | d4-conf-aaai-2011-0100 |
| 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 contains no numbered theorem, lemma, corollary, or proposition asserting a computational result, so bit (a) fails. Its toolkit algorithms and empirical OCR results do not supply a qualifying anchor; the proposed assignment-mass optimization would be a new problem.
fails bit a — no named computational result to mirror
No named computational result qualifies as an anchor; the toolkit design, described algorithms, and empirical OCR evaluation therefore have no surviving mirror coverage.
This paper has no qualifying anchor: it contains no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational complexity result. CSP-1 and WMV are named mechanisms, not numbered results proved here; the paper’s results are toolkit descriptions and empirical OCR evaluations. So the required anchor set is empty, and I can’t make a qualifying mirror case from this paper without treating an experiment or algorithm description as a theorem.
A plausible *follow-on* model would treat workers with the same reliability, productivity, and price as one type, with mass representing their share of a large crowd. Given masses of OCR tasks by difficulty, one could choose assignment mass to minimize expected payment while meeting an accuracy target and deadline. But the paper tracks individual workers and adapts assignments over time; this optimization model would be a new question, not a mirror of a named result in the paper.
There is no computational result here to mirror. The paper names CSP-1 and weighted majority voting, describes a worker-pool algorithm, and reports simulations and MTurk experiments. It states no theorem about the complexity of a defined problem, gives no running-time bound, and establishes no approximation or parameterized result. Its measured accuracy, redundancy, and completion behaviour are empirical findings. That leaves no qualifying anchor: the proposed case for supplies none.
The assignment-mass model is the strongest follow-on candidate. One could group workers by reliability, productivity, and price, group tasks by difficulty, and optimize assignment mass against an accuracy target and deadline. But that would introduce the optimization problem the paper does not formulate or solve. Making the model faithful also requires care: the toolkit tracks workers’ histories, handles task-specific answers and error patterns, and responds to arrivals over time. Coarse worker and task averages can lose the dependencies that determine whether a vote is correct. A richer model could preserve them, but it would still be a new stochastic planning problem, not a continuous version of a result in this paper.
I would not argue that no large-market scenario could make such a model sensible. Workers can plausibly fall into repeated types, and a carefully built aggregate model might be worthwhile on its own. The case against a mirror is stronger and narrower: this paper provides no named computational result for that mirror to illuminate. So the universal claim that no related continuous research could be worthwhile goes beyond what the paper supports; the paper-specific case against a worthwhile *mirror* is decisive.
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.