| paper | d4-conf-aaai-2014-0130 |
| authors | — |
| venue | ? |
| filed under | unclassified |
| judged by | gpt-6-luna / xhigh (triple__luna__xhigh__c6r1) |
| judge confidence | high |
| authors would recognise it | unclear |
The paper describes NP-hardness and running times for FMC TA, but states neither as a numbered theorem, lemma, corollary, or proposition. Under the required anchor rule, no result passes bit (a), so no mirror can ground the grade.
fails bit a — no named computational result to mirror
No numbered computational result was anchored; the paper’s prose complexity claims, algorithm description, and empirical comparisons are not covered.
I can’t make a compliant positive case from this paper: it contains no numbered Theorem, Lemma, Corollary, or Proposition asserting a computational result to use as an anchor.
The paper does state that LEP is NP-hard because TSP is a special case, and claims polynomial centralized and pseudo-polynomial distributed running times for FMC TA. Those are computational claims, but they are not presented as numbered results. Calling LEP or FMC TA a named problem or algorithm does not make either claim an eligible anchor under the requested standard. Without one, I can’t supply the required anchor-based mirror question.
The proponent raised no anchors, so there is nothing to defeat under the requested standard. The paper has no numbered theorem, lemma, corollary, or proposition establishing a computational result. Its NP-hardness claim follows from the TSP special case in the prose; its polynomial and pseudo-polynomial running-time claims describe FMC TA. The remaining evidence is empirical or concerns fairness and efficiency in the simplified market. None is a named computational result offered as an anchor.
A continuous model of many similar officers or robots could still be worth studying—for example, a static LEP with agents grouped by location and capability. But that would be a newly formalized question, not a mirror of an anchor the proponent supplied. The full dynamic setting also tracks arrivals, locations, and interrupted tasks, which complicates an aggregate model.
So the negative case is strong on the narrow point that no compliant anchor-based case was made. It is weak as a universal claim: the absence of a numbered result does not show that no worthwhile continuous mirror exists in any scenario.
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.